The Evolving Guide on How the Internet is Changing Research,
Collaboration and Scholarly Publishing
Sönke Bartling & Sascha Friesike
Opening Science
Sönke Bartling • Sascha Friesike
Editors
Opening Science
The Evolving Guide on How the Internet
is Changing Research, Collaboration
and Scholarly Publishing
123
Editors
Sönke Bartling
German Cancer Research Center
Heidelberg
Germany
and
Institute for Clinical Radiology
and Nuclear Medicine
Mannheim University Medical Center
Heidelberg University
Mannheim
Germany
Sascha Friesike
Alexander von Humboldt Institute
for Internet and Society
Berlin
Germany
ISBN 978-3-319-00025-1 ISBN 978-3-319-00026-8 (eBook)
DOI 10.1007/978-3-319-00026-8
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013953226
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Preface
Initially the Internet was designed for research purposes—so was the World Wide
Web. Yet, society deviated from this intended use and as such many aspects of our
daily lives have changed drastically over the past 20 years. The Internet has
changed our ways of communicating, watching movies, interacting, shopping, and
travelling. Many tools offered by the Internet have become second nature to us.
At first, the net was designed as a plain data transfer network for researchers, yet it
has since morphed into a vivid, transforming, living network. The evolution of the
Internet came with barely foreseeable cultural changes, affecting core elements of
our society, such as collaboration, government, participation, intellectual property,
content, and information as a whole.
Novel online research tools pop up constantly and they are slowly but surely
finding their way into research culture. A culture that grew after the first scientific
revolution some 300 years ago and that has brought humanity quite far is on the
verge of its second profound metamorphosis. It is likely that the way that
researchers publish, assesses impact, communicate, and collaborate will change
more within the next 20 years than it did in the past 200 years.
This book will give researchers, scientists, decision makers, politicians, and
stakeholders an overview on the basics, the tools, and the vision behind the current
changes we see in the field of knowledge creation. It is meant as a starting point for
readers to become an active part in the future of research and to become an
informed party during the transition phase. This is pivotal, since research, as a
sensitive, complex process with many facets and millions of participants, hierar-
chies, personal networks, and structures, needs informed participants.
Many words are used to describe the future of research: ‘Science 2.0’, ‘Cyber-
science 2.0’, ‘Open Research’, ‘Open Science’, ‘Digital Humanities’, ‘eScience’,
‘Mode 2’, etc.…They may trigger feelings of buzzwordism, yet at the same time the
struggle for precise definitions highlights the current uncertainty regarding these and
shows the many possible outcomes the current changes in research might bring.
It seems contradictory in itself to publish a ‘traditional’ book on this topic—
why don’t we simply go online? The book is and will be an important medium in
research, just as papers and abstracts, and most importantly human interactions,
will continue to be. However, all will be supplemented by novel tools, and
accordingly so is this book. You can find, download, and even edit the entire book
online at www.openingscience.org. It is published under the Creative Commons
v
license, and everyone is invited to contribute to it and adopt and reuse its content.
The book was created using a collaborative authoring tool, which saved us many
meetings and tedious synchronizations of texts among authors. We made this book
a living example of the communication culture research can have—not only in the
future—but already today.
We thank all authors; their contributions and invested efforts are highly appre-
ciated. The authors participated in the review process of the book. Besides our
authors, many thanks go to our discussion partners and reviewers of our work, and to
those who have not (yet) contributed a particular text, who are Annalies Gartz,
Ayca-Nina Zuch, Joeseph Hennawi, Prof. Fabian Kiessling, Christine Kiefer,
Thomas Rodt, Kersten Peldschus, Daniel Schimpfoessl, Simon Curt Harlinghausen,
Prof. Wolfhard Semmler, Clemens Kaiser, Michael Grasruck, Carin Knoop, Martin
Nissen, Jan Kuntz, Alexander Johannes Edmonds, Aljona Bondarenko, Prof. Marc
Kachelrieß, Radko Krissak, Johannes Budjan, Prof. Henrik Michaely, Thomas
Henzler, Prof. Christian Fink, Prof. Stefan O. Schönberg, Tillmann Bartling, Rajiv
Gupta, and many others …
Heidelberg Sönke Bartling
Berlin Sascha Friesike
vi Preface
Contents
Part I Basics/Background
Towards Another Scientific Revolution. . . . . . . . . . . . . . . . . . . . . . . . 3
Sönke Bartling and Sascha Friesike
Open Science: One Term, Five Schools of Thought . . . . . . . . . . . . . . 17
Benedikt Fecher and Sascha Friesike
Excellence by Nonsense: The Competition for Publications
in Modern Science. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Mathias Binswanger
Science Caught Flat-Footed: How Academia Struggles with Open
Science Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Alexander Gerber
Open Science and the Three Cultures: Expanding Open Science
to all Domains of Knowledge Creation . . . . . . . . . . . . . . . . . . . . . . . . 81
Michelle Sidler
Part II Tools
(Micro)Blogging Science? Notes on Potentials and Constraints
of New Forms of Scholarly Communication . . . . . . . . . . . . . . . . . . . . 89
Cornelius Puschmann
Academia Goes Facebook? The Potential of Social Network
Sites in the Scholarly Realm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Michael Nentwich and René König
Reference Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Martin Fenner, Kaja Scheliga and Sönke Bartling
vii
Open Access: A State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Dagmar Sitek and Roland Bertelmann
Novel Scholarly Journal Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Peter Binfield
The Public Knowledge Project: Open Source Tools for Open
Access to Scholarly Communication . . . . . . . . . . . . . . . . . . . . . . . . . . 165
James MacGregor, Kevin Stranack and John Willinsky
Part III Vision
Altmetrics and Other Novel Measures for Scientific Impact . . . . . . . . 179
Martin Fenner
Dynamic Publication Formats and Collaborative Authoring . . . . . . . . 191
Lambert Heller, Ronald The and Sönke Bartling
Open Research Data: From Vision to Practice . . . . . . . . . . . . . . . . . . 213
Heinz Pampel and Sünje Dallmeier-Tiessen
Intellectual Property and Computational Science . . . . . . . . . . . . . . . . 225
Victoria Stodden
Research Funding in Open Science . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Jörg Eisfeld-Reschke, Ulrich Herb and Karsten Wenzlaff
Open Innovation and Crowdsourcing in the Sciences . . . . . . . . . . . . . 255
Thomas Schildhauer and Hilger Voss
The Social Factor of Open Science . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Tobias Fries
Part IV Cases, Recipes and How-Tos
Creative Commons Licences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
Sascha Friesike
Organizing Collaboration on Scientific Publications:
From Email Lists to Cloud Services . . . . . . . . . . . . . . . . . . . . . . . . . . 289
Sönke Bartling
viii Contents
Unique Identifiers for Researchers . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Martin Fenner and Laure Haak
Challenges of Open Data in Medical Research . . . . . . . . . . . . . . . . . . 297
Ralf Floca
On the Sociology of Science 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
Vladimir B. Teif
How This Book was Created Using Collaborative Authoring
and Cloud Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Sönke Bartling
History II.O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
Luka Orešković
Making Data Citeable: DataCite . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
Jan Brase
About the Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
Contents ix
Part I
Basics/Background
Towards Another Scientific Revolution
Sönke Bartling and Sascha Friesike
But even within those limits, the openness I am advocating
would be a giant cultural shift in how science is done, a second
Open Science revolution extending and completing the first
Open Science revolution, of the 17th and 18th centuries.
—Michael Nielsen
Abstract In this introductory chapter we establish a common understanding of
what are and what drives current changes in research and science. The concepts of
Science 2.0 and Open Science will be introduced. As such we provide a short
introduction to the history of science and knowledge dissemination. We explain
the origins of our scientific culture which evolved around publication methods.
Interdependencies of current concepts will be elucidated and it will be stated that
the transition towards Open Science is a complex cultural change. Reasons as to
why the change is slow are discussed and the main obstacles are identified. Next,
we explain the recent changes in scientific workflows and how these cause changes
in the system as a whole. Furthermore, we provide an overview on the entire book
and explain what can be found in each chapter.
Nicole Forster’s goal as a researcher is to enhance cancer treatment. That is why
she and her colleagues in the laboratory of Leif W. Ellisen at Massachusetts
General Hospital Cancer Center in Boston, Massachusetts, study tumors on indi-
vidual levels and search for cancer causes. In March 2012 Forster was trying to
isolate ribonucleic acid (RNA)—the genetic blueprint for proteins within the
cell—within mouse cells. To prepare the cells for her experiment she mixed them
with a special gel that provided them with all the nutrients to grow and proliferate,
even outside the body, for a short period of time. Yet in the following step, she had
to get rid of the gel to get to the information she needed: the RNA. And therein lay
S. Bartling (&)
German Cancer Research Center, Heidelberg, Germany
e-mail: soenkebartling@gmx.de
S. Bartling
Institute for Clinical Radiology and Nuclear Medicine, Mannheim University
Medical Center, Heidelberg University, Mannheim, Germany
S. Friesike
Alexander von Humboldt Institute for Internet and Society, Berlin, Germany
e-mail: friesike@hiig.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_1,  The Author(s) 2014
3
her problem. She had never done that specific isolation before and hence did not
know how to do it. Her colleagues did not know, either. No one in my lab or even
on my floor of the Cancer Center was doing such experiments, said Forster.
She was stuck. Then Forster thought of turning to the community of ResearchGate.
ResearchGate is a social network (Boyd & Ellison 2007) for scientists to exchange
ideas, publications, and to discuss research. Forster had first signed up to
ResearchGate in 2009. She had heard about the network at a conference in Boston
and was intrigued: I thought that sharing research experience and discussing
topics that you always wanted to discuss with someone would be a great oppor-
tunity. I like that it is a professional network where you can help other people and
be helped. Since then she had answered multiple questions from fellow
ResearchGate members and now it was her turn to ask the community for help.
Within 24 h Forster had a solution. Two researchers replied to her post and sug-
gested different methods. She tried one and it worked. You don’t have to search for
the best approach via Google or go through all of these publications, Forster says.
A social network for scientists helped Forster to solve a problem that she had
bugged colleagues about for several weeks within a single day. Forster’s case is far
from uncommon. Researchers all over the world use modern communication tools
such as social networks, blogs, or Wikipedia to enhance their scientific expertise,
meet experts, and discuss ideas with people that face similar challenges. They do
not abandon classical means of scientific communication such as publications or
conferences, but rather they complement them. Today we can see that these novel
communication methods are becoming more and more established in the lives of
researchers; we argue that they may become a significant part of the future of
research. We undertook this book in order to highlight the different developments
that are currently arising in the world of knowledge creation. We do not know
whether all of these developments will prevail, yet we are certain that institutional
knowledge creation will change drastically over the next decade. Naturally, any-
one involved in research does well to inform themselves about these develop-
ments. There is no perfect way by which research will be carried out in the future.
Every researcher has to decide for themselves which technologies and methods
they will include in their work. This, however,—as anything in research—starts
with informing oneself about what is already out there; it is our goal to provide that
information with this book.
Knowledge Creation and Dissemination: A Brief History
In an early draft-version of this book, the present section was called ‘A Brief
History of Science’. Yet, we ran into several problems with this heading. Firstly,
there is a singularity in the English language that differentiates between knowledge
creation that is concerned with the rules of the natural world (science) and
knowledge creation that is concerned with the human condition (humanities).
Throughout the preparation of this book we constantly ran into this dilemma and
4 S. Bartling and S. Friesike
we would like to take the opportunity to tell you that whenever we talk about
science we mean any organized form of knowledge creation (see chapter Open
Science and the Three Cultures: Expanding Open Science to all Domains of
Knowledge Creation). Secondly, science is often understood as the product created
by a scientists. And a scientists is understood as someone with a full-time job at a
university or a research institute. Yet, new forms of collaboration reach far beyond
our institutional understanding of doing research, which brings us to certain
dissent.
As such we labeled the section ‘Knowledge Creation and Dissemination’.
Knowledge creation and its dissemination are two sides of the same coin—
knowledge does not impact on society if it is unable to disseminate (Merton 1993).
Throughout history we can see that breakthroughs in knowledge creation went
hand in hand with breakthroughs in its dissemination. In turn, dissemination is not
only bound to technological changes but also societal changes such as freedom of
speech or the Renaissance. In large, the present book is a compendium that pre-
sents current changes that we see in knowledge creation and dissemination.
Actually, many chapters of this book challenge our traditional understanding of
how scientific knowledge should be disseminated. Moreover, as of today,
researchers’ views on how knowledge creation is changing differ drastically in
many aspects. And it is likely that our understanding differs from your under-
standing. As such, all we want to offer in this book is a comprehensive overview
on what is changing in the world of knowledge creation, which foundations are
being laid today, and what might become essential in the future.
The history of human knowledge is closely linked to the history of civiliza-
tion—one could even argue that the history of civilization is in large parts based on
knowledge creation and its dissemination. In prehistoric times, knowledge was
passed from one generation to the next one orally or by showing certain tech-
niques. This mainly applied to basic everyday tasks such as hunting, fire making,
manufacturing clothes, or gathering nutritious foods. The creation of this knowl-
edge was not yet structured and it was not recorded, except for occasional
drawings like cave paintings. The drastic change in knowledge creation was the
invention of a writing system. Roughly at the same time, agriculture came to life.
These two inventions combined laid the groundwork for what we today consider
civilization. Civilization allowed for the division of labor and hence individuals
began to specialize—knowledge creation accelerated. The researcher as a pro-
fession concerned with the creation of knowledge made his debut in ancient
Greece. Scientists like Plato, Aristotle, Pythagoras, Socrates, or Archimedes wrote
their observations down, taught others, and created knowledge that is still relevant
roughly 2,500 years later. Disciplines as we know them today formed many
centuries later and as such ancient scientists were usually philosophers, mathe-
maticians, and physicists in one. Similar developments were noticeable in other
societies as well. In China for instance thinkers like Confucius, Laozi, or Sun Tzu
were concerned with question similar to those raised in ancient Greece.
During the following centuries, religion played a major role in the development
of knowledge creation. Beliefs about certain essential questions such as how was
Towards Another Scientific Revolution 5
the earth created? where do diseases come from? or what happens after death?
impeded scientific advances in many fields and as such slowed down overall
knowledge creation. Not very surprisingly, the middle Ages are often considered
to be a dark age, in which rational thinking was prohibited. With the invention of
the printing press and the beginning of the Renaissance in the 17th century,
research slowly emancipated itself from religion. Slowly meaning that it took the
church until 1992 to rehabilitate Galileo for his outrageous claim that the sun
might be the center of our universe.
During the Renaissance, considerable amounts of knowledge were created by a
few polymaths—more or less a small group of outstanding thinkers involved in all
kinds of questions ranging from biology, to art, to engineering—hence the label
‘Renaissance man’. Da Vinci, for instance, developed machines related to today’s
helicopters, analyzed water, clouds, and rain, painted some of the most important
paintings of mankind, and did considerable research on anatomy. Goethe wrote,
did research in botany, and was in dispute with Newton over questions concerning
optics and color.
What we consider modern science came to life in the 17th century when
knowledge creation was both, professionalized and institutionalized. The number
of scientists started to skyrocket—from a few polymath during the renaissance to
over a million scientists in 1850. This growth did not slow down over the fol-
lowing 150 years and today we can globally count roughly 100 million people
involved in science. More and more disciplines formed and scientists became
professional specialists in tiny fields rather than experts in general knowledge.
Professionalization of Knowledge Creation:
The First Scientific Revolution
The professionalization of knowledge creation is often called the first scientific
revolution. Indeed it is this revolution that laid the groundwork for many principles
that guide scientific work today. Academic disciplines as we today know them
formed during the first scientific revolution, as did our publishing system. The
professionalization of knowledge creation required means of assessing the value of
a contribution, so that incentives for successful research could be provided.
Lacking a sufficient system for these incentives, 17th century researchers were
secretive in their discoveries. Without a scientific publication system they claimed
inventorship by sending out anagrams to fellow researchers that did not make
sense without knowledge of the discovery.1 This method prevented other scientists
from claiming inventorship, and was still a form of publishing. When the
knowledge in question began to spread and the anagrams could be made sense of,
future research funding was hopefully already secured. Today, this sounds
1 http://www.mathpages.com/home/kmath151/kmath151.htm
6 S. Bartling and S. Friesike
downright preposterous, as we all agree upon the notion that research is always
based upon other research and as such that research results should be available to
those interested in them.
It was the development of a journal publication system that drastically changed
publishing in research and gave appropriate credits to researchers. The first journal
purely dedicated to science was Philosophical Transactions which has been
published ever since [e.g. one of the first scientific articles (Hook 1665)]. Pub-
lishing scientific journal articles became a pivotal building block of modern sci-
ence. Researchers developed a common understanding that it is in the common
interest for research results to be openly available to all other researchers (David
2004). This understanding of the necessity for openness in science … led to the
modern journal system, a system that is perhaps the most open system for the
transmission of knowledge that could be built with seventeenth-century media
(Nielsen 2011). Based on this core concept of publishing, myriads of partially
institutionalized, partially commercialized structures grew. These structures
developed constitute the cultural, political, and fundamental background in which
academic knowledge creation works till today. The entire system is inherently
based upon journals printed on paper. Almost every scientific publication we see
today is created as if it is meant to be printed. Articles come in very predefined
forms and are usually downloaded as printout-like PDFs. There is no fundamental
reason to stick to this principle—other than our scientific heritage.
Currently, we can see a transition in knowledge dissemination set off by the
Internet that enables scientists to publish in forms unimaginable only a few years
ago. In all kinds of disciplines these new methods pop up, be it in the humanities
under the term ‘digital humanities’, from a Web 2.0 angle under the term ‘Science
2.0’, or from those fighting for free knowledge under the term ‘open research’ and
‘Open Science’. The Internet offers new answers to many challenges which the
first scientific revolution overcame hundreds of years ago. And it is the task of
today’s researchers to assess and evaluate those newly created options, to bridge
the legacy gap, and to lay a path towards the second scientific revolution.
Legacy Gap: The Background of the Second
Scientific Revolution
The journal system developed at a time when written or printed letters and a few
books were the only means of transferring knowledge. Before printing and dis-
seminating a piece of knowledge, it had to be in a complete and correct form,
otherwise it was not worth paying for the costly publication process (Fig. 1).
Publishers derived control over scientific content by controlling the printing and
dissemination of scientific results. Accordingly, the assessment of scientific impact
developed around the journal system.
However, paper is no longer the only media of choice. Publishing costs
diminished and from a technical viewpoint preliminary results or idea snippets
Towards Another Scientific Revolution 7
could be published, edited, and commented on. Yet, research as a whole is affected
by the culture it has developed; it is affected by a the journal system created when
results simply had to be printed on paper. We are currently in a ‘‘legacy gap’’
(Fig. 1) and everything points to the fact that we are on the brink of a new
scientific revolution. Yet, how this revolution actually will be played out remains
one of the most interesting questions in modern science.
The Second Scientific Revolution
Picture a situation in which scientists would be able to publish all their thoughts,
results, conclusions, data, and such as they occur, openly and widely available to
everybody. The Internet already provides tools that could make this possible
(microblogs, blogs, wikis, etc.). Moreover, picture a scientific culture in which
researchers could be in the situation of doing so with the assurance that they will
be credited appropriately. Imagine the potential for interactions between
researchers. Knowledge could flow quickly, regardless of institutions and personal
networks. Research results could be published as they occur. There would be no
need to wait until results are complete enough to support a full paper. Similarly, if
Fig. 1 The first scientific revolution happened when the publishing of scientific papers became
the prevailing means of disseminating scientific knowledge. Our scientific culture developed
around this. Today the Internet provides novel means of publishing and we are in the ‘legacy gap’
between the availability of these tools and their profound integration into the scientific culture
(second scientific revolution)
8 S. Bartling and S. Friesike
projects were to be stopped, negative or small findings could be published in blog
posts or other low threshold publications. These findings could therefore still
contribute to the scientific knowledge process. Today, negative results are often
dismissed and thus the entire knowledge created in such a research project is not
available to others. Someone else might start a similar project running into the
same problem that stopped the first project simply because the first project never
published an explanation of its failure (Fig. 2).
The advantages of such a scientific culture are multifaceted. We would see
faster knowledge exchange, prevention of unnecessarily repeated experiments, and
a more vivid discussion (Fig. 3). However, in order to use these novel publication
formats, they must be appropriately credited by other scientists and—maybe more
importantly—by granting authorities, which is not yet the case.
Naming the New: Science 2.0, Open Science, eScience,
Mode2, Open Research
Terms like Science 2.0, Open Science, Digital Humanities, eScience, Mode2, or
Open Research are all umbrella terms that formed over the past few years and that
emphasize various aspects of the second scientific revolution.
Fig. 2 Today, research projects are conducted until results justify a full-blown paper. In the future,
scientists might openly share ideas, preliminary results, and negative results at much earlier stages
of their research using the novel publication methods that became available with the Internet
Towards Another Scientific Revolution 9
All of these umbrella terms struggle to find a clear definition and people often
use them interchangeably when talking about current changes in scientific pursuits.
We sought after defining each and every one of these terms in order to establish a
coherent picture of how the change in knowledge creation is seen from different
angles. Yet, what each of the terms means and how exactly it differs from the
others is often unclear. If you ask five people how Mode 2 and Science 2.0 are
associated you can be certain to get five different and possibly contradictory
answers. All terms are somewhat born of the necessity that a term for the present
changes was needed. Knowledge creation is a wide field and thus several terms
emerged, whereof we would like to define only two—mainly in order to use them
in the discussions contained within this book.
• Science 2.0 refers to all scientific culture, incl. scientific communication, which
employs features enabled by Web 2.0 and the Internet (in contrast to Science 1.0
which represents a scientific culture that does not take advantage of the
Internet).
• Open Science refers to a scientific culture that is characterized by its openness.
Scientists share results almost immediately and with a very wide audience.
Fig. 3 The research culture of the future possibly supports an open and wide communication
beyond institutes and personal networks by providing novel, credited means of disseminating
knowledge between researchers. Negative as well as positive findings will contribute to other
research projects much sooner after the findings occur
10 S. Bartling and S. Friesike
Strictly speaking, since the first scientific revolution, science has been open
(Fig. 4). Through the Internet and Web 2.0 science can become ‘more Open
Science’, meaning that researchers share results, ideas, and data much earlier
and much more extensively to the public than they do at the moment.
Science 2.0 enables Open Science, but Science 2.0 does not necessarily have to
happen in an Open Science fashion, since scientists can still employ features of the
Internet, but stay very much put in terms of publishing their results. This might be
due to cultural and legal restrictions.
The Second Scientific Revolution:
Road to a Great New Future?
Many stakeholders serve the current scientific culture. They brought research, and
with it society, quite far. Yet now, we have to face the challenges that come with
all the novel developments and with the second scientific revolution. History
shows that knowledge creation has always adopted new opportunities. In turn, it
certainly will do so this time, too. Yet the question remains as to who will be the
drivers and the stakeholders of tomorrow. In the best case, the biggest benefactor
will be the scientific knowledge generating process—and with it research itself.
Many researchers show considerable concern in respect to the novel concepts of
the second scientific revolution. From these concerns vivid discussions should
arise and useful conclusions should be found that steer the second scientific rev-
olution in the right direction. This is especially true since significant input should
come from within the active research community itself.
Fig. 4 Since the first scientific revolution, science and knowledge creation was open—as open as
the methods of the seventeenth century allowed it to be. The Internet has brought about novel
methods, thus allowing science to be more open
Towards Another Scientific Revolution 11
Another question is whether future openness and onlineness will set optimal
incentives for the creation of knowledge. Many wrong paths could be picked and
may result in dead-ends. It is important that stakeholders are flexible and honest
enough to be able to leave dead-end streets.
Some voices discuss the current transition of research as a revolutionizing
process that might overcome current shortcomings in scientific conduct. Short-
comings are among many others: questionable proof generating means (such as
wrongly applied statistics (Ioannidis 2005; Sterne 2001), intolerance against
uncommon theses and approaches, citation-based ‘truth generation’, and inflexible
cultures of scientific approaches within disciplines. Furthermore, publication-bias
through rejection of negative results or rejection of non-confirming studies (Turner
et al. 2008; Begley & Ellis 2012) and questionable incentives that are set by the
current methods to assess scientific quality (see chapter Excellence by Nonsense:
The Competition for Publications in Modern Science) are also factors. The tran-
sition towards the second scientific revolution can help to solve these problems,
but it does not necessarily have to. It can be a way to make science more open,
liberal, and fair, but it can also result in the opposite.
To conclude, much will depend upon whether researchers become the leading
force within this transition, or whether they play a passive role driven by other
stakeholders of the research process. In order to prevent the latter, researchers
should be deeply involved in this process and they should be aware of the potential
consequences. This book is meant to support scientists in becoming a constructing
factor in the designing process of the second scientific revolution.
The Second Scientific Revolution is Based on Many Novel
Aspects and Tools
Despite their separation, the key aspects of the second scientific revolution are
interconnected (Fig. 5). Open Access (see chapter Open Access: A State of the Art),
for instance, needs new forms of copyright concepts (see Creative Commons
Licences). Reference managers (see Reference Management) are a great addition to
social networks for scientists (see chapter Academia Goes Facebook? The Potential
of Social Network Sites in the Scholarly Realm). Assessing the scientific impact of
novel publications such as blog posts (see (Micro)Blogging Science? Notes on
Potentials and Constraints of New Forms of Scholarly Communication) needs novel
impact measurement factors—altmetrics (see chapter Altmetrics and Other Novel
Measures for Scientific Impact), which might be based on unambiguous researcher
IDs (see chapter Unique Identifiers for Researchers). Altmetrics, at the same time,
can be integrated into social networks. There is no single most important factor: it is
more a multitude of facets that jointly change how research works.
12 S. Bartling and S. Friesike
How This Book Works: Artificially Dissecting the Second
Scientific Revolution
This book brings together the enabling concepts that shape the current discussion
on our changing research environment. We divided the book into three parts in
order to make its content easily accessible.
• The first part of the book is called Basics; here we cover topics that highlight the
overall shift in scientific thinking. It begins with the chapter ‘‘Open Science:
One Term, Five Schools of Thought’’ in which Benedikt Fecher and editor
Sascha Friesike explain the many meanings which have been given to the term
Open Science. This is followed by Mathias Binswanger’s ‘‘Excellence by
Nonsense: The Competition for Publications in Modern Science’’ in which he
highlights some of the downsides in today publication driven scientific envi-
ronments. Alexander Gerber’s article titled ‘‘Science Caught Flat-footed: How
Academia Struggles with Open Science Communication’’ follows; here the
author explains why social media are adopted quite slowly by the research
community, especially in Europe. The last article in the section was written by
Michelle Sidler and is entitled ‘‘Open Science and the Three Cultures:
Fig. 5 It is important to understand that many tools of the second scientific revolution will only
make sense if others are also implemented. For example, alternative impact measurement systems
such as altmetrics only make sense if researchers can be uniquely identified—either with a
Unique Researcher ID or within a social network
Towards Another Scientific Revolution 13
Expanding Open Science to All Domains of Knowledge Creation’’; in it the
author highlights a core weakness that the terms Open Science and Science 2.0
share: the fact that all of the implied concepts are valid for researchers outside
the sciences as well, yet the name might scare them away.
• The second part of the book is called Tools and deals with implementations that
already work today. Cornelius Puschmann starts the section with his piece on
blogging and microblogging among researches called ‘‘(Micro) blogging
Science? Notes on Potentials and Constraints of New Forms of Scholarly
Communication’’. He is followed by Michael Nentwich and René König’s article
‘‘Academia Goes Facebook? The Potential of Social Network Sites in the
Scholarly Realm’’. ‘‘Reference Management’’ by Martin Fenner, Kaja Scheliga,
and editor Sönke Bartling is the next chapter. It is succeeded by ‘‘Open Access:
A State of the Art’’ by Dagmar Sitek and Roland Bertelmann, and James
MacGregor, Kevin Stranack, and John Willinsky’s ‘‘The Public Knowledge
Project: Open Source Tools for Open Access to Scholarly Communication’’.
• The third part named Vision takes a more long term view on the issue and thus
explains how single aspects of research might develop over the next decade or
two. The section begins with an article by Martin Fenner named ‘‘Altmetrics
and Other Novel Measures for Scientific Impact’’ and an article by Lambert
Heller, Ronald The, and Sönke Bartling called ‘‘Dynamic Publication Formats
and Collaborative Authoring’’. It follows ‘‘Open Research Data: From Vision to
Practice’’ by Heinz Pampel and Sünje Dallmeier-Tiessen, and ‘‘Intellectual
Property and Computational Science’’ by Victoria Stodden. The next chapter is
called ‘‘Research Funding in Science 2.0’’ and was written by Jörg Eisfeld-
Reschke, Ulrich Herb, and Karsten Wenzlaff. The last chapter of the book was
written by ThomasSchildhauer and Hilger Voss and is entitled ‘‘Open
Innovation and Crowdsourcing in the Sciences’’.
• The book closes with a collection of cases which highlight in a rather brief
manner some aspects of the Open Science movement. Here the authors focus on
specific aspects and projects, give advice, or present their experience
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
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research. Nature, 483(7391), 531–533. doi:10.1038/483531a.
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: definition, history, and scholarship.
J. Comput.-Mediated Commun., 13(1), 210–230. doi:10.1111/j.1083-6101.2007.00393.x.
David, P. A. (2004). Understanding the emergence of ‘‘Open Science’’ institutions: functionalist
economics in historical context. Ind. Corporate Change, 13(4), 571–589. doi:10.1093/icc/
dth023.
14 S. Bartling and S. Friesike
Hook, M. (1665). Some observations lately made at London concerning the planet Jupiter.
Philosophical transactions of the royal society of London, 1(1–22), pp. 245–247. doi:10.1098/
rstl.1665.0103.
Ioannidis, J.P.A. (2005). Why most published research findings are false. PLoS Medicine, 2(8),
p. e124. doi:10.1371/journal.pmed.0020124.
Merton, R.K. (1993). On the shoulders of giants: a Shandean postscript Post-Italianate ed.,
Chicago: University of Chicago Press.
Nielsen, M. (2011). Reinventing discovery: the new era of networked science. New Jersey:
Princeton University Press.
Sterne, J. A. C. (2001). Sifting the evidence—what’s wrong with significance tests? Another
comment on the role of statistical methods. BMJ, 322(7280), 226–231. doi:10.1136/
bmj.322.7280.226.
Turner, E. H., Matthews, A. M., Linardatos, E., & Tell, R. A. (2008). Selective publication of
antidepressant trials and its influence on apparent efficacy. New England J. Med., 358(3),
252–260. doi:10.1056/NEJMsa065779.
Towards Another Scientific Revolution 15
Open Science: One Term, Five Schools
of Thought
Benedikt Fecher and Sascha Friesike
Abstract Open Science is an umbrella term encompassing a multitude of
assumptions about the future of knowledge creation and dissemination. Based on a
literature review, this chapter aims at structuring the overall discourse by pro-
posing five Open Science schools of thought: The infrastructure school (which is
concerned with the technological architecture), the public school (which is con-
cerned with the accessibility of knowledge creation), the measurement school
(which is concerned with alternative impact measurement), the democratic school
(which is concerned with access to knowledge) and the pragmatic school (which is
concerned with collaborative research).
There is scarcely a scientist who has not stumbled upon the term ‘Open Science’ of
late and there is hardly a scientific conference where the word and its meaning are
not discussed in some form or other. ‘Open Science’ is one of the buzzwords of the
scientific community. Moreover, it is accompanied by a vivid discourse that
apparently encompasses any kind of change in relation to the future of scientific
knowledge creation and dissemination; a discourse whose lowest common
denominator is perhaps that science in the near future somehow needs to open up
more. In fact, the very same term evokes quite different understandings and opens
a multitude of battlefields, ranging from the democratic right to access publicly
funded knowledge (e.g. Open Access to publications) or the demand for a better
bridging of the divide between research and society (e.g. citizen science) to the
development of freely available tools for collaboration (e.g. social media platforms
B. Fecher
German Institute for Economic Research, Mohrenstraße 58, Berlin 10117, Germany
S. Friesike (&)
Alexander von Humboldt Institute for Internet and Society, Berlin, Germany
e-mail: friesike@hiig.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_2,  The Author(s) 2014
17
for scientists). From this vantage point, openness could refer to pretty much
anything: The process of knowledge creation, its result, the researching individual
him- or herself, or the relationship between research and the rest of society.
The diversity, and perhaps ambiguity, of the discourse is, however, under-
standable considering the diversity of stakeholders that are directly affected by a
changing scientific environment. These are in the first place: Researchers from all
fields, policy makers, platform programmers and operators, publishers, and the
interested public. It appears that each peer group discussing the term has a different
understanding of the meaning and application of Open Science. As such the whole
discourse can come across as somewhat confusing. By structuring the Open Sci-
ence discourse on the basis of existing literature, we would like to offer an
overview of the multiple directions of development of this still young discourse, its
main arguments, and common catchphrases. Furthermore, we intend to indicate
issues that in our eyes still require closer attention.
Looking at the relevant literature on Open Science, one can in fact recognize
iterative motives and patterns of argumentation that, in our opinion, form more or
less distinct streams. Referring to the diversity of these streams, we allowed
ourselves to call them schools of thought. After dutifully combing through the
literature on Open Science, we identified five distinct schools of thought. We do
not claim a consistently clear-cut distinction between these schools (in fact some
share certain ontological principles). We do, however, believe that our compilation
can give a comprehensible overview of the predominant thought patterns in the
current Open Science discourse and point towards new directions in research
regarding Open Science. In terms of a literature review, we furthermore hope that
this chapter identifies some of the leading scholars and thinkers within the five
schools.
The following table (Table 1) comprises the five identified schools together
with their central assumptions, the involved stakeholder groups, their aims, and the
tools and methods used to achieve and promote these aims.
It must be noted that our review is not solely built upon traditional scholarly
publications but, due to the nature of the topic, also includes scientific blogs and
newspaper articles. It is our aim in this chapter to present a concise picture of the
ongoing discussion rather than a complete list of peer-reviewed articles on the
topic. In the following, we will describe the five schools in more detail and provide
references to relevant literature for each.
18 B. Fecher and S. Friesike
Public School: The Obligation to Make Science Accessible
to the Public
In a nutshell, advocates of the public school argue that science needs to be
accessible for a wider audience. The basic assumption herein is that the social web
and Web 2.0 technologies allow scientists, on the one hand, to open up the research
process and, on the other, to prepare the product of their research for interested
non-experts (see Table 2).
Accordingly, we recognize two different streams within the public school: The
first is concerned with the accessibility of the research process (the production), the
second with the comprehensibility of the research result (the product). Both
streams involve the relationship between scientists and the public and define
openness as a form of devotion to a wider audience. In the following section we
will elaborate more on both streams in reference to relevant literature.
Accessibility to the Research Process:
Can Anyone be a Scientist?
To view the issue as a formerly hidden research process becoming transparent and
accessible to the common man seems a decidedly romantic image of doing science.
Open Science: One Term, Five Schools of Thought 19
T
ab
le
1
Fi
ve
O
pe
n
Sc
ie
nc
e
sc
ho
ol
s
of
th
ou
gh
t
Sc
ho
ol
of
th
ou
gh
t
C
en
tr
al
as
su
m
pt
io
n
In
vo
lv
ed
gr
ou
ps
C
en
tr
al
A
im
T
oo
ls
&
M
et
ho
ds
D
em
oc
ra
ti
c
T
he
ac
ce
ss
to
kn
ow
le
dg
e
is
un
eq
ua
ll
y
di
st
ri
bu
te
d.
Sc
ie
nt
is
ts
,
po
li
ti
ci
an
s,
ci
ti
ze
ns
M
ak
in
g
kn
ow
le
dg
e
fr
ee
ly
av
ai
la
bl
e
fo
r
ev
er
yo
ne
.
O
pe
n
A
cc
es
s,
in
te
ll
ec
tu
al
pr
op
er
ty
ri
gh
ts
,
O
pe
n
da
ta
,
O
pe
n
co
de
Pr
ag
m
at
ic
K
no
w
le
dg
e-
cr
ea
ti
on
co
ul
d
be
m
or
e
ef
fic
ie
nt
if
sc
ie
nt
is
ts
w
or
ke
d
to
ge
th
er
.
Sc
ie
nt
is
ts
O
pe
ni
ng
up
th
e
pr
oc
es
s
of
kn
ow
le
dg
e
cr
ea
ti
on
.
W
is
do
m
of
th
e
cr
ow
ds
,
ne
tw
or
k
ef
fe
ct
s,
O
pe
n
D
at
a,
O
pe
n
C
od
e
In
fr
as
tr
uc
tu
re
E
ffi
ci
en
t
re
se
ar
ch
de
pe
nd
s
on
th
e
av
ai
la
bl
e
to
ol
s
an
d
ap
pl
ic
at
io
ns
.
Sc
ie
nt
is
ts
&
pl
at
fo
rm
pr
ov
id
er
s
C
re
at
in
g
op
en
ly
av
ai
la
bl
e
pl
at
fo
rm
s,
to
ol
s
an
d
se
rv
ic
es
fo
r
sc
ie
nt
is
ts
.
C
ol
la
bo
ra
ti
on
pl
at
fo
rm
s
an
d
to
ol
s
Pu
bl
ic
Sc
ie
nc
e
ne
ed
s
to
be
m
ad
e
ac
ce
ss
ib
le
to
th
e
pu
bl
ic
.
Sc
ie
nt
is
ts
&
ci
ti
ze
ns
M
ak
in
g
sc
ie
nc
e
ac
ce
ss
ib
le
fo
r
ci
ti
ze
ns
.
C
it
iz
en
Sc
ie
nc
e,
Sc
ie
nc
e
PR
,
Sc
ie
nc
e
B
lo
gg
in
g
M
ea
su
re
m
en
t
Sc
ie
nt
ifi
c
co
nt
ri
bu
ti
on
s
to
da
y
ne
ed
al
te
rn
at
iv
e
im
pa
ct
m
ea
su
re
m
en
ts
.
Sc
ie
nt
is
ts
&
po
li
ti
ci
an
s
D
ev
el
op
in
g
an
al
te
rn
at
iv
e
m
et
ri
c
sy
st
em
fo
r
sc
ie
nt
ifi
c
im
pa
ct
.
A
lt
m
et
ri
cs
,
pe
er
re
vi
ew
,
ci
ta
ti
on
,
im
pa
ct
fa
ct
or
s
20 B. Fecher and S. Friesike
T
ab
le
2
Pu
bl
ic
Sc
ho
ol
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
Pu
sc
hm
an
n
(2
01
2)
B
oo
k
ch
ap
te
r
(M
ic
ro
)b
lo
gg
in
g
Sc
ie
nc
e?
N
ot
es
on
Po
te
nt
ia
ls
an
d
C
on
st
ra
in
ts
of
N
ew
Fo
rm
s
of
Sc
ho
la
rl
y
C
om
m
un
ic
at
io
n
Sc
ie
nt
is
ts
to
da
y
ne
ed
to
m
ak
e
th
ei
r
re
se
ar
ch
ac
ce
ss
ib
le
to
a
w
id
er
au
di
en
ce
by
us
in
g
(m
ic
ro
)b
lo
gs
.
‘‘S
ci
en
ti
st
s
m
us
t
be
ab
le
to
ex
pl
ai
n
w
ha
t
th
ey
do
to
a
br
oa
de
r
pu
bl
ic
to
ga
rn
er
po
li
ti
ca
l
su
pp
or
t
an
d
fu
nd
in
g
fo
r
en
de
av
or
s
w
ho
se
ou
tc
om
es
ar
e
un
cl
ea
r
at
be
st
an
d
da
ng
er
ou
s
at
w
or
st
,a
di
ffi
cu
lt
y
th
at
is
m
ag
ni
fie
d
by
th
e
co
m
pl
ex
it
y
of
sc
ie
nt
ifi
c
is
su
es
.’’
(P
.
X
X
)
C
ri
bb
&
Sa
ri
(2
01
0)
M
on
og
ra
ph
O
pe
n
Sc
ie
nc
e—
Sh
ar
in
g
K
no
w
le
dg
e
in
th
e
di
gi
ta
l
ag
e
T
he
ac
ce
ss
ib
il
it
y
of
sc
ie
nt
ifi
c
kn
ow
le
dg
e
is
a
m
at
te
r
of
it
s
pr
es
en
ta
ti
on
.
‘‘S
ci
en
ce
is
by
na
tu
re
co
m
pl
ic
at
ed
,
m
ak
in
g
it
al
l
th
e
m
or
e
im
po
rt
an
t
th
at
go
od
sc
ie
nc
e
w
ri
ti
ng
sh
ou
ld
be
si
m
pl
e,
cl
ea
n
an
d
cl
ea
r.
’’
(p
.
15
)
G
ra
nd
et
al
.
(2
01
2)
Jo
ur
na
l
A
rt
ic
le
O
pe
n
Sc
ie
nc
e:
A
N
ew
‘‘T
ru
st
T
ec
hn
ol
og
y’
’?
Sc
ie
nt
is
ts
ca
n
ra
is
e
pu
bl
ic
tr
us
t
by
us
in
g
W
eb
2.
0
to
ol
s.
‘‘A
s
m
ai
ns
tr
ea
m
sc
ie
nc
e—
an
d
co
m
m
en
t
on
sc
ie
nc
e—
fo
ll
ow
s
th
e
pi
on
ee
rs
in
to
th
e
re
al
m
of
W
eb
2.
0,
to
be
ab
le
to
na
vi
ga
te
th
e
cu
rr
en
ts
of
th
e
in
fo
rm
at
io
n
flo
w
in
th
is
re
la
ti
ve
ly
un
m
ap
pe
d
te
rr
it
or
y,
sc
ie
nt
is
ts
an
d
m
em
be
rs
of
th
e
pu
bl
ic
w
il
l
al
l
ne
ed
re
li
ab
le
an
d
ro
bu
st
to
ol
s.
’’
(p
.
68
5)
M
or
ri
s
&
M
ie
tc
he
n
(2
01
0)
Pr
oc
ee
di
ng
s
C
ol
la
bo
ra
ti
ve
St
ru
ct
ur
in
g
of
K
no
w
le
dg
e
by
E
xp
er
ts
an
d
th
e
Pu
bl
ic
U
si
ng
W
eb
2.
0
to
ol
s
to
m
ak
e
kn
ow
le
dg
e
pr
od
uc
ti
on
ac
ce
ss
ib
le
fo
r
th
e
pu
bl
ic
.
‘‘(
...
)
th
er
e
is
st
il
l
pl
en
ty
of
op
po
rt
un
it
ie
s
fo
r
re
in
ve
nt
in
g
an
d
ex
pe
ri
m
en
ti
ng
w
it
h
ne
w
w
ay
s
to
re
nd
er
an
d
co
ll
ab
or
at
e
on
kn
ow
le
dg
e
pr
od
uc
ti
on
an
d
to
se
e
if
w
e
ca
n
bu
il
d
a
m
or
e
st
ab
le
,
su
st
ai
na
bl
e
an
d
co
ll
eg
ia
l
at
m
os
ph
er
e
(.
..)
fo
r
ex
pe
rt
s
an
d
th
e
pu
bl
ic
to
w
or
k
to
ge
th
er
.’’
(p
.
32
)
T
ac
ke
(2
01
2)
B
lo
g
en
tr
y
R
au
s
au
s
de
m
E
lf
en
be
in
tu
rm
:
O
pe
n
Sc
ie
nc
e
T
he
W
eb
2.
0
gi
ve
s
sc
ie
nt
is
ts
ne
w
op
po
rt
un
it
ie
s
to
sp
re
ad
sc
ie
nt
ifi
c
kn
ow
le
dg
e
to
a
w
id
er
pu
bl
ic
.
‘‘I
m
ei
nf
ac
hs
te
n
Fa
ll
kö
nn
en
W
is
se
ns
ch
af
tl
er
et
w
a
in
B
lo
gs
üb
er
T
he
m
en
au
s
ih
re
m
Fa
ch
ge
bi
et
be
ri
ch
te
n
un
d
Fr
ag
en
vo
n
in
te
re
ss
ie
rt
en
da
zu
be
an
tw
or
te
n.
’’
(p
.
2)
(c
on
ti
nu
ed
)
Open Science: One Term, Five Schools of Thought 21
T
ab
le
2
(c
on
ti
nu
ed
)
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
Ir
w
in
(2
00
6)
M
on
og
ra
ph
T
he
po
li
ti
cs
of
ta
lk
D
ue
to
m
od
er
n
te
ch
no
lo
gy
,
ci
ti
ze
ns
ca
n
pa
rt
ic
ip
at
e
in
sc
ie
nt
ifi
c
kn
ow
le
dg
e
cr
ea
ti
on
.
‘‘(
...
)
th
is
bo
ok
is
co
m
m
it
te
d
bo
th
to
an
im
pr
ov
ed
un
de
rs
ta
nd
in
g
of
‘s
ci
en
ce
,
te
ch
no
lo
gy
an
d
ci
ti
ze
ns
hi
p’
an
d
to
be
tt
er
so
ci
al
pr
ac
ti
ce
in
th
is
ar
ea
(.
..)
’’
(p
.
8)
H
an
d
(2
01
0)
A
rt
ic
le
C
it
iz
en
sc
ie
nc
e:
Pe
op
le
po
w
er
C
it
iz
en
s
po
ss
es
s
va
lu
ab
le
kn
ow
le
dg
e
fr
om
w
hi
ch
sc
ie
nc
e
ca
n
be
ne
fit
.
‘‘B
y
ha
rn
es
si
ng
hu
m
an
br
ai
ns
fo
r
pr
ob
le
m
so
lv
in
g,
Fo
ld
it
ta
ke
s
B
O
IN
C
’s
di
st
ri
bu
te
d-
co
m
pu
ti
ng
co
nc
ep
t
to
a
w
ho
le
ne
w
le
ve
l.’
’
(p
.
2)
E
bn
er
&
M
au
re
r
(2
00
9)
A
rt
ic
le
C
an
m
ic
ro
bl
og
s
an
d
w
eb
lo
gs
ch
an
ge
tr
ad
it
io
na
l
sc
ie
nt
ifi
c
w
ri
ti
ng
?
B
lo
gs
ca
n
co
nt
ri
bu
te
to
m
ak
e
re
se
ar
ch
m
or
e
ac
ce
ss
ib
le
to
th
e
pu
bl
ic
.
Y
et
th
ey
ca
nn
ot
re
pl
ac
e
ar
ti
cl
es
an
d
es
sa
ys
in
sc
ho
la
rl
y
co
m
m
un
ic
at
io
n.
‘‘W
eb
lo
gs
an
d
m
ic
ro
bl
og
s
ca
n
en
ha
nc
e
le
ct
ur
es
by
br
in
gi
ng
th
e
re
so
ur
ce
s
of
th
e
W
or
ld
W
id
eW
eb
to
th
e
co
ur
se
an
d
m
ak
in
g
th
em
di
sc
us
sa
bl
e.
B
ot
h
ne
w
te
ch
no
lo
gi
es
,
ho
w
ev
er
,
ca
nn
ot
re
pl
ac
e
w
ri
ti
ng
es
sa
ys
an
d
ar
ti
cl
es
,b
ec
au
se
of
th
ei
r
di
ff
er
en
t
na
tu
re
.’’
C
at
li
n-
G
ro
ve
s
(2
01
2)
R
ev
ie
w
ar
ti
cl
e
T
he
C
it
iz
en
Sc
ie
nc
e
L
an
ds
ca
pe
:
Fr
om
V
ol
un
te
er
s
to
C
it
iz
en
Se
ns
or
s
an
d
B
ey
on
d
C
it
iz
en
s
ca
n
he
lp
m
on
it
or
in
g
on
a
la
rg
e
sc
al
e.
‘‘T
he
ar
ea
s
in
w
hi
ch
it
[c
it
iz
en
sc
ie
nc
e]
ha
s,
an
d
m
os
t
pr
ob
ab
ly
w
il
l
co
nt
in
ue
to
ha
ve
,
th
e
gr
ea
te
st
im
pa
ct
an
d
po
te
nt
ia
l
ar
e
th
at
of
m
on
it
or
in
g
ec
ol
og
y
or
bi
od
iv
er
si
ty
at
la
rg
e
ge
og
ra
ph
ic
sc
al
es
.’’
Po
w
el
l
&
C
ol
in
(2
00
9)
A
rt
ic
le
Pa
rt
ic
ip
at
or
y
pa
ra
do
xe
s:
Fa
ci
li
ta
ti
ng
ci
ti
ze
n
en
ga
ge
m
en
t
in
sc
ie
nc
e
an
d
te
ch
no
lo
gy
fr
om
th
e
T
op
-D
ow
n?
C
it
iz
en
sc
ie
nc
e
pr
oj
ec
ts
ar
e
of
te
n
sh
or
t-
li
ve
d.
‘‘M
os
t
pa
rt
ic
ip
at
or
y
ex
er
ci
se
s
do
no
t
en
ga
ge
ci
ti
ze
ns
be
yo
nd
an
ev
en
to
r
a
fe
w
w
ee
ks
/m
on
th
s,
an
d
th
ey
do
no
tb
ui
ld
ci
ti
ze
ns
’
pa
rt
ic
ip
at
or
y
sk
il
ls
in
w
ay
s
th
at
w
ou
ld
he
lp
th
em
en
ga
ge
w
it
h
sc
ie
nt
is
ts
or
po
li
cy
m
ak
er
s
in
de
pe
nd
en
tl
y.
’’
(p
.
32
7)
22 B. Fecher and S. Friesike
Yet, coming from the assumptions that communication technology not only allows
the constant documentation of research, but also the inclusion of dispersed external
individuals (as supposed in the pragmatic school), an obvious inference is that the
formerly excluded public can now play a more active role in research. A pervasive
catchphrase in this relationship is the concept of so-called citizen science which,
put simply, describes the participation of non-scientists and amateurs in research.
Admittedly, the term, as well as the idea, have already existed for a long time. In
1978, well before the digital age, the biochemist Erwin Chargaff already used this
term to espouse a form of science that is dominated by dedicated amateurs. The
meaning of the term has not changed; it merely experiences a new magnitude in
the light of modern communication technology.
Hand (2010) refers, for instance, to Rosetta@Home, a distributed-computing
project in which volunteer users provide their computing power (while it is not in
use) to virtually fold proteins. The necessary software for this also allowed users to
watch how their computer tugged and twisted the protein in search of a suitable
configuration (ibid., p.2). By observing this, numerous users came up with sug-
gestions to speed up the otherwise slow process. Reacting to the unexpected user
involvement, the research team applied a new interface to the program that allowed
users to assist in the folding in form of an online game called Foldit. Hand states: ‘‘By
harnessing human brains for problem solving, Foldit takes BOINC’s distributed-
computing concept to a whole new level’’ (ibid., p. 2). In this specific case, the
inclusion of citizens leads to a faster research process on a large public scale. Citizen
science is in this regard a promising tool to ‘harness’ a volunteer workforce.
However, one can arguably question the actual quality of the influence of amateurs
upon the analytical part of the research research. Catlin-Groves (2012) takes the
same line as the Rosetta@Home project. She expects citizen science’s greatest
potential in the monitoring of ecology or biodiversity at a large scale (ibid., p. 2). The
specific fields possibly issue from the author’s area of research (Natural Sciences)
and the journal in which the review article was published (International Journal of
Zoology). Nonetheless, in respect to the two fields, it becomes apparent that citizens
can rather be considered a mass volunteer workforce instead of actual scientists.
Indeed, most citizen science projects follow a top-down logic in which pro-
fessional scientists give impetuses, take on leading roles in the process and
analysis, and use amateurs not as partners, but rather as a free workforce. Irwin
(2006) even claims that most citizen science projects are not likely to provide
amateurs with the skills and capacities to significantly affect research in mean-
ingful ways. Powell and Colin (2009) also criticize the lack of a meaningful impact
for non-experts in the research: ‘‘Most participatory exercises do not engage
citizens beyond an event or a few weeks/months, and they do not build citizens’
participatory skills in ways that would help them engage with scientists or policy
makers independently’’ (ibid., p. 327).
The authors further present their own citizen science project, the Nanoscale
Science and Engineering Center (NSEC), which at first also started as a onetime
event. After the project was finished, however, the University engaged a citizen
Open Science: One Term, Five Schools of Thought 23
scientist group which is in frequent dialogue with field experts. The authors do not
lay out in detail how citizens can actually influence research policies, rather
present a perspective for a bottom-up relationship between interested amateurs and
professionals. There is still a lack of research when it comes to models of active
involvement of citizens in the research process beyond feeder services. Future
research could therefore focus on new areas of citizen participation (e.g. citizen
science in ‘soft sciences’) or alternative organizational models for citizen science
(e.g. how much top-down organization is necessary?).
Another, also yet to explored, aspect that can be associated with citizen science
is the crowdfunding of science. Crowdfunding is a financing principle that is
already well established in the creative industries. Via online platforms, single
Internet users can contribute money to project proposals of their choice and, if the
project receives enough funding, enable their realization. Contributions are often
rewarded with non-monetary benefits for the benefactors. A similar model is
conceivable for science: The public finances research proposals directly through
monetary contributions and in return receives a benefit of some description (for
instance: access to the results). Crowdfunding of science allows direct public
influence on the very outskirts of the research (a kind of civic scientific agenda
setting) yet hardly at all during the process. Nonetheless, it possibly constitutes a
new decisive force in the pursuit of research interests besides the ‘‘classica’’ of
institutional and private funding. There is still, at least to the authors’ knowledge,
no research regarding this topic. Future research could for instance cover factors of
success for project pitches or the actual potential of crowdfunding for science.
Comprehensibility of the Research Result:
Making Science Understandable
The second stream of the public school refers to the comprehensibility of science
for a wider audience, that is mainly science communication. Whereas, for instance,
citizen science concerns the public influence on the research, this sub-stream
concerns the scientists’ obligation to make research understandable for a wider
audience—a demand that Tacke (2012), in an entry on his blog, provocatively
entitled ‘‘Come out of the ivory tower!’’.
In this regard, Cribb and Sari demand a change in the scientific writing style:
‘‘Science is by nature complicated, making it all the more important that good
science writing should be simple, clean and clear’’ (2010, p. 15). The authors’
credo is that as the scientific audience becomes broader and the topics more
specific, the academic dissemination of knowledge needs to adapt.
On a perhaps more applied level, numerous authors suggest specific tools for
science communication. Weller and Puschmann (2011), for instance, describe the
microblogging service Twitter as a suitable tool to direct users to, for example,
relevant literature and as a source for alternative impact factors (as expressed in
24 B. Fecher and S. Friesike
the measurement school). In this volume (see chapter Micro(blogging) Science?
Notes on Potentials and Constraints of New Forms of Scholarly Communication),
Puschmann furthermore dwells on the role of the scientist today and his need to
communicate: ‘‘Scientists must be able to explain what they do to a broader public
to garner political support and funding for endeavors whose outcomes are unclear
at best and dangerous at worst, a difficulty that is magnified by the complexity of
scientific issues’’. As adequate tools for the new form of scholarly public justifi-
cation, the author refers to scientific blogging or Twitter during conferences. In the
same line of reasoning, Grand et al. (2012) argues that by using Web 2.0 tools and
committing to public interaction, a researcher can become a public figure and
honest broker of his or her information (ibid., p. 684).
While numerous researchers already focus on the new tools and formats of
science communication and the audience’s expectations, there is still a need for
research on the changing role of a researcher in a digital society, that is,
for instance, the dealings with a new form of public pressure to justify the need for
instant communication and the ability to format one’s research for the public.
A tenable question is thus also if a researcher can actually meet the challenge to,
on the one hand, carry out research on highly complex issues and, on the other,
prepare these in easily digestible bits of information. Or is there rather an emerging
market for brokers and mediators of academic knowledge? Besides, what are the
dangers of preparing research results in easily digestible formats?
Democratic School: Making Research Products Available
The democratic school is concerned with the concept of access to knowledge.
Unlike the public school, which promotes accessibility in terms of participation to
research and its comprehensibility, advocates of the democratic school focus on
the principal access to the products of research. This mostly relates to research
publications and scientific data, but also to source materials, digital representations
of pictorial and graphical materials, or multimedia material (as Sitek and
Bertelmann describe it their chapter).
Put simply, they argue that any research product should be freely available. The
reason we refer to the discourse about free access to research products as
the democratic school issues from its inherent rationale that everyone should have
the equal right to access knowledge, especially when it is state-funded.
In the following, we will focus on two central streams of the democratic school,
namely Open Access to research publications and Open Data. We assume that both
represent a wider set of arguments that accompanies discussion on free access to
research products.
Open Science: One Term, Five Schools of Thought 25
Open Data
Regarding Open Data in science, Murray-Rust (2008, p. 52) relates the meaning of
the prefix ‘open’ to the common definition of open source software. In that
understanding, the right of usage of scientific data does not demise to an academic
journal but remains in the scientific community: ‘‘I felt strongly that data of this
sort should by right belong to the community and not to the publisher and started
to draw attention to the problem’’ (ibid., p. 54). According to Murray-Rust, it is
obstructive that journals claim copyright for supporting information (often data) of
an article and thereby prevent the potential reuse of the data. He argues that ‘‘(it) is
important to realize that SI is almost always completely produced by the original
authors and, in many cases, is a direct output from a computer. The reviewers may
use the data for assessing the validity of the science in the publication but I know
of no cases where an editor has required the editing of (supporting information)’’
(ibid., p. 53). The author endorses that text, data or meta-data can be re-used for
whatever purpose without further explicit permission from a journal (see Table 3).
He assumes that, other than validating research, journals have no use for claiming
possession over supporting information—other researchers, however, do.
According to Murray-Rust’s understanding, data should not be ‘free’ (as in free
beer), but open for re-use in studies foreseen or unforeseen by the original creator.
The rationale behind Open Data in science is in this case researcher-centric; it is a
conjuncture that fosters meaningful data mining and aggregation of data from
multiple papers. Put more simply, Open Data allows research synergies and pre-
vents duplication in the collection of data. In this regard, Murray-Rust does not
only criticize the current journal system and the withholding of supporting
information but also intimates at the productive potential of Open Data. It has to be
said, though, that the synergy potentials that Murray-Rust describes mostly apply
to natural sciences (or at least research fields in which data is more or less stan-
dardized) or at least fields in which an intermediate research product (e.g. data) can
be of productive use for others.
Similar to Murray-Rust, Molloy (2011) criticises the current journal system
which, according to the author, works against the maximum dissemination of
scientific data that underlies publications. She elaborates on the barriers inherent in
the current journal system thus: ‘‘Barriers include inability to access data,
restrictions on usage applied by publishers or data providers, and publication of
data that is difficult to reuse, for example, because it is poorly annotated or
‘hidden’ in unmodifiable tables like PDF documents’’ (ibid., p. 1). She suggests a
dealing with data that follows the Open Knowledge Foundation’s definition of
openness, meaning that the data in question should be available as a whole, at no
more than a reasonable reproduction cost (preferably through download), and in a
convenient and modifiable form.
Other than Murray-Rust (2008) and Molloy (2011), Vision (2010), and Boulton
et al. (2011) firstly hold the researchers liable for practicing Open Data. Vision
refers to a study by Campbell et al. (2002), in which it is shown that only one
26 B. Fecher and S. Friesike
T
ab
le
3
D
em
oc
ra
ti
c
Sc
ho
ol
—
O
pe
n
da
ta
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
M
ur
ra
y-
R
us
t
(2
00
8)
Pr
ec
ee
di
ng
s
O
pe
n
da
ta
in
sc
ie
nc
e
O
pe
n
da
ta
de
pe
nd
s
on
a
ch
an
ge
of
th
e
jo
ur
na
l
pr
ac
ti
ce
re
ga
rd
in
g
th
e
w
it
hh
ol
di
ng
of
su
pp
or
ti
ng
in
fo
rm
at
io
n.
‘‘T
he
ge
ne
ra
l
re
al
iz
at
io
n
of
th
e
va
lu
e
of
re
us
e
w
il
l
cr
ea
te
st
ro
ng
pr
es
su
re
fo
r
m
or
e
an
d
be
tt
er
da
ta
.
If
pu
bl
is
he
rs
do
no
t
gl
ad
ly
ac
ce
pt
th
is
ch
al
le
ng
e,
th
en
sc
ie
nt
is
ts
w
il
l
ra
pi
dl
y
fin
d
ot
he
r
w
ay
s
of
pu
bl
is
hi
ng
da
ta
,
pr
ob
ab
ly
th
ro
ug
h
in
st
it
ut
io
na
l,
de
pa
rt
m
en
ta
l,
na
ti
on
al
or
in
te
rn
at
io
na
l
su
bj
ec
t
re
po
si
to
ri
es
.
In
an
y
ca
se
th
e
co
m
m
un
it
y
w
il
l
ra
pi
dl
y
m
ov
e
to
O
pe
n
D
at
a
an
d
pu
bl
is
he
rs
re
si
st
in
g
th
is
w
il
l
be
se
en
as
a
pr
ob
le
m
to
be
ci
rc
um
ve
nt
ed
.’’
(p
.
64
)
V
is
io
n
(2
01
0)
Jo
ur
na
l
A
rt
ic
le
O
pe
n
D
at
a
an
d
th
e
So
ci
al
C
on
tr
ac
to
f
Sc
ie
nt
ifi
c
P
ub
li
sh
in
g
D
at
a
is
a
co
m
m
od
it
y.
T
he
sh
ar
in
g
of
da
ta
en
ab
le
s
be
ne
fit
s
ot
he
r
re
se
ar
ch
er
s.
‘‘D
at
a
ar
e
a
cl
as
si
c
ex
am
pl
e
of
a
pu
bl
ic
go
od
,
in
th
at
sh
ar
ed
da
ta
do
no
t
di
m
in
is
h
in
va
lu
e.
T
o
th
e
co
nt
ra
ry
,
sh
ar
ed
da
ta
ca
n
se
rv
e
as
a
be
nc
hm
ar
k
th
at
al
lo
w
s
ot
he
rs
to
st
ud
y
an
d
re
fin
e
m
et
ho
ds
of
an
al
ys
is
,
an
d
on
ce
co
ll
ec
te
d,
th
ey
ca
n
be
cr
ea
ti
ve
ly
re
pu
rp
os
ed
by
m
an
y
ha
nd
s
an
d
in
m
an
y
w
ay
s,
in
de
fin
it
el
y.
’’
(p
.
33
0)
B
ou
lt
on
et
al
.
(2
01
1)
C
om
m
en
t
Sc
ie
nc
e
as
a
pu
bl
ic
en
te
rp
ri
se
:
th
e
ca
se
fo
r
op
en
da
ta
D
at
a
ne
ed
s
to
be
pr
ep
ar
ed
in
a
us
ab
le
fo
rm
at
.
‘‘C
on
ve
nt
io
na
lp
ee
r-
re
vi
ew
ed
pu
bl
ic
at
io
ns
ge
ne
ra
ll
y
pr
ov
id
e
su
m
m
ar
ie
s
of
th
e
av
ai
la
bl
e
da
ta
,
bu
t
no
t
ef
fe
ct
iv
e
ac
ce
ss
to
da
ta
in
a
us
ab
le
fo
rm
at
.’’
(p
.
16
34
)
M
ol
lo
y
(2
01
1)
O
pe
n
A
cc
es
s
A
rt
ic
le
T
he
op
en
kn
ow
le
dg
e
fo
un
da
ti
on
:
O
pe
n
da
ta
m
ea
ns
be
tt
er
sc
ie
nc
e
D
at
a
sh
ou
ld
be
fr
ee
to
re
us
e
an
d
re
di
st
ri
bu
te
w
it
ho
ut
re
st
ri
ct
io
ns
.
‘‘T
he
de
fin
it
io
n
of
‘‘
op
en
’’
,c
ry
st
al
li
se
d
in
th
e
O
K
D
,m
ea
ns
th
e
fr
ee
do
m
to
us
e,
re
us
e,
an
d
re
di
st
ri
bu
te
w
it
ho
ut
re
st
ri
ct
io
ns
be
yo
nd
a
re
qu
ir
em
en
t
fo
r
at
tr
ib
ut
io
n
an
d
sh
ar
e-
al
ik
e.
A
ny
fu
rt
he
r
re
st
ri
ct
io
ns
m
ak
e
an
it
em
cl
os
ed
kn
ow
le
dg
e.
’’
(p
.
1)
A
ue
r
et
al
.
(2
00
7)
D
B
pe
di
a:
A
nu
cl
eu
s
fo
r
a
w
eb
of
op
en
da
ta
th
e
se
m
an
ti
c
w
eb
O
pe
n
D
at
a
is
a
m
aj
or
ch
al
le
ng
e
fo
r
co
m
pu
te
r
sc
ie
nt
is
ts
in
fu
tu
re
.
‘‘I
ti
s
no
w
al
m
os
tu
ni
ve
rs
al
ly
ac
kn
ow
le
dg
ed
th
at
st
it
ch
in
g
to
ge
th
er
th
e
w
or
ld
’s
st
ru
ct
ur
ed
in
fo
rm
at
io
n
an
d
kn
ow
le
dg
e
to
an
sw
er
se
m
an
ti
ca
ll
y
ri
ch
qu
er
ie
s
is
on
e
of
th
e
ke
y
ch
al
le
ng
es
of
co
m
pu
te
r
sc
ie
nc
e,
an
d
on
e
th
at
is
li
ke
ly
to
ha
ve
tr
em
en
do
us
im
pa
ct
on
th
e
w
or
ld
as
a
w
ho
le
.’’
(p
.
1)
(c
on
ti
nu
ed
)
Open Science: One Term, Five Schools of Thought 27
T
ab
le
3
(c
on
ti
nu
ed
)
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
L
öh
&
H
in
ze
(2
00
6)
O
pe
n
D
at
a
ty
pe
s
an
d
op
en
fu
nc
ti
on
s
T
he
pr
ob
le
m
of
su
pp
or
ti
ng
th
e
m
od
ul
ar
ex
te
ns
ib
il
it
y
of
bo
th
da
ta
an
d
fu
nc
ti
on
s
in
on
e
pr
og
ra
m
m
in
g
la
ng
ua
ge
(k
no
w
n
as
ex
pr
es
si
on
pr
ob
le
m
)
‘‘T
he
in
te
nd
ed
se
m
an
ti
cs
is
as
fo
ll
ow
s:
th
e
pr
og
ra
m
sh
ou
ld
be
ha
ve
as
if
th
e
da
ta
ty
pe
s
an
d
fu
nc
ti
on
s
w
er
e
cl
os
ed
,
de
fin
ed
in
on
e
pl
ac
e.
’’
(p
.
1)
M
il
le
r
et
al
.
(2
00
8)
O
pe
n
D
at
a
C
om
m
on
s,
A
L
ic
en
ce
fo
r
O
pe
n
D
at
a
Pr
ac
ti
ci
ng
op
en
da
ta
is
a
qu
es
ti
on
of
ap
pr
op
ri
at
e
li
ce
nc
in
g
of
da
ta
.
‘‘I
ns
te
ad
,
li
ce
ns
es
ar
e
re
qu
ir
ed
th
at
m
ak
e
ex
pl
ic
it
th
e
te
rm
s
un
de
r
w
hi
ch
da
ta
ca
n
be
us
ed
.
B
y
ex
pl
ic
it
ly
gr
an
ti
ng
pe
rm
is
si
on
s,
th
e
gr
an
to
r
re
as
su
re
s
th
os
e
w
ho
m
ay
w
is
h
to
us
e
th
ei
r
da
ta
,
an
d
ta
ke
s
a
co
ns
ci
ou
s
st
ep
to
in
cr
ea
se
th
e
po
ol
of
O
pe
n
D
at
a
av
ai
la
bl
e
to
th
e
w
eb
.’’
(p
.
1)
28 B. Fecher and S. Friesike
quarter of scientists share their research data—even upon request. According to the
study, the most common reason for denying requests was the amount of effort
required for compliance. Vision presents disciplinary data repositories that are
maintained by the data creators themselves as an appropriate solution to the
problem. This way, scientists would only need to upload their data once instead of
complying with requests. Although Vision emphasizes the necessity to minimize
the submission burden for the author, he does not suggest concrete inducements for
scientists to upload their data (for instance forms of community recognition or
other material rewards). In an empirical study about the sharing behavior among
scientists, Haeussler found out that the sharing of data is indeed closely related to a
form of counter-value (Haeussler 2011, p. 8).
Who is to blame for the fact that Open Data has not yet achieved its break-
through despite its potential? Is it the journal system and its supporting information
practice? Researchers and their reluctance to share? Missing incentive systems? Or
overcomplicated data repositories? The apparent divergence regarding the
impediments of Open Data demonstrates the need for further empirical research on
this issue. Future studies could address the reluctance of researchers to practice
Open Data, the role of journals and supporting material, or the design of an
appropriate online data repository or meta-data structures for research data. The
implied multitude of obstacles for practicing Open Data also illustrates that
research on this issue needs to be holistic.
Open Access to Research Publication
When it comes the Open Access of research publications, the argument is often
less researcher-centric. Cribb and Sari (2010) make the case for the Open Access
to scientific knowledge as a human right (see Table 4). According to them, there is
a gap between the creation and the sharing of knowledge: While scientific
knowledge doubles every 5 years, the access to this knowledge remains limited—
leaving parts of the world in the dark: ‘‘As humanity progresses through the 21st
century (…) many scholars point to the emergence of a disturbing trend: the world
is dividing into those with ready access to knowledge and its fruit, and those
without.’’ (ibid., p. 3). For them, free access to knowledge is a necessity for human
development. In a study on Open Access in library and information science, Rufai
et al. (2012) take the same line. They assume that countries ‘‘falling in the low-
income economic zones have to come on Open Access canvas’’ (ibid., 2011,
p. 225). In times of financial crises, open journal systems and consequently equal
access to knowledge could be an appropriate solution. Additionally, Phelps et al.
(2012) regard Open Access to research publications as a catalyst for development,
whereas limited access to a small subset of people with subscription is a hindrance
to development. Consistently, they define Open Access as ‘‘the widest possible
dissemination of information’’ (ibid., p. 1).
Open Science: One Term, Five Schools of Thought 29
T
ab
le
4
D
em
oc
ra
ti
c
Sc
ho
ol
—
O
pe
n
A
cc
es
s
to
pu
bl
ic
at
io
ns
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
C
ri
bb
&
Sa
ri
(2
01
0)
M
on
og
ra
ph
O
pe
n
Sc
ie
nc
e
-
Sh
ar
in
g
K
no
w
le
dg
e
in
th
e
G
lo
ba
l
C
en
tu
ry
O
pe
n
A
cc
es
s
to
kn
ow
le
dg
e
is
a
to
ol
fo
r
de
ve
lo
pm
en
t.
‘‘
A
s
hu
m
an
it
y
pr
og
re
ss
es
th
e
21
st
ce
nt
ur
y
(.
..)
m
an
y
sc
ho
la
rs
po
in
t
to
th
e
em
er
ge
nc
e
of
a
di
st
ur
bi
ng
tr
en
d:
th
e
w
or
ld
is
di
vi
di
ng
in
to
th
os
e
w
it
h
re
ad
y
ac
ce
ss
to
kn
ow
le
dg
e
an
d
it
s
fr
ui
t,
an
d
th
os
e
w
it
ho
ut
.’’
(p
.
3)
R
uf
ai
et
al
.
(2
01
2)
Jo
ur
na
l
A
rt
ic
le
O
pe
n
A
cc
es
s
Jo
ur
na
ls
in
L
ib
ra
ry
an
d
In
fo
rm
at
io
n
Sc
ie
nc
e:
T
he
St
or
y
so
F
ar
O
pe
n
A
cc
es
s
he
lp
s
un
de
rd
ev
el
op
ed
co
un
tr
ie
s
to
br
id
ge
th
e
ga
p
be
tw
ee
n
th
em
an
d
de
ve
lo
pe
d
co
un
tr
ie
s.
‘‘
T
he
su
st
ai
na
bi
li
ty
of
O
pe
n
A
cc
es
s
jo
ur
na
ls
in
th
e
fie
ld
of
L
IS
is
ev
id
en
t
fr
om
th
e
st
ud
y.
C
ou
nt
ri
es
fa
ll
in
g
in
th
e
lo
w
-i
nc
om
e
ec
on
om
ic
zo
ne
s
ha
ve
to
co
m
e
on
O
pe
n
A
cc
es
s
ca
nv
as
.’’
(p
.
22
5)
Ph
el
ps
,
Fo
x
&
M
ar
in
co
la
(2
01
2)
Jo
ur
na
l
A
rt
ic
le
Su
pp
or
ti
ng
th
e
ad
va
nc
em
en
t
of
sc
ie
nc
e:
O
pe
n
A
cc
es
s
pu
bl
is
hi
ng
an
d
th
e
ro
le
of
m
an
da
te
s
O
pe
n
A
cc
es
s
in
cr
ea
se
s
th
e
di
ss
em
in
at
io
n
of
a
sc
ho
la
r’
s
w
or
k.
‘‘
M
ay
be
on
e
of
th
e
re
as
on
s
th
at
O
pe
n
A
cc
es
s
is
an
in
cr
ea
si
ng
ly
po
pu
la
r
ch
oi
ce
fo
r
so
ci
et
y
jo
ur
na
ls
is
th
at
it
fit
s
w
el
l
w
it
h
m
an
y
so
ci
et
y
m
is
si
on
s
to
en
co
ur
ag
e
th
e
ad
va
nc
em
en
t
of
kn
ow
le
dg
e
by
pr
ov
id
in
g
th
e
w
id
es
t
po
ss
ib
le
di
ss
em
in
at
io
n
w
it
h
no
ba
rr
ie
rs
to
ac
ce
ss
.’’
(p
.
3)
C
ar
ro
l
(2
01
1)
Jo
ur
na
l
A
rt
ic
le
W
hy
fu
ll
O
pe
n
A
cc
es
s
m
at
te
rs
O
pe
n
A
cc
es
s
he
lp
s
ov
er
co
m
in
g
th
e
in
ef
fic
ia
nc
y
of
tr
ad
it
io
na
l
pe
er
-
re
vi
ew
jo
ur
na
ls
.
‘‘
P
ri
ci
ng
of
tr
ad
it
io
na
l,
su
bs
cr
ip
ti
on
-fi
na
nc
ed
sc
ie
nt
ifi
c
jo
ur
na
ls
is
hi
gh
ly
in
ef
fic
ie
nt
.T
he
gr
ow
th
in
di
gi
ta
l
te
ch
no
lo
gi
es
an
d
in
di
gi
ta
l
ne
tw
or
ks
sh
ou
ld
be
dr
iv
in
g
do
w
n
th
e
pr
ic
e
of
ac
ce
ss
to
th
e
sc
ho
la
rl
y
jo
ur
na
l
li
te
ra
tu
re
,b
ut
in
st
ea
d
pr
ic
es
ha
ve
in
cr
ea
se
d
at
a
ra
te
gr
ea
tl
y
in
ex
ce
ss
of
in
fla
ti
on
.’’
(p
.
1)
H
ar
na
d
&
B
ro
dy
(2
00
4)
C
om
pa
ri
ng
th
e
Im
pa
ct
of
O
pe
n
A
cc
es
s
(O
A
)
vs
.
N
on
-O
A
A
rt
ic
le
s
in
th
e
Sa
m
e
Jo
ur
na
ls
O
pe
n
A
cc
es
s
ca
n
in
cr
ea
se
th
e
nu
m
be
r
of
ci
ta
ti
on
s
an
d
he
lp
s
sk
ir
ti
ng
th
e
hi
gh
ac
ce
ss
to
ll
s
of
jo
ur
na
ls
.
‘‘
A
cc
es
s
is
no
t
a
su
ffi
ci
en
t
co
nd
it
io
n
fo
r
ci
ta
ti
on
,
bu
t
it
is
a
ne
ce
ss
ar
y
on
e.
O
A
dr
am
at
ic
al
ly
in
cr
ea
se
s
th
e
nu
m
be
r
of
po
te
nt
ia
l
us
er
s
of
an
y
gi
ve
n
ar
ti
cl
e
by
ad
di
ng
th
os
e
us
er
s
w
ho
w
ou
ld
ot
he
rw
is
e
ha
ve
be
en
un
ab
le
to
ac
ce
ss
it
be
ca
us
e
th
ei
r
in
st
it
ut
io
n
co
ul
d
no
t
af
fo
rd
th
e
ac
ce
ss
-t
ol
ls
of
th
e
jo
ur
na
l
in
w
hi
ch
it
ap
pe
ar
ed
;
th
er
ef
or
e,
it
st
an
ds
to
re
as
on
th
at
O
A
ca
n
on
ly
in
cr
ea
se
bo
th
us
ag
e
an
d
im
pa
ct
.’’
(c
on
ti
nu
ed
)
30 B. Fecher and S. Friesike
T
ab
le
4
(c
on
ti
nu
ed
)
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
H
ar
na
d
et
al
.
(2
00
4)
T
he
A
cc
es
s/
Im
pa
ct
P
ro
bl
em
an
d
th
e
G
re
en
an
d
G
ol
d
R
oa
ds
to
O
pe
n
A
cc
es
s
O
nl
y
5%
of
jo
ur
na
ls
ar
e
go
ld
,
bu
t
ov
er
90
%
ar
e
al
re
ad
y
gr
ee
n
(i
.e
.,
th
ey
ha
ve
gi
ve
n
th
ei
r
au
th
or
s
th
e
gr
ee
n
lig
ht
to
se
lf
-a
rc
hi
ve
);
ye
t
on
ly
ab
ou
t
10
-2
0%
of
ar
ti
cl
es
ha
ve
be
en
se
lf
-a
rc
hi
ve
d.
‘‘
A
lo
ng
w
it
h
th
e
su
bs
ta
nt
ia
l
re
ce
nt
ri
se
in
O
A
co
ns
ci
ou
sn
es
s
w
or
ld
w
id
e,
th
er
e
ha
s
al
so
be
en
an
un
fo
rt
un
at
e
te
nd
en
cy
to
eq
ua
te
O
A
ex
cl
us
iv
el
y
w
it
h
O
A
jo
ur
na
lp
ub
li
sh
in
g
(i
.e
.,
th
e
go
ld
en
ro
ad
to
O
A
)
an
d
to
ov
er
lo
ok
th
e
fa
st
er
,
su
re
r,
an
d
al
re
ad
y
m
or
e
he
av
il
y
tr
av
el
ed
gr
ee
n
ro
ad
of
O
A
se
lf
-a
rc
hi
vi
ng
.’’
(p
.
31
4)
A
nt
el
m
an
n
(2
00
4)
D
o
O
pe
n-
A
cc
es
s
A
rt
ic
le
s
H
av
e
a
G
re
at
er
R
es
ea
rc
h
Im
pa
ct
?
O
pe
n
A
cc
es
s
ar
ti
cl
es
ha
ve
a
hi
gh
er
re
se
ar
ch
im
pa
ct
th
an
no
t
fr
ee
ly
av
ai
la
bl
e
ar
ti
cl
es
.
‘‘
T
hi
s
st
ud
y
in
di
ca
te
s
th
at
,a
cr
os
s
a
va
ri
et
y
of
di
sc
ip
li
ne
s,
O
pe
n-
A
cc
es
s
ar
ti
cl
es
ha
ve
a
gr
ea
te
r
re
se
ar
ch
im
pa
ct
th
an
ar
ti
cl
es
th
at
ar
e
no
t
fr
ee
ly
av
ai
la
bl
e.
’’
(p
.
37
9)
Open Science: One Term, Five Schools of Thought 31
Apart from the developmental justification, Phelps et al. (2012) mention
another, quite common, logic for Open Access to research publications: ‘‘It is
argued (…) that research funded by tax-payers should be made available to the
public free of charge so that the tax-payer does not in effect pay twice for the
research (…)’’ (ibid., p.1). ‘Paying twice for research’ refers to the fact that
citizens do not only indirectly finance government-funded research but also the
subsequent acquisition of publications from public libraries.
Carroll (2011) also criticizes the inefficiency of traditional, subscription-
financed scientific journals in times of growth in digital technologies and net-
works. He argues that prices should have dropped considerably in the light of the
Internet—instead they have increased drastically. He further argues that the Open
Access model would shift the balance of power in journal publishing and greatly
enhance the efficiency and efficacy of scientific communication (ibid., p. 1). By
shifting the financing away from subscriptions, the Open Access model re-aligns
copyright and enables broad reuse of publications while at the same time assuring
authors and publishers that they receive credit for their effort (e.g. through open
licensing).
Pragmatic School: Making Research More Efficient
Advocates of the pragmatic school regard Open Science as a method to make
research and knowledge dissemination more efficient. It thereby considers science
as a process that can be optimized by, for instance, modularizing the process of
knowledge creation, opening the scientific value chain, including external
knowledge and allowing collaboration through online tools. The notion of ‘open’
follows in this regard very much the disclosed production process known from
open innovation concepts (see Table 5).
Tacke (2010) for instance builds upon the connection between open innovation
and Open Science. Similar to open innovation, the author applies the outside-in
(including external knowledge to the production process) and inside-out (spill-
overs from the formerly closed production process) principles to science. He
regards Web 2.0 in this regard as a fertile ground for practicing collaborative
research (ibid., p. 41) and emphasizes the ‘wisdom of the crowds’ as a necessity in
solving today’s scientific problems: ‘‘Taking a closer look at science reveals a
similar situation: problems have become more complex and often require a joint
effort in order to find a solution’’ (ibid., p. 37).
Tacke refers to Hunter and Leahey (2008) who examined trends in collabora-
tion over a 70 years period. They found out that between 1935 and 1940 only
11 % of the observed articles were co-authored, whereas between 2000 and 2005
almost 50 % were coauthored—a significant increase that according to Tacke
issues from the increasing complexity of research problems over time; research
problems that apparently can only be solved through multi-expert consideration.
Indeed, Bozeman and Corley (2004) found out in an empirical study on researcher
32 B. Fecher and S. Friesike
T
ab
le
5
Pr
ag
m
at
ic
sc
ho
ol
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
T
ac
ke
(2
01
0)
Pr
oc
ee
di
ng
s
O
pe
n
Sc
ie
nc
e
2.
0:
H
ow
re
se
ar
ch
an
d
ed
uc
at
io
n
ca
n
be
ne
fit
fr
om
op
en
in
no
va
ti
on
an
d
W
eb
2.
0
C
om
pl
ex
si
tu
at
io
ns
ca
n
be
be
tt
er
ju
dg
ed
by
th
e
co
ll
ec
ti
ve
w
is
do
m
of
th
e
cr
ow
ds
.
‘‘
H
ow
ev
er
,s
ev
er
al
cr
it
ic
s
em
ph
as
iz
e
th
at
on
e
pe
rs
on
ca
n
ne
ve
r
po
ss
es
s
en
ou
gh
kn
ow
le
dg
e
in
or
de
r
to
ju
dg
e
co
m
pl
ex
si
tu
at
io
ns
ex
pe
di
en
tl
y,
an
d
th
at
it
m
ay
m
or
e
ap
pr
op
ri
at
e
to
us
e
th
e
co
ll
ec
ti
ve
w
is
do
m
of
cr
ow
ds
.’’
(p
.
37
)
H
ae
us
sl
er
(2
01
1)
Jo
ur
na
l
A
rt
ic
le
In
fo
rm
at
io
n-
sh
ar
in
g,
so
ci
al
ca
pi
ta
l,
O
pe
n
Sc
ie
nc
e
Sc
ie
nt
is
ts
ex
pe
ct
a
be
ne
fit
fr
om
sh
ar
in
g
in
fo
rm
at
io
n.
‘‘
M
y
st
ud
y
sh
ow
ed
th
at
fa
ct
or
s
re
la
te
d
to
so
ci
al
ca
pi
ta
l
in
flu
en
ce
th
e
im
pa
ct
of
th
e
co
m
pe
ti
ti
ve
va
lu
e
of
th
e
re
qu
es
te
d
in
fo
rm
at
io
n
on
a
sc
ie
nt
is
t’
s
de
ci
si
on
to
sh
ar
e
or
w
it
hh
ol
d
in
fo
rm
at
io
n.
’’
(p
.
11
7)
N
ey
lo
n
&
W
u
(2
00
9)
Sy
m
po
si
um
W
or
ks
ho
p
O
pe
n
Sc
ie
nc
e:
to
ol
s,
ap
pr
oa
ch
es
,
an
d
im
pl
ic
at
io
ns
O
pe
n
Sc
ie
nc
e
to
ol
s
ne
ed
to
fit
to
th
e
sc
ie
nt
ifi
c
pr
ac
ti
ce
of
re
se
ar
ch
er
s.
‘‘
T
oo
ls
w
he
th
er
th
ey
be
so
ci
al
ne
tw
or
ki
ng
si
te
s,
el
ec
tr
on
ic
la
bo
ra
to
ry
no
te
bo
ok
s,
or
co
nt
ro
ll
ed
vo
ca
bu
la
ri
es
,
m
us
t
be
bu
il
t
to
he
lp
sc
ie
nt
is
ts
do
w
ha
t
th
ey
ar
e
al
re
ad
y
do
in
g,
no
t
w
ha
t
th
e
to
ol
de
si
gn
er
fe
el
s
th
ey
sh
ou
ld
be
do
in
g.
’’
(p
.5
43
)
N
ie
ls
en
(2
01
2)
M
on
og
ra
ph
R
ei
nv
en
ti
ng
D
is
co
ve
ry
:
T
he
N
ew
E
ra
of
N
et
w
or
ke
d
Sc
ie
nc
e
‘‘W
e
ne
ed
to
im
ag
in
e
a
w
or
ld
w
he
re
th
e
co
ns
tr
uc
ti
on
of
th
e
sc
ie
nt
ifi
c
in
fo
rm
at
io
n
co
m
m
on
s
ha
s
co
m
e
to
fr
ui
ti
on
.T
hi
s
is
a
w
or
ld
w
he
re
al
l
sc
ie
nt
ifi
c
kn
ow
le
dg
e
ha
s
be
en
m
ad
e
av
ai
la
bl
e
on
li
ne
,
an
d
is
ex
pr
es
se
d
in
a
w
ay
th
at
ca
n
be
un
de
rs
to
od
by
co
m
pu
te
rs
.’’
(i
bi
d.
,
p.
11
1)
W
ei
ss
(2
00
5)
T
he
P
ow
er
of
C
ol
le
ct
iv
e
In
te
ll
ig
en
ce
Pa
rt
ic
ip
at
io
n
in
co
ll
ec
ti
ve
kn
ow
le
dg
e-
cr
ea
ti
on
de
pe
nd
s
on
th
e
to
ol
s
an
d
se
rv
ic
es
av
ai
la
bl
e.
‘‘
W
it
h
ev
er
m
or
e
so
ph
is
ti
ca
te
d
A
P
Is
an
d
W
eb
se
rv
ic
es
be
in
g
sh
ar
ed
,
at
tr
ac
ti
ng
a
cr
it
ic
al
m
as
s
of
de
ve
lo
pe
rs
to
bu
il
d
to
ol
s
on
th
os
e
se
rv
ic
es
,
an
d
a
cr
it
ic
al
m
as
s
of
us
er
s
co
nt
ri
bu
ti
ng
to
th
e
se
rv
ic
es
’
va
lu
e
by
ag
gr
eg
at
in
g
sh
ar
ed
kn
ow
le
dg
e
an
d
co
nt
en
t,
w
e
ha
ve
th
e
m
ak
in
gs
of
a
tr
ul
y
co
ll
ab
or
at
iv
e,
se
lf
-o
rg
an
iz
in
g
pl
at
fo
rm
.’’
(p
.
4)
(c
on
ti
nu
ed
)
Open Science: One Term, Five Schools of Thought 33
T
ab
le
5
(c
on
ti
nu
ed
)
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
A
ra
zy
et
al
.
(2
00
6)
W
is
do
m
of
th
e
C
ro
w
ds
:
D
ec
en
tr
al
iz
ed
K
no
w
le
dg
e
C
on
st
ru
ct
io
n
in
W
ik
ip
ed
ia
Pa
rt
ic
ip
at
io
n
in
th
e
co
-c
re
at
io
n
of
kn
ow
le
dg
e
de
pe
nd
s
on
th
e
en
tr
y
ba
rr
ie
rs
.
‘‘
T
o
en
ti
ce
pa
rt
ic
ip
at
io
n,
or
ga
ni
za
ti
on
s
us
in
g
w
ik
is
sh
ou
ld
st
ri
ve
to
el
im
in
at
e
ba
rr
ie
rs
(e
.g
.
al
lo
w
us
er
s
to
po
st
an
on
ym
ou
sl
y)
an
d
pr
ov
id
e
in
ce
nt
iv
es
fo
r
co
nt
ri
bu
ti
on
s.
’’
G
ow
er
s
&
N
ie
ls
en
(2
00
9)
M
as
si
ve
ly
C
ol
la
bo
ra
ti
ve
M
at
he
m
at
ic
s
N
at
ur
al
sc
ie
nc
es
ca
n
pr
ofi
t
fr
om
co
ll
ab
or
at
io
n
of
re
se
ar
ch
er
s.
‘‘
B
ut
op
en
sh
ar
in
g
of
ex
pe
ri
m
en
ta
l
da
ta
do
es
at
le
as
t
al
lo
w
op
en
da
ta
an
al
ys
is
.T
he
w
id
es
pr
ea
d
ad
op
ti
on
of
su
ch
op
en
-
so
ur
ce
te
ch
ni
qu
es
w
il
l
re
qu
ir
e
si
gn
ifi
ca
nt
cu
lt
ur
al
ch
an
ge
s
in
sc
ie
nc
e,
as
w
el
l
as
th
e
de
ve
lo
pm
en
t
of
ne
w
on
li
ne
to
ol
s.
W
e
be
li
ev
e
th
at
th
is
w
il
l
le
ad
to
th
e
w
id
es
pr
ea
d
us
e
of
m
as
s
co
ll
ab
or
at
io
n
in
m
an
y
fie
ld
s
of
sc
ie
nc
e,
an
d
th
at
m
as
s
co
ll
ab
or
at
io
n
w
il
l
ex
te
nd
th
e
li
m
it
s
of
hu
m
an
pr
ob
le
m
-
so
lv
in
g
ab
il
it
y.
’’
(p
.
88
1)
34 B. Fecher and S. Friesike
collaboration that some of the most frequent reasons for collaborative research are
access to expertise, aggregation of different kinds of knowledge, and productivity.
Apart from the assumed increasing complexity of today’s research problems and
the researcher’s pursuit of productivity, Tacke also points out the technical pro-
gress that enables and fosters collaboration in the first place. The Web 2.0 allows
virtually anyone to participate in the process of knowledge creation (ibid., p. 4). It
is thus tenable to consider, besides the strive for productivity and the increasing
complexity of research process, also the emerging communication and collabo-
ration technology as a solid reason for collaborative research.
Nielsen (2012) argues accordingly. He proceeds from the assumption that
openness indicates a pivotal shift in the scientific practice in the near future—
namely from closed to collaborative. Through reference to numerous examples of
collective intelligence, such as the Polymath Project (in which Tim Gower posted
a mathematical problem on his blog that was then solved by a few experts) or the
Galaxy Zoo Project (an online astronomy project which amateurs can join to assist
morphological classification), he emphasizes the crucial role of online tools in this
development: ‘‘Superficially, the idea that online tools can make us collectively
smarter contradicts the idea, currently fashionable in some circles, that the
Internet is reducing our intelligence’’ (ibid., p. 26).
Nielsen’s presentation of examples for collaborative knowledge discoveries
permits conjecture on the wide variety of collaborative research when it comes to
scale and quality—be it a rather-small scale expert collaboration as in the Poly-
math project or large-scale amateur collaboration as in the Galaxy Zoo project.
Nielsen also points towards the importance of Open Data (ibid., p. 101) and
promotes comprehensive scientific commons: ‘‘We need to imagine a world where
the construction of the scientific information commons has come to fruition. This is
a world where all scientific knowledge has been made available online, and is
expressed in a way that can be understood by computers’’ (ibid., p. 111). It
becomes obvious that Nielsen’s vision of Open Science is based on vesting con-
ditions like the enhanced use of online platforms, the inclusion of non-experts in
the discovery process and, not least, the willingness to share on the part of sci-
entists; all of which show that Nielsen’s notion of collective research is also bound
to numerous profound changes in the scientific practice—not to mention the
technological ability to understand all formats of knowledge by computers.
Haeussler (2011) addresses the sharing behaviour of researchers in an empirical
study among scientists. She uses arguments from social capital theory in order to
explain why individuals share information even at (temporary) personal cost. Her
notion of Open Science is thereby strongly bound to the free sharing of infor-
mation (similar to one of Nielsen’s core requirements for Open Science). One of
Haeussler’s results concerns the competitive value of information. She concludes:
‘‘My study showed that factors related to social capital influence the impact of the
competitive value of the requested information on a scientist’s decision to share or
withhold information.’’ (ibid., p. 117). If academic scientists expect the inquirer to
be able to return the favor, they are much more likely to share information.
Haeussler’s study shows that the scientist’s sharing behaviour is not altruistic per
Open Science: One Term, Five Schools of Thought 35
se—which is often taken for granted in texts on Open Science. Instead, it is rather
built on an, even non-monetary, system of return. The findings raise the question
as to how the sharing of information and thus, at least according to Nielsen and
Haeussler, a basic requirement for Open Science could be expedited. It implies
that a change in scientific practice comes with fundamental changes in the culture
of science; in this case the incentives to share information.
Neylon and Wu (2009), in a general text on the requirements for Open Science,
elaborate more on Web 2.0 tools that facilitate and accelerate scientific discovery.
According to them, tools ‘‘whether they be social networking sites, electronic
laboratory notebooks, or controlled vocabularies, must be built to help scientists
do what they are already doing, not what the tool designer feels they should be
doing’’ (ibid., p. 543). The authors thereby regard the implementation of Web 2.0
tools in close relation to the existing scientific practice. Following this, scientific
tools can only foster scientific discovery if they tie in with the research practice.
The most obvious target, according to the authors, is in this regard ‘‘tools that
make it easier to capture the research record so that it can be incorporated into
and linked from papers’’ (ibid., p. 543). Unfortunately, the authors do not further
elaborate on how potential tools could be integrated in the researchers’ work flows.
Nonetheless, they take a new point of view when it comes to the role of Web 2.0
tools and the necessity to integrate these into an existing research practice. In this
regard, they differ from what we subsume as the infrastructure school.
The authors mentioned in this chapter reveal visionary perspectives on scien-
tific practice in the age of Web 2.0. Nonetheless, we assume that further research
must focus on the structural requirements of Open Science, the potential incentives
for scientists to share information, or the potential inclusion of software tools in
the existing practice. In other words: The assumed coherence in regard to Open
Science still lacks empirical research.
Infrastructure School: Architecture Matters Most
The infrastructure school is concerned with the technical infrastructure that
enables emerging research practices on the Internet, for the most part software
tools and applications, as well as computing networks. In a nutshell, the infra-
structure school regards Open Science as a technological challenge (see Table 6).
Literature on this matter is therefore often practice-oriented and case-specific; it
focuses on the technological requirements that facilitate particular research prac-
tices (e.g. the Open Science Grid).
In 2003, Nentwich (2003, p. 1) coined the term cyberscience to describe the
trend of applying information and communication technologies to scientific
research—a development that has prospered since then. The authors locate
cyberscience in a Web 2.0 context, alluding not only to the technical progress that
fosters collaboration and interaction among scientists, but also to a cultural change
similar to the open source development. Most Open Science practices described in
36 B. Fecher and S. Friesike
T
ab
le
6
In
fr
as
tr
uc
tu
re
sc
ho
ol
A
ut
ho
r
(Y
ea
r)
T
yp
e
of
Pu
bl
ic
at
io
n
T
it
le
C
on
te
nt
A
lt
un
ay
et
al
.
(2
01
0)
A
rt
ic
le
A
Sc
ie
nc
e
D
ri
ve
n
P
ro
du
ct
io
n
C
yb
er
in
fr
as
tr
uc
tu
re
—
th
e
O
pe
n
Sc
ie
nc
e
G
ri
d
Sc
ie
nc
e
gr
id
ca
n
be
us
ed
fo
r
hi
gh
-t
hr
ou
gh
pu
t
re
se
ar
ch
pr
oj
ec
ts
.
‘‘T
hi
s
ar
ti
cl
e
de
sc
ri
be
s
th
e
O
pe
n
Sc
ie
nc
e
G
ri
d,
a
la
rg
e
di
st
ri
bu
te
d
co
m
pu
ta
ti
on
al
in
fr
as
tr
uc
tu
re
in
th
e
U
ni
te
d
St
at
es
w
hi
ch
su
pp
or
ts
m
an
y
di
ffe
re
nt
hi
gh
-t
hr
ou
gh
pu
t
sc
ie
nt
ifi
c
ap
pl
ic
at
io
ns
(.
..)
to
fo
rm
m
ul
ti
-
do
m
ai
n
in
te
gr
at
ed
di
st
ri
bu
te
d
sy
st
em
s
fo
r
sc
ie
nc
e.
’’
(
p.
20
1)
D
e
R
ou
re
et
al
.
(2
00
8)
C
on
fe
re
nc
e
Pa
pe
r
T
ow
ar
ds
O
pe
n
Sc
ie
nc
e:
th
e
m
yE
xp
er
im
en
t
ap
pr
oa
ch
‘‘m
yE
xp
er
im
en
ti
s
th
e
fir
st
re
po
si
to
ry
of
m
et
ho
ds
w
hi
ch
m
aj
or
s
on
th
e
so
ci
al
di
m
en
si
on
,
an
d
w
e
ha
ve
de
m
on
st
ra
te
d
th
at
an
on
li
ne
co
m
m
un
it
y
an
d
w
or
kfl
ow
co
ll
ec
ti
on
ha
s
be
en
es
ta
bl
is
he
d
an
d
is
no
w
gr
ow
in
g
ar
ou
nd
it
.’’
(p
.
23
50
)
Fo
st
er
(2
00
2)
Jo
ur
na
l
A
rt
ic
le
T
he
gr
id
:
A
ne
w
in
fr
as
tr
uc
tu
re
fo
r
21
st
ce
nt
ur
y
sc
ie
nc
e
C
om
pu
ta
ti
on
is
a
m
aj
or
ch
al
le
ng
e
fo
r
sc
ie
nt
ifi
c
co
ll
ab
or
at
io
n
in
fu
tu
re
.
‘‘D
ri
ve
n
by
in
cr
ea
si
ng
ly
co
m
pl
ex
pr
ob
le
m
s
an
d
by
ad
va
nc
es
in
un
de
rs
ta
nd
in
g
an
d
te
ch
ni
qu
e,
an
d
po
w
er
ed
by
th
e
em
er
ge
nc
e
of
th
e
In
te
rn
et
(.
..)
,
to
da
y’
s
sc
ie
nc
e
is
as
m
uc
h
ba
se
d
on
co
m
pu
ta
ti
on
,
da
ta
an
al
ys
is
,
an
d
co
ll
ab
or
at
io
n
as
on
th
e
ef
fo
rt
s
of
in
di
vi
du
al
ex
pe
ri
m
en
ta
li
st
s
an
d
th
eo
ri
st
s.
’’
(p
.
52
)
D
e
R
ou
re
et
al
.
(2
00
3)
B
oo
k
C
ha
pt
er
T
he
Se
m
an
ti
c
G
ri
d:
A
F
ut
ur
e
e-
Sc
ie
nc
e
In
fr
as
tr
uc
tu
re
K
no
w
le
dg
e
la
ye
r
se
rv
ic
es
ar
e
ne
ce
ss
ar
y
fo
r
se
am
le
ss
ly
au
to
m
at
in
g
a
si
gn
ifi
ca
nt
ra
ng
e
of
ac
ti
on
s.
‘‘W
hi
le
th
er
e
ar
e
st
il
l
m
an
y
op
en
pr
ob
le
m
s
co
nc
er
ne
d
w
it
h
m
an
ag
in
g
m
as
si
ve
ly
di
st
ri
bu
te
d
co
m
pu
ta
ti
on
s
in
an
ef
fic
ie
nt
m
an
ne
r
an
d
in
ac
ce
ss
in
g
an
d
sh
ar
in
g
in
fo
rm
at
io
n
fr
om
he
te
ro
ge
no
us
so
ur
ce
s
(.
..)
,
w
e
be
li
ev
e
th
e
fu
ll
po
te
nt
ia
lo
fG
ri
d
co
m
pu
ti
ng
ca
n
on
ly
be
re
al
is
ed
by
fu
ll
y
ex
pl
oi
ti
ng
th
e
fu
nc
ti
on
al
it
y
an
d
ca
pa
bi
li
ti
es
pr
ov
id
ed
by
kn
ow
le
dg
e
la
ye
r
se
rv
ic
es
.’’
(p
.
43
2)
H
ey
&
T
re
fe
th
en
(2
00
5)
A
rt
ic
le
C
yb
er
in
fr
as
tr
uc
tu
re
fo
r
e-
Sc
ie
nc
e
Se
rv
ic
e-
or
ie
nt
ed
sc
ie
nc
e
ha
s
th
e
po
te
nt
ia
l
to
in
cr
ea
se
in
di
vi
du
al
an
d
co
lle
ct
iv
e
sc
ie
nt
ifi
c
pr
od
uc
ti
vi
ty
by
m
ak
in
g
po
w
er
fu
l
in
fo
rm
at
io
n
to
ol
s
av
ai
la
bl
e
to
al
l,
an
d
th
us
en
ab
li
ng
th
e
w
id
es
pr
ea
d
au
to
m
at
io
n
of
da
ta
an
al
ys
is
an
d
co
m
pu
ta
ti
on
.
‘‘A
lt
ho
ug
h
th
er
e
is
cu
rr
en
tl
y
m
uc
h
fo
cu
s
in
th
e
G
ri
d
co
m
m
un
it
y
on
th
e
lo
w
le
ve
l
m
id
dl
ew
ar
e,
th
er
e
ar
e
su
bs
ta
nt
ia
l
re
se
ar
ch
ch
al
le
ng
es
fo
r
co
m
pu
te
r
sc
ie
nt
is
ts
to
de
ve
lo
p
hi
gh
-l
ev
el
in
te
ll
ig
en
tm
id
dl
ew
ar
e
se
rv
ic
es
th
at
ge
nu
in
el
y
su
pp
or
tt
he
ne
ed
s
of
sc
ie
nt
is
ts
an
d
al
lo
w
th
em
to
ro
ut
in
el
y
co
ns
tr
uc
t
se
cu
re
V
O
s
an
d
m
an
ag
e
th
e
ve
ri
ta
bl
e
de
lu
ge
of
sc
ie
nt
ifi
c
da
ta
th
at
w
il
l
be
ge
ne
ra
te
d
in
th
e
ne
xt
fe
w
ye
ar
s.
’’
(p
.
82
0)
Open Science: One Term, Five Schools of Thought 37
the previously discussed schools have to be very much understood as a part of an
interplay between individuals and the tools at hand. The technical infrastructure is
in this regard a cyclic element for almost all identified schools in this chapter;
imagine Open Data without online data repositories or collaborative writing
without web-based real-time editors. In one way or another it is the new techno-
logical possibilities that change established scientific practices or even constitute
new ones, as in the case of altmetrics or scientific blogging.
Still, we decided to include the infrastructure school as a separate and super-
ordinate school of thought due to discernible infrastructure trends in the context of
Open Science; trends that in our eyes enable research on a different scale. We will
therefore not list the multitude of Open Science projects and their technological
infrastructure but instead dwell on two infrastructure trends and selected examples.
It has to be said that these trends are not mutually exclusive but often interwoven.
The trends are:
• Distributed computing: Using the computing power of many users for research
• Social and collaboration networks for scientists: Enabling researcher interaction
and collaboration
Distributed Computing
A striking example for distributed computing in science is the Open Science Grid,
‘‘a large distributed computational infrastructure in the United States, which
supports many different high-throughput scientific applications (…) to form multi-
domain integrated distributed systems for science.’’ (Altunay et al. 2010, p. 201).
Put simply, the Open Science Grid enables large-scale, data-intensive research
projects by connecting multiple computers to a high-performance computer net-
work. Autonomous computers are interconnected in order to achieve high-
throughput research goals. The Open Science Grid provides a collaborative
research environment for communities of scientists and researchers to work
together on distributed computing problems (ibid., p. 202).
It is thus not completely accurate to confine the Open Science Grid to its
computational power alone as it also provides access to storage resources, offers a
software stack, and uses common operational services. Nonetheless, its core
strength resides in the computational power of many single computers, allowing
scientists to realize data-intensive research projects, high throughput processing
and shared storage. Typical projects that use the Open Science Grid are therefore
CPU-intensive, comprise a large number of independent jobs, demand a significant
amount of database-access, and/or implicate large input and output data from
remote servers.
Foster encapsulates the increasing importance of grids as an essential com-
puting infrastructure: ‘‘Driven by increasingly complex problems and by advances
38 B. Fecher and S. Friesike
in understanding and technique, and powered by the emergence of the Internet
(…), today’s science is as much based on computation, data analysis, and col-
laboration as on the efforts of individual experimentalists and theorists.’’ (2003,
p. 52). Foster (ibid.) further emphasizes the potential to enable large-scale sharing
of resources within distributed, often loosely coordinated and virtual groups—an
idea that according to the author is not all new. He refers to a case from 1968,
when designers of the Multics operating system envisioned a computer facility
operating as a utility (ibid., p. 52). What is new though, according to Foster, is the
performance of such network utilities in the light of technological progress (ibid.,
p. 53).
Distributed computing allows scientists to realize research almost indepen-
dently from the individual computing resources. It is thereby an opportunity to
untie a researcher from locally available resources by providing a highly efficient
computer network. Considering the importance of big data, scientific computing
will be an essential research infrastructure in the near future. One could say that
the objective of scientific computing is the increase of performance by intercon-
necting many autonomous and dispersed computers.
Social and Collaboration Networks
A second, more researcher-centric, infrastructure trend focuses on platforms that
foster interaction between locally dispersed individuals and allow collaboration by
implementing Web 2.0 tools. Drawing on the example of myExperiment, De
Roure et al. (2008) propose four key capabilities of what they consider a Social
Virtual Research Environment (SVRE):
• According to the authors, a SVRE should firstly facilitate the management and
sharing of research objects. These can be any digital commodities that are used
and reused by researchers (e.g. methods and data).
• Secondly, it should have incentives for researchers to make their research
objects available.
• Thirdly, the environment should be open and extensible—meaning that soft-
ware, tools and services can be easily integrated.
• Fourthly, it should provide a platform to action research. Actioning research is,
in the authors’ understanding, what makes a platform an actual research envi-
ronment. Research objects are in this regard not just stored and exchanged but
they are used in the conduct of research (De Roure et al. 2008, p. 182).
This depiction of a SVRE does of course not exclude mass computation (the
third capability in fact endorses the integration of additional services)—it does,
however, clearly focus on the interaction and collaboration between researchers.
Furthermore, it becomes apparent that the authors’ notion of ‘virtual social
research’ involves a multitude of additional tools and services enabling
Open Science: One Term, Five Schools of Thought 39
collaborative research. It implies (directly or indirectly) the existence of integrated
large-scale data repositories that allow researchers to make their data publicly
available in the first place.
Nentwich and König (2012, p. 42), who are also featured in this book, point
towards other social networks for scientists, such as ResearchGate, Mendeley,
Nature Networks, Vivo, or Academia.edu. The authors state that present academic
social networks are principally functional for scientists and do not (yet) feature a
convergence towards one provider. They further point towards the use of multi-
purpose social networks (such as Facebook, LinkedIN, or Xing) among scientists.
These are used for thematic expert groups (not only scientists), self-marketing, or
job exchange. In the chapter ‘‘Academia Goes Facebook?: The Potential of Social
Network Sites in the Scholarly Realm’’, the authors elaborate more on the role of
social networks for scientists—including tools for scientific collaboration such as
collaborative writing environments.
Measurement School: Finding Alternative Measurements
for Scientific Output
The measurement school is concerned with alternative standards to ascertain
scientific impact. Inarguably, the impact factor, which measures the average
number of citations to an article in a journal, has a decisive influence on a
researcher’s reputation and thereby his or her funding and career opportunities. It
is therefore hardly surprising that a discourse about Open Science and how science
will be done in the future is accompanied by the crucial question as to how
scientific impact can be measured in the digital age.
Advocates of the Open Science measurement school express the following
concerns about the current impact factor:
• The peer review is time-consuming. (McVeigh 2004; Priem and Costello 2010)
• The impact is linked to a journal rather than directly to an article. (McVeigh
2004)
• New publishing formats (e.g. online Open Access journals, blogs) are seldom in
a journal format to which an impact factor can be assigned to (Weller and
Puschmann 2011; Priem et al. 2012; Yeong and Abdullah 2012)
Accordingly, this school argues the case for an alternative and faster impact
measurement that includes other forms of publication and the social web coverage
of a scientific contribution. The general credo is: As the scholarly workflow is
migrates increasingly to the web, formerly hidden uses like reading, bookmarking,
sharing, discussing, and rating are leaving traces online and offer a new basis by
which to measure scientific impact. The umbrella term for these new impact
measurements is altmetrics (See Table 7).
40 B. Fecher and S. Friesike
T
ab
le
7
M
ea
su
re
m
en
t
sc
ho
ol
A
ut
ho
r
(Y
ea
r)
T
it
le
C
on
te
nt
Pr
ie
m
&
L
ig
ht
C
os
te
ll
o
(2
01
0)
Pr
oc
ee
di
ng
s
H
ow
an
d
w
hy
sc
ho
la
rs
ci
te
on
tw
it
te
r
T
w
ee
ts
ca
n
be
us
ed
as
an
al
te
rn
at
iv
e
ba
si
s
to
m
ea
su
re
sc
ie
nt
ifi
c
im
pa
ct
.
’’T
w
it
te
r
ci
ta
ti
on
s
ar
e
m
uc
h
fa
st
er
th
an
tr
ad
it
io
na
l
ci
ta
ti
on
s,
w
it
h
40
%
oc
cu
rr
in
g
w
it
hi
n
on
e
w
ee
k
of
th
e
ci
te
d
re
so
ur
ce
’s
pu
bl
ic
at
io
n.
F
in
al
ly
,
w
hi
le
T
w
it
te
r
ci
ta
ti
on
s
ar
e
di
ffe
re
nt
fr
om
tr
ad
it
io
na
l
ci
ta
ti
on
s,
ou
r
pa
rt
ic
ip
an
ts
su
gg
es
t
th
at
th
ey
st
il
l
re
pr
es
en
t
an
d
tr
an
sm
it
sc
ho
la
rl
y
im
pa
ct
.’’
W
el
le
r
&
Pu
sc
hm
an
n
(2
01
1)
Po
st
er
T
w
it
te
r
fo
r
Sc
ie
nt
ifi
c
C
om
m
un
ic
at
io
n:
H
ow
C
an
C
it
at
io
ns
/R
ef
er
en
ce
s
be
Id
en
ti
fie
d
an
d
M
ea
su
re
d?
Sc
ie
nt
ifi
c
tw
ee
ts
ca
n
be
id
en
ti
fie
d
in
nu
m
er
ou
s
w
ay
s.
Pr
ie
m
et
al
.
(2
01
2)
Pr
oc
ee
di
ng
s
U
nc
ov
er
in
g
im
pa
ct
s:
C
it
ed
In
an
d
to
ta
l-
im
pa
ct
,
tw
o
ne
w
to
ol
s
fo
r
ga
th
er
in
g
al
tm
et
ri
cs
C
it
ed
In
an
d
to
ta
l-
im
pa
ct
ar
e
to
ol
s
th
at
ca
n
m
ea
su
re
sc
ie
nt
ifi
c
im
pa
ct
.
‘‘C
it
ed
In
an
d
to
ta
l-
im
pa
ct
ar
e
tw
o
to
ol
s
in
ea
rl
y
de
ve
lo
pm
en
tt
ha
ta
im
to
ga
th
er
al
tm
et
ri
cs
.A
te
st
of
th
es
e
to
ol
s
us
in
g
a
re
al
-l
if
e
da
ta
se
t
sh
ow
s
th
at
th
ey
w
or
k,
an
d
th
at
th
er
e
is
a
m
ea
ni
ng
fu
l
am
ou
nt
of
al
tm
et
ri
cs
da
ta
av
ai
la
bl
e.
’’
M
cV
ei
gh
(2
01
2)
N
ew
s
pa
pe
r
ar
ti
cl
e
T
w
it
te
r,
pe
er
re
vi
ew
an
d
al
tm
et
ri
cs
:
th
e
fu
tu
re
of
re
se
ar
ch
im
pa
ct
as
se
ss
m
en
t
‘‘S
o
w
hy
is
a
re
vo
lu
ti
on
ne
ed
ed
?
B
ec
au
se
lo
ng
be
fo
re
th
e
to
ol
s
ev
en
ex
is
te
d
to
do
an
yt
hi
ng
ab
ou
t
it
,
m
an
y
in
th
e
re
se
ar
ch
co
m
m
un
it
y
ha
ve
be
m
oa
ne
d
th
e
st
ra
ng
le
ho
ld
th
e
im
pa
ct
fa
ct
or
of
a
re
se
ar
ch
pa
pe
r
ha
s
he
ld
ov
er
re
se
ar
ch
fu
nd
in
g,
ca
re
er
s
an
d
re
pu
ta
ti
on
s.
’’
Pr
ie
m
&
H
em
m
in
ge
r
(2
01
2)
Jo
ur
na
l
ar
ti
cl
e
D
ec
ou
pl
in
g
th
e
sc
ho
la
rl
y
jo
ur
na
l
‘‘T
hi
s
ti
gh
t
co
up
li
ng
[o
f
th
e
jo
ur
na
l
sy
st
em
]
m
ak
es
it
di
ffi
cu
lt
to
ch
an
ge
an
y
on
e
as
pe
ct
of
th
e
sy
st
em
,
ch
ok
in
g
ou
t
in
no
va
ti
on
.’’
Y
eo
ng
&
A
bd
ul
la
h
(2
01
2)
Po
si
ti
on
pa
pe
r
A
lt
m
et
ri
cs
:
th
e
ri
gh
t
st
ep
fo
rw
ar
d
A
ltm
et
ri
cs
ar
e
an
al
te
rn
at
iv
e
m
et
ri
c
fo
r
an
al
ys
in
g
an
d
in
fo
rm
in
g
sc
ho
la
rs
hi
p
ab
ou
t
im
pa
ct
.
‘‘A
lt
m
et
ri
cs
re
ly
on
a
w
id
er
se
t
of
m
ea
su
re
s
[t
ha
n
w
eb
om
et
ri
cs
]
(.
..)
ar
e
fo
cu
se
d
on
th
e
cr
ea
ti
on
an
d
st
ud
y
of
ne
w
m
et
ri
cs
ba
se
d
on
th
e
so
ci
al
w
eb
fo
r
an
al
ys
in
g
an
d
in
fo
rm
in
g
sc
ho
la
rs
hi
p.
’’ (c
on
ti
nu
ed
)
Open Science: One Term, Five Schools of Thought 41
T
ab
le
7
(c
on
ti
nu
ed
)
A
ut
ho
r
(Y
ea
r)
T
it
le
C
on
te
nt
B
jö
rn
eb
or
n
&
In
gw
er
so
n
(2
00
1)
Jo
ur
na
l
ar
ti
cl
e
P
er
sp
ec
ti
ve
s
of
w
eb
om
et
ri
cs
T
he
la
ck
of
m
et
ad
at
a
at
ta
ch
ed
to
w
eb
do
cu
m
en
ts
an
d
li
nk
s
an
d
th
e
la
ck
of
se
ar
ch
en
gi
ne
s
ex
pl
oi
ti
ng
m
et
ad
at
a
af
fe
ct
s
fil
te
ri
ng
op
tio
ns
,
an
d
th
us
kn
ow
le
dg
e
di
sc
ov
er
y
op
ti
on
s,
w
he
re
as
fie
ld
co
de
s
in
tr
ad
iti
on
al
da
ta
ba
se
s
su
pp
or
t
K
D
D
(K
no
w
le
dg
e
D
is
co
ve
ry
in
D
at
ab
as
es
)
‘‘A
s
st
at
ed
ab
ov
e,
th
e
fe
as
ib
il
it
y
of
us
in
g
bi
bl
io
m
et
ri
c
m
et
ho
ds
on
th
e
W
eb
is
hi
gh
ly
af
fe
ct
ed
by
th
e
di
st
ri
bu
te
d,
di
ve
rs
e
an
d
dy
na
m
ic
al
na
tu
re
of
th
e
W
eb
an
d
by
th
e
de
fic
ie
nc
ie
s
of
se
ar
ch
en
gi
ne
s.
T
ha
t
is
th
e
re
as
on
th
at
so
fa
r
th
e
W
eb
Im
pa
ct
F
ac
to
r
in
ve
st
ig
at
io
ns
ba
se
d
on
se
co
nd
ar
y
da
ta
fr
om
se
ar
ch
en
gi
ne
s
ca
nn
ot
be
ca
rr
ie
d
ou
t.’
’
(p
.
78
)
42 B. Fecher and S. Friesike
Yeong and Abdullah (2012) state that altmetrics differ from webometrics which
are, as the authors argue, relatively slow, unstructured, and closed. Altmetrics
instead rely upon a wider set of measures that includes tweets, blogs, discussions,
and bookmarks (e.g. mendeley.com). Altmetrics measure different forms of sig-
nificance and usage patterns by looking not just at the end publication, but also the
process of research and collaboration (ibid., p. 2). Unfortunately, the authors do
not further outline how a scientific process instead of a product could be evaluated.
A possibility could be to measure the impact of emerging formats of research
documentation in the social web (e.g. scientific blogs) or datasets (e.g. Open Data).
As a possible basis for altmetrics, Priem et al. (2011, p. 1) mention web pages,
blogs, and downloads, but also social media like Twitter, or social reference
managers like CiteULike, Mendeley, and Zotero. As a result of a case study with
214 articles, they present the two open-source online tools, CitedIn and Total
Impact, as potential alternatives to measure scientific impact as they are based on a
meaningful amount of data from more diverse academic publications. At the same
time, they emphasize that there is still a need for research regarding the compa-
rability of altmetrics, which is difficult due to the high dimensionality of altmetrics
data.
While many authors already recognize the need for new metrics in the digital
age and a more structured and rapid alternative to webometrics (Yeong and
Abdullah 2012), research on this matter is still in its infancy. There is scarcely any
research on the comparability of altmetrics and virtually no research on their
potential manipulations and network effects. Furthermore, altmetrics are not yet
broadly applied in the scientific community, raising the question as to what hinders
their broad implementation. A possible reason is the tight coupling of the existing
journal system and its essential functions of archiving, registration, dissemination,
and certification of scholarly knowledge (Priem and Hemminger 2012). All the
more, it appears that future research should also focus on the overall process of
science, its transformative powers, and, likewise, constraints.
Discussion
This chapter showed that ‘‘Open Science’’ is an umbrella term that encompasses
almost any dispute about the future of knowledge creation and dissemination, a
term that evokes quite different understandings depending on the viewpoint of its
respective advocates and leads to many quarrels under the same flag—yet with
varying inducements and targets. Even though the chapter implies a certain lack of
conceptual clarity in the term Open Science, we do not promote a precisely defined
concept. On the contrary, we assume that doing so could prevent fertile discussions
from the very beginning. We therefore aimed at offering an overview of the
leading discourses by suggesting five (more or less) distinct schools of thought,
Open Science: One Term, Five Schools of Thought 43
and their core aims and argumentations. We suggest that this classification can be a
starting point for structuring the overall discourse and locating its common
catchphrases and argumentations. In this respect the mindmap graphic below
attempts to arrange the most common keywords in the Open Science discourse
according to the aforegoing described schools.
Although Open Science covers in the broadest sense anything about the future
of knowledge creation and dissemination, not necessarily all developments
described in this chapter are novel. In fact, many demands and argumentations
existed long before the dawn of the Internet and the digital age. Some would even
argue that science is per definition open, since the aim of research is, after all, to
publish its results, and as such to make knowledge public. Nonetheless, science
certainly has experienced a new dynamic in the light of modern communication
technology. Collaborative forms of research, the increasing number of co-authored
scientific articles, new publication formats in the social web, the wide range of
online research tools, and the emergence of Open Access journals all bear witness
to what is entitled in this book ‘the dawn of a new era’.
Science is doubtlessly faced with enormous challenges in the coming years.
New approaches to knowledge creation and dissemination go hand in hand with
profound systemic changes (e.g. when it comes to scientific impact), changes in
the daily practice of researchers (e.g. when it comes to new tools and methods),
changes in the publishing industry (e.g. when it comes to coping with alternative
publication formats), and many more. In this regard, this chapter should not only
provide insight into the wide range of developments in the different Open Science
schools, but also point towards the complexity of the change, the intertwinedness
of the developments, and thus the necessity for holistic approaches in research on
the future of research. For example: How could one argue for extensive practicing
of Open Data if there is no remuneration for those who do it? How could one
expect a researcher to work collaboratively online if platforms are too complicated
to use? Why should a researcher invest time and effort in writing a blog if it has no
impact on his or her reputation?
The entirety of the outlined developments in this chapter marks a profound
change of the scientific environment. Yet even if the most prominent accompa-
niments of this change (be it Open Access, Open Data, citizen science, or col-
laborative research) are possibly overdue for a knowledge industry in the digital
age and welcomed by most people who work in it, they still depend upon com-
prehensive implementation. They depend upon elaborate research policies, con-
venient research tools, and, not least, the participation of the researchers
themselves. In many instances Open Science appears to be somewhat like the
proverbial electric car—an indeed sensible but expenseful thing which would do
better to be parked in the neighbor’s garage; an idea everybody agrees upon but
urges others to take the first step for.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
44 B. Fecher and S. Friesike
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Open Science: One Term, Five Schools of Thought 47
Excellence by Nonsense: The Competition
for Publications in Modern Science
Mathias Binswanger
Most scientific publications are utterly redundant, mere
quantitative ‘productivity’.
—Gerhard Fröhlich
Abstract In this chapter, Binswanger (a critic of the current scientific process)
explains how artificially staged competitions affect science and how they result in
nonsense. An economist himself, Binswanger provides examples from his field and
shows how impact factors and publication pressure reduce the quality of scientific
publications. Some might know his work and arguments from his book ‘Sinnlose
Wettbewerbe’.
In Search of Excellence
Since the Age of Enlightenment, science has mostly taken place at universities and
their respective institutes, where for a long time the ideal of uniting research and
teaching was upheld. Since their re-establishment by Humboldt in 1810, German
universities have been, also in terms of academic work, largely independent and
the principle of academic freedom was applied.
The government merely determined that amount of money that was paid to
universities and set the legal framework for science and teaching. In terms of
research, the government did not impose specific research policies-with the
exception of some inglorious episodes (e.g. the Nazi regime). Universities were
trusted to know best what kind of research they were doing.
Generally, it was accepted not to tell a country’s best academics what they
should be interested in and what research they should be doing (Schatz 2001;
Kohler 2007). Therefore, the academic practice of professors and other scientists
M. Binswanger (&)
University of Applied Sciences of Northwestern Switzerland, Olten, Switzerland
e-mail: mathias.binswanger@fhnw.ch
M. Binswanger
University of St. Gallen, St. Gallen, Switzerland
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_3,  The Author(s) 2014
49
was hardly documented and assessed systematically, as it was assumed that aca-
demics would strive for excellence without having to be forced to do so.
Sometimes this was right and sometimes it was wrong. Huge differences in
quality between individual scientists were the result. Scientific geniuses and lame
ducks jointly populated universities, whereby even during the scientists’ lifetimes
it was not always discernible who was the lame duck and who the genius.
The extraordinary is the rare result of average science and only broad quality,
growing out from mediocrity, brings the great achievement at the end says Jürgen
Mittelstrass, philosopher of science (2007). Still in 1945, the then president of
Harvard University wrote in a letter addressed to the New York Times (August,
13th, 1945): There is only one method to guarantee progress in science. One has to
find geniuses, support them and let them carry out their work independently.
Meanwhile, the government has given up its reservations towards universities
and formerly proud bastions of independent thinking have turned into servants of
governmental programs and initiatives. Lenin’s doctrine applies once again: trust
is good, control is better.
To ensure the efficient use of scarce funds, the government forces universities
and professors, together with their academic staff, to permanently take part in
artificially staged competitions. This is happening on two fronts: universities have
to prove themselves by competing, both in terms of education and scientific
research, in order to stay ahead in the rankings. Yet how did this development
occur? Why did successful and independent universities forget about their noble
purpose of increasing knowledge and instead degenerated into ‘‘publication fac-
tories’’ and ‘‘project mills’’ which are only interested in their rankings?
To understand this, we have to take a closer look at the development of uni-
versities since the 1960s. Until then, people with a tertiary education made up a
relatively small fraction of the population. Universities were relatively elitist
institutions which remained out of reach for the majority of working class kids.
Since the 1960s however, increasing access to tertiary education occurred, for
which the term ‘mass higher education’ (Trow 1997) was coined.
From 1950, first-year student rates increased from an average of 5 % in the
industrialized countries to up to 50 % at the beginning of the 21st century
(Switzerland, with its 20 %, is an exception). Universities and politics, however,
were not prepared to deal with this enormous increase. It was believed to be
possible to carry on with 1,000 students in the same way as has been done with 50
students, by just increasing the number of universities and professors and by
putting more money into administration.
The mass education at universities made a farce of Humboldt’s old idea of unity
of research and education. This had consequences for both education and research.
There were more and more students and also more and more researchers who were
employed at universities (and later on at universities of applied sciences), but most
of them no longer had any time for research. In Germany, the number of students
also grew disproportionately faster than the number of professors due to the very
generous government support of students through BAFÖG (Federal Education and
Trainings Assistance Act). Therefore, one professor had to supervise more and
50 M. Binswanger
more students and postgraduates and there was no more time to seriously deal with
them.
Dissertations became mass products, the majority of which added little or
nothing to scientific advancement. An environment emerged that was neither
stimulating for professors, nor for their assistants and doctoral students, which
logically led to increasing mediocrity. German universities in particular have often
been criticized along the following lines: studies last too long, the dropout rates are
too high, the curricula are obsolete, and research performance is only average and
rarely of value and relevance for industrial innovations.
A second phenomenon which did a lot of harm to the European universities,
was the lasting glorification of the American higher education system. Many
politicians, but also scientists themselves, see this system as a permanent source of
excellence and success without—as US scientist Trow (1997) writes—getting the
general picture of the American higher education system. Attention is directed
exclusively at Harvard, Princeton, Yale, MIT, and other Ivy-League universities,
which make up only a small percentage of the university landscape in the US. In
this euphoria, it is intentionally overlooked that the majority of colleges and
universities displays an intellectually modest standard and hardly contributes to
academic progress. Much of what we celebrate as ‘globalization’ and ‘adjustment
to international standards’ is in reality the adjustment to US-American provin-
cialism (Fröhlich 2006).
In Europe, the idea became fashionable that imitating top US universities would
magically create a new academic elite. Like small boys, all universities wanted to
be the greatest, and politics started propagating sponsorship of Ivy-League uni-
versities, elite institutions, and elite scientists. Germany started an Excellence
Initiative in order to boost its international competitiveness. Switzerland aimed to
be one of the top 5 countries for innovation by supporting excellence, and the
European Union, with the so-called Lisbon-strategy of 2000, hoped to turn the EU
into the most dynamic knowledge-based economy by 2010.
Cutting-edge universities, top-institutes, and research clusters shot up every-
where, and everyone wanted to be even more excellent than their already-excellent
competitors. Amongst this childish race for excellence, it was overlooked that not
all can be more excellent than the rest. This fallacy of composition applies here as
well. Instead, the term ‘excellence’ became a meaningless catchword. Philosopher
Mittelstrass (2007) writes:
Until now, no one took offence at the labeling of excellent cuisine, excellent performance,
excellent academics or excellent scientists. […] In the case of science this changed since
science policy has occupied this term and talks about excellent research, excellent research
establishments, clusters of excellence and Excellence Initiatives, in endless and almost
unbearable repetitions.
Yet how do we actually know what excellence is and where it is worthwhile to
foster a scientific elite? In reality, no one actually knows, least of all the politicians
who enthusiastically launch such excellence initiatives. This is where the idea of
artificially staged competition comes in. It is assumed that these competitions will
Excellence by Nonsense: The Competition for Publications in Modern Science 51
automatically make the best rise to the top—without the need to care about neither
content nor purpose of research. We may call this ‘contest illusion’. This contest
illusion was applied to science in England for the first time under the Thatcher
government in the 1980s. Afterwards it was quickly copied in other countries. The
Thatcher government, inspired by its belief in markets and competition, would
have loved to privatize all institutions engaged in academic activities and to let
markets decide which kind of science was needed, and which was not. However,
this proved to be impossible. Basic research constitutes, for the most part, a
common good which cannot be sold for profit at a market. Privatization would
therefore completely wipe out basic research. Thus, artificially staged competitions
were created, which were then termed markets (internal markets, pseudo-markets),
even though this was false labeling.
Connected to the euphoria about markets and competition, there was also a
deep mistrust towards independent research taking place within ‘‘ivory towers’’,
the purpose of which politicians often do not understand. What does the search for
knowledge bring apart from high costs? On these grounds, the former British
minister of education Charles Clarke characterized ‘‘the medieval search for truth’’
as obsolete and unnecessary.1 Modern universities should produce applicable
knowledge, which can be transformed into growth of the gross domestic product,
and additionally make it more sustainable. Universities should think ‘‘entrepre-
neurial’’ and adjust to economic needs (see Maasen and Weingart 2008). For this
reason, governments in many countries, particularly in the EU, started to organize
gigantic research programs. Instead of making research funds directly available to
universities, they are now in competition with each other, so that only the ‘‘best’’
get a chance. This should ensure that above all practice-oriented and applicable
knowledge is created and government funds are not wasted (e.g. for ‘‘unneces-
sary’’ basic research). Hence universities are forced to construct illusionary worlds
of utility and pretend that all research serves an immediate purpose (Körner 2007).
How can you impress the research commissions responsible for the distribution
of funds? This is mainly achieved by increasing measurable output such as pub-
lications, projects funded by third-party funds, and networks with other institutes
and universities. In this way, ‘‘excellence’’ is demonstrated, in turn leading to
easier access to further government research funds. Competitiveness has therefore
become a priority for universities and their main goal is to perform as highly as
possible in measurable indicators which play an important role in these artificially
staged competitions. The underlying belief is that our knowledge increases pro-
portionally to the amount of scientific projects, publications, and intensity of
networking between research institutions, which in turn is supposed to lead to
more progress and wealth. This naïve ton ideology is widespread among politi-
cians and bureaucrats.
1 BBC News. Clarke questions study as ‘adornment’: http://news.bbc.co.uk/2/hi/uk_news/
education/3014423.stm
52 M. Binswanger
The modern university is only marginally concerned with gaining knowledge,
even though the public from time to time is assured that this is still the major goal.
Today’s universities are, on the one hand, fundraising institutions, determined to
receive as many research funds as possible. On the other hand, they are publication
factories, trying to maximize their publication output. Hence, the ideal professor is
a mixture of fundraiser, project manager, and mass publisher (either directly as
author, or as co-author in publications written by employees of the institution),
whose main concern is measurable contribution to scientific excellence, rather than
increasing our knowledge. Moreover, in order to make professors deliver their
contribution to excellence, faculty managers have been recruited for each
department in addition to traditional deans. Nowadays, the principal is sort of a
CEO who is supposed to implement new strategies for achieving more and more
excellence. Research becomes a means in the battle for ‘‘market shares’’ of uni-
versities and research institutions (Münch 2009).
Universities which on the surface expose themselves as great temples of sci-
entific excellence, are forced to participate in project- and publication-olympics,
where instead of medals, winners are rewarded with the elite or excellence status,
exemption from teaching duties, and sometimes also with higher salaries. This is
how it goes, even though many of the projects and publications do not have the
slightest importance for the rest of the population outside the academic system.
Two artificially staged competitions in particular incentivize the production of
nonsense: the competition for the highest amount of publications and the com-
petition for the highest amount of research funding. The resulting indicators for
publications and third-party funds play a central role in today’s research rankings,
such as, for example the German CHE Research Ranking of German universities
(see Berghoff et al. 2009).
The competition for the highest amount of publications will be analyzed below
in more detail. On the basis of the competition for publications, it can be nicely
demonstrated how perverse incentives emerge and what consequences this entails
not only for research, but also generally for society and the economy.
The Competition for Publications in Academic Journals:
The Peer-Review Process
In almost every academic discipline, publications are the most important and often
the only measurable output. Indeed, in some natural sciences and in engineering
inventions or patents also play a certain role, yet this more concerns applied
science. Basic research, however, always manifests itself in publications. What is
more obvious than measuring a scientist or institute’s output or productivity on the
basis of publications? For is it not the case that many publications are the result of
a lot of research, consequently increasing our relevant knowledge? Should not
every scientist be driven to publish as much as possible in order to achieve
Excellence by Nonsense: The Competition for Publications in Modern Science 53
maximum ‘‘scientific productivity’’? Someone who has just a little knowledge of
universities and academic life can immediately answers these questions with an
overwhelming ‘‘no’’. Indeed, more publications increase the amount of printed
sheets of paper, but this number does not say any more about the significance of a
scientist or institute’s research activity than the number of notes played says about
the quality of a piece of music.
Of course, measurements of scientific output are not as primitive as counting
every written page of scientific content as scientific activity. Relevant publications
are in professional journals, where submitted work is subjected to a ‘‘rigorous’’ and
‘‘objective’’ selection method: the so-called ‘‘peer-review process’’. This should
ensure that only ‘‘qualitatively superior’’ work is published, which then is regarded
as a ‘‘real scientific publication’’. Thus, strictly speaking, the aim of the artificially
staged competitions amongst scientists is to publish as many articles as possible in
peer-reviewed scientific journals.
However, among scientific journals strict hierarchies also exist which are
supposed to represent the average ‘‘quality’’ of the accepted papers. In almost
every scientific discipline there are a few awe-inspiring top-journals (A-journals),
and then there are various groups of less highly respected journals (B- and C-
journals), where it is easier to place an article, but where the publication does not
have the same significance as an A-journal article. Publishing one’s work in an A-
journal is therefore the most important and often also the only aim of modern
scientists, thus allowing them to ascend to the ‘‘Champions’ League’’ of their
discipline. Belonging to this illustrious club makes it easier to publish further
articles in A-journals, to secure more research funds, to conduct even more
expensive experiments, and, therefore, to become even more excellent. The ‘‘Taste
for Science’’, described by Merton (1973), which is based on intrinsic motivation
and supposed to guide scientists was replaced by the extrinsically motivated
‘‘Taste for Publications.’’
But what is actually meant by the peer-review process? When a scientist wants
to publish a paper in an accepted scientific journal, the paper has to be submitted to
the journal’s editors, who have established themselves as champions within their
disciplines. These editors usually do not have the time to deal with the day-to-day
business of ‘‘their journal’’ and thus there is a less accomplished Managing Editor,
who is responsible for administrative tasks and receives manuscripts from the
publishing-hungry scientists and then puts the peer-review process in motion. The
Managing Editor gives the submitted manuscripts to one or several professors or
other distinguished scientists (the so-called peers) who ideally work in the same
field as the author and therefore should be able to assess the work’s quality.
To ensure the ‘‘objectivity’’ of the expert judgments, the assessment is usually
performed as a double-blind procedure. This means that the reviewers do not know
who are the authors of the article to be reviewed, and the authors are not told by
whom their paper is assessed. At the end of the peer review process, the reviewers
inform the editor in writing whether they plead for acceptance (very rare), revi-
sion, or rejection (most common) of the article submitted to the journal in ques-
tion. Quite a few top journals pride themselves on high rejection rates, supposedly
54 M. Binswanger
reflecting the high quality of these journals (Fröhlich 2007). For such journals the
rejection rates amount to approximately 95 %, which encourages the reviewers to
reject manuscripts in almost all cases in order to defend this important ‘‘quality
measure’’. Solely manuscripts that find favor with their reviewers get published,
because although the final decision concerning publication rests with the editors,
they generally follow the expert recommendations.
The peer-review process is thus a kind of insider procedure (also known as clan
control, Ouchi 1980), which is not transparent for scientists outside the established
circle of champions. The already-established scientists of a discipline evaluate
each other, especially newcomers, and decide what is worthy to be published.
Although the claim is made that scientific publications ultimately serve the general
public, and thereby also serve people who are not active in research, the general
public, who is actually supposed to stand behind the demand for scientific
achievement, has no influence upon the publication process. The peers decide on
behalf of the rest of mankind, since the public can hardly assess the scientific
quality of a work.2 Outside of the academic system, most people neither know
what modern research is about, nor how to interpret the results and their potential
importance to mankind. Although scientists often also do not know the latter, they
are—in contrast to the layman—educated to conceal this lack of knowledge
behind important sounding scientific jargon and formal models. In this way, even
banalities and absurdities can be represented as A-journal worthy scientific
excellence, a process laymen and politicians alike are not aware of. They are kept
in the blissful belief that more competition in scientific publication leads to ever-
increasing top performance and excellence.
Considering the development of the number of scientific publications, it seems
that scientists are actually accomplishing more and more. Worldwide, the number
of scientific articles, according to a count conducted by the Centre for Science and
Technology Studies at the University of Leiden (SBF 2007) has increased enor-
mously. The number of scientific publications in professional journals worldwide
increased from approximately 686,000 in 1990 to about 1,260,000 in 2006, which
corresponds to an increase of 84 %. The annual growth rate calculated on this
basis was more than 5 %. The number of scientific publications grows faster than
the global economy and significantly faster than the production of goods and
services in industrial countries, from where the largest number of publications
originates (OECD 2008).
By far the largest share of world production of scientific articles comes from the
U.S. (25 %), followed by Britain with 6.9 %. Germany produces 6.3 %,
Switzerland 1.5 %, and Austria 0.7 % (SBF 2007). However, calculating pub-
lished articles per capita, Switzerland becomes the world’s leading country,
2 In the language of economics, this means that the information asymmetry between scientists
and lay people is so large that ‘‘monitoring’’ by outsiders is no longer possible (Partha and David
1994, p. 505).
Excellence by Nonsense: The Competition for Publications in Modern Science 55
because there are 2.5 published scientific articles per 1,000 inhabitants, while in
the U.S. there are 1.2 articles, and only one article in Germany (SBF 2007).3 The
same picture emerges if one applies the number of publications to the number of
researchers. In this case, in Switzerland for each 1,000 researchers there are 725
publications while there are 295 in Germany and 240 in the United States. Thus, in
no other country in the world are more research publications squeezed out of the
average researcher than in Switzerland.
Once we begin to examine the background of this increasing flood of publi-
cations it quickly loses its appeal. This is to a large extent inherent in the peer-
review process itself. This supposedly objective system for assessing the quality of
articles in reality rather resembles a random process for many authors (Osterloh
and Frey 2008). A critical investigation reveals a number of facts that funda-
mentally question the peer-review process as a quality assurance instrument (cf.
Atkinson 2001; Osterloh and Frey 2008; Starbuck 2006). It generally appears that
expert judgments are highly subjective, since the consensus of several expert
judgments is usually low. One reason is that by no means do all peers, who are
mostly preoccupied with their own publications, actually read, let alone under-
stand, the articles to be evaluated. Time is far too short for this and usually it is not
even worth it because there are much more interesting things to do. Hence, time
after time reviewers pass on the articles to their assistants who, in the manner of
their boss, draft the actual review as ghostwriters (Frey et al. 2009). No wonder
that under such conditions important scientific contributions at hindsight are fre-
quently rejected. Top journals repeatedly rejected articles that later on turned out
to be scientific breakthroughs and even won the Nobel Prize. Conversely, however,
plagiarism, fraud and deception are hardly ever discovered in the peer review
process (Fröhlich 2007). In addition, unsurprisingly, reviewers assess those articles
that are in accordance with their own work more favorably, and vice versa, they
reject articles that contradict them (Lawrence 2003).
Due to the just-described peer-review process, the competition for publication
in scientific journals results in a number of perverse incentives. To please the
reviewers, a potential author undertakes everything conceivably possible. To
describe this behavior Frey (2003) rightly coined the term ‘‘academic prostitu-
tion’’, which—in contrast to traditional prostitution—does not spring from natural
demand, but is induced by artificially staged competition (cf. Giusta et al. 2007). In
particular, the following perverse effects can be observed:
Modes of perverse behavior caused by the peer-review process:
3 Nevertheless, the Neue Zürcher Zeitung already worried in an article from 2004 that the growth
of publications in Switzerland compared to the average of OECD countries was below average.
This thinking reveals once again a naive ton ideology, in which more scientific output is equated
with more well-being.
56 M. Binswanger
• Strategic citing and praising4
When submitting an article to a journal, the peer-review process induces authors
to think about possible reviewers who have already published articles dealing
with the same or similar topics. To flatter the reviewers, the author will pref-
erably quote all of them or praise their work (as a seminal contribution, inge-
nious idea, etc.); An additional citation is useful for the potential reviewer
because in turn it is improving his own standing as a scientist. Furthermore,
editors often consult the bibliography at the end of an article while looking for
possible reviewers, which makes strategic citing even more attractive.
Conversely, an author will avoid criticizing the work of possible reviewers, as
this is a sure road to rejection. Accordingly, this attitude prevents the criticism
and questioning of existing approaches. Instead, the replication of established
knowledge gets promoted through elaboration upon preexisting approaches
through further model variations or additional empirical investigations.
• No deviation from established theories
In any scientific discipline there are some eminent authorities who dominate
their field and who often at the same time are the editors of top journals. This in
turn allows them to prevent the appearance of approaches or theories that
question their own research. Usually this is not difficult, since most authors try
to adapt to the prevailing mainstream theories in their own interest. The majority
of the authors simply wants to publish articles in top journals, and this makes
them flexible in terms of content. They present traditional or fashionable
approaches that evoke little protest (Osterloh and Frey 2008). In this way, some
disciplines (e.g. economics) have degenerated into a kind of theology where
heresy is no longer tolerated in established journals. Heresy takes place in only a
few marginal journals specializing in divergent theories, but these publications
rarely contribute to the reputation of a scientist. As Gerhard Fröhlich aptly
writes: ‘‘In science as in the Catholic Church similar conditions prevail: cen-
sorship, opportunism and adaptation to the mainstream of research. As a result, a
highly stylized technocratic rating- and hierarchy-system develops, which hin-
ders real scientific progress.’’
In empirical studies, the adherence to established theories can also be discovered
by the results of statistical tests. To falsify an existing theory is linked to low
chances of publication and thus there is an incentive to only publish successful
tests and to conceal negative results (Osterloh and Frey 2008).
• Form is more important than content
Since presenting original content usually lowers the chances of publication,
novelty has shifted to the form how content is presented. Simple ideas are blown
up into highly complex formal models which demonstrate the technical and
mathematical expertise of the authors and signal importance to the reader. In
many cases, the reviewers are not able to evaluate these models because they
4 In the meantime, there are now so-called guides along the lines of ‘‘How to publish
successfully?’’, which provide strategic advice to young scientists in the manner described herein.
Excellence by Nonsense: The Competition for Publications in Modern Science 57
have neither the time nor the inclination to deal with these models over several
days. Since they cannot admit this, in a case of doubt formal brilliance is
assessed positively because it usually supports prevailing theories. It helps to
immunize the prevailing theories against criticism from outside, and all col-
leagues who are not working within the same research field just need to believe
what was ‘‘proven to be right’’ in the existing model or experiment.
With this formalization, sciences increasingly move away from reality as false
precision is more important than actual relevance. The biologist Körner writes
(2007, p. 171): The more precise the statement [of a model], the less it usually
reflects the scale of the real conditions which are of interest to or available for
the general public and which leads to scientific progress.
The predominance of form over content (let us call this ‘crowding-out’ of form
by content) does also attract other people to science. The old type of an often
highly unconventional scientist who is motivated by intrinsic motivation is
increasingly being replaced by formally gifted, streamlined men and women,5
who in spite of their formal brilliance have hardly anything important to say.
• Undermining of anonymity by expert networks
In theory, the peer-review process should work in such a way that publication
opportunities are the same for all authors. Both the anonymity of the authors and
the reviewers are guaranteed thanks to the double-blind principle. For many
established scientists at top universities, ‘‘real’’ competition under these con-
ditions would be a nuisance. After all, why did one work hard for a lifetime only
to be subject to the same conditions as any newcomer? The critical debate on the
peer-reviewed process discussed in the journal Nature in 2007, however, clearly
showed that in practice the anonymity of the process for established scientists is
rare. They know each other and know in advance which papers by colleagues or
by scientists associated with them will be submitted. In expert networks
maintained in research seminars, new papers are presented to each other, which
successfully undermines the anonymity of the peer-review process.
This fact can clearly be seen when looking at the origin of scientists who publish
in top journals. For example, a study of the top five journals in economics (Frey
et al. 2009, p. 153) shows that of the 275 articles published in 2007, 43 %
originated from scientists working at only a few top American universities
(Harvard, Yale, Princeton, MIT, Chicago, Berkeley, Stanford). The professors
of these universities are basically set as authors and the rest must then go
through an arduous competition for the few remaining publication slots. What
George Orwell noted in his book ‘‘Animal Farm’’ can be paraphrased: All
authors are equal but some are more equal than others.
5 Just look at today’s photos of highly praised young talents in sciences. In this case, images
often say more than 1,000 words.
58 M. Binswanger
• Revenge of frustrated experts
Ultimately, the entire publication process is a tedious and humiliating experi-
ence for many researchers. Constantly, submitted papers are rejected, and often
for reasons that are not comprehensible. One has to be pleased if the reviewers
have the grace to make recommendations for a revision of the article. In this
case, in order to finally get it published, one needs to (or in fact ‘‘must’’) change
the article according to the wishes of the reviewers. This is hardly a pleasant
task as it is not uncommon that a revision is done ‘‘contre coeur.’’ Therefore it is
no wonder that many reviewers are at the same time frustrated authors, who can
now pay back to innocent third authors the humiliation they had gone through
themselves (Frey et al. 2009, p. 153): They should not have it easier than us, and
they should not think that getting a publication is so easy. is the tenor. For this
reason, articles are often rejected out of personal grudges, and the supposedly
objective competition for publication becomes a subjective statement. This is
particularly the case when it comes to approaches that are hated by the reviewers
(in reality it is often the professor behind the publication who is hated) and they
will not forgo the chance to make the life of this author a little bit more
miserable.
The perverse incentives created by the peer-review process ensure that the
steadily increasing number of published articles in scientific journals often does
not lead to new or original insights and, therefore, many new ideas do not show up
in established journals. They can rather be found in books and working papers,
where there is no pseudo-quality control which hinders innovative ideas. Although
the peer-review process prevents the publication of obvious platitudes and non-
sense on the one hand, on the other hand it promotes the publication of formally
and verbally dressed-up nonsense. The increasing irrelevance of content is the
result of artificially staged competition for publication in professional journals.
The next section deals with the use of publications and citations as indicators for
the assessment of individual scientists and scientific institutions, and explains why
we have more and more irrelevant publications.
The Competition for Top-Rankings by Maximising
Publications and Citations
Despite the great difficulties involved in publishing articles in professional jour-
nals, the number of publications is constantly growing because more and more
journals exist simultaneously. These publications are important for the rankings of
individual scientists as well as institutions and universities. Furthermore, if young
scientists apply for a professorship, the list of publications is usually the most
important criterion in the decision process of who will get the post. No wonder that
scientists do everything to publish as much as possible despite the painstaking
peer-review process. The question as to what to publish, where, and with whom
Excellence by Nonsense: The Competition for Publications in Modern Science 59
has become essential to the modern scientist. Publication problems cause sleepless
nights and the acceptance of an article in a top journal is the greatest thing that can
happen in the life of a modern scientist. This is the case, although most of these
publications are not of the slightest importance for anybody outside of the aca-
demic system. In most articles the opposite of what has been ‘‘proved’’ could also
be ‘‘proved’’ and it would not change the course of the world at all.
How does the number of publications actually get into the evaluation and
ranking process of scientists and their institutions? At first glance, this seems quite
simple: one simply counts all the articles published by a scientist in scientific
journals (or counts number of pages) and then gets to the relevant number of the
scientist’s publication output. However, there is a problem. As we have already
seen, the journals differ dramatically in terms of their scientific reputation, and an
article in an A journal is worth much more than an article in a B or C journal. So
we must somehow take into account the varying quality of the journals in order to
achieve a ‘‘fairly’’ assessed publication output. To this end, an entirely new science
has developed, called scientometrics or bibliometrics, which deals with nothing
else than measuring and comparing the publication output of scientists. This sci-
ence has by now obtained its own professors and its own journals, and conse-
quently the measurements are also becoming more complex and less transparent,
which then in turn justifies even more bibliometric research.
The most important tool of bibliometric research is citation analysis, which has
the purpose of determining the quantity of citations of the specific journal article to
be analyzed. Based on this, the effect of scientific articles can be ascertained. The
rationale behind this is simple: whoever is much quoted is read often, and what is
often read must be of high quality. Hence, the quantity of citations can be used as a
‘‘quality indicator’’ of an article. This quality indicator can then be used to weigh
up the articles published in various magazines. Thus, we obtain an ‘‘objective’’
number for a scientist’s publication output which then can be easily compared and
used for rankings. This is also done on a large scale and university administrators
seem to put more energy and effort into these comparisons than into actual
research.
The International Joint Committee on Quantitative Assessment of Research,
consisting of mathematicians and statisticians, talks in a report dated 2008 (Adler
et al. 2008, p. 3) about a Culture of Numbers and sums up the assessment of the
situation as follows:
The drive towards more transparency and accountability in the academic world has created
a ‘culture of numbers’ in which institutions and individuals believe that fair decisions can
be reached by algorithmic evaluation of some statistical data; unable to measure quality
(the ultimate goal), decision-makers replace quality by numbers that they can measure.
(…) But this faith in the accuracy, independence, and efficacy of metrics is misplaced.
This is a warning coming from experts, which we should take seriously. Also,
the German Research Foundation (DFG 2002) warned a few years ago about
believing too much in quantitative measures (translated by the author):
60 M. Binswanger
Quantitative indicators are comfortable, they seem objective and are (…) surrounded by an
aura of hardly disputable authority. Nevertheless, the naive trust in numbers is a fatal
misbelief which each faculty (…) should counteract.
However, the similarities between various publication rankings are low because
different quality measurements lead to very different results (see e.g., Frey and
Rost 2010; Maasen and Weingart 2008). However, ‘‘clever’’ researchers have
found a solution even to that problem (see Franke and Schreier 2008). If rankings
do not lead to clear results, we should simply calculate a weighted average from
the different rankings. In other words, we construct a meta-ranking out of all
existing rankings and again we have a clear result. And if in future several meta-
rankings should exist, then one can also construct a meta-meta-ranking! Academic
excellence at its best!
A measure which has become particularly popular among number-fetishists is
the so-called ‘‘Impact Factor’’. This factor is widely used nowadays in order to
calculate the ‘‘quality’’ of journals. The Impact Factor of a particular journal is a
quotient where the numerator is the number of citations of articles published in
that particular journal during previous years (mostly over the last two years) in a
series of selected journals in a given year. The denominator comprises of the total
number of articles published in that journal within the same period of time. For
example, if a journal has an Impact Factor of 1.5 in 2010, this tells us that papers
published in this journal in 2008 and 2009 were cited 1.5 times on average in the
selected journals in 2010.
The Impact Factors used in science today are calculated annually by the
American company Thomson Scientific; these then get published in the Journal
Citation Reports. Thomson Scientific has a de facto monopoly for the calculation
of impact factors, although the exact calculation is not revealed, which has been
questioned repeatedly (see, e.g. Rossner et al. 2007). The sciences have allowed
Thomson Scientific to dominate them. (Winiwarter and Luhmann 2009, p. 1). This
is even more absurd if, on the one hand, the blessing of competition keeps being
praised, but on the other hand, a monopoly for Thomson Scientific is allowed,
which enables Thomson Scientific to sell its secretly fabricated Impact Factors to
academic institutions at a high price, although in many sciences less than 50 % of
today’s existing scientific journals are included in the calculation.
A concrete example will show how numbers are fabricated mindlessly. The
following proposal is from a 2005 research paper published by the Thurgau
Institute of Economics at the University of Konstanz. The author, Miriam Hein
(who studied economics) naively propagates a method for measuring quality
without being aware of the perverse incentives that this would create. In the
introduction we read (Hein 2005, p. 3):
An intended increase in research performance can probably be induced only by the use of
incentive-compatible management tools. Research units and individual researchers who
undertake high quality research must [an imperative!] be rewarded, and those who are less
successful, should be sanctioned. It is therefore important to identify good and bad
research. Well-designed ranking tools can serve this purpose.
Excellence by Nonsense: The Competition for Publications in Modern Science 61
The above-quoted section talks about ‘‘high quality research’’ within the same
article and a few pages later a proposal for the measurement and calculation of the
quality of research follows. The average ‘‘quality research’’ (DQ) of an institution
shall be determined according to the following formula:
DQ ¼ FX
FXS
¼
P
i
P
k
pkiwk
nkiP
i
P
k
pki
nki
Pki stands for the number of pages in publication k of scientist i, n denotes the
number of authors of the publication k, and wk is a quality factor for the article k,
which is typically the impact factor of the journal, in which the article was pub-
lished. Therefore, the numerator shows the quality-weighted research output (FX)
and the denominator simply consists of the number of published pages (FXS). The
content of an article, on the other hand, plays no role! The important thing is how
long the article is and where it got published. Nevertheless, the just described
‘‘quality measure’’ is seriously praised as progress in quality measurement. The
scientist is treated like a screw salesman: The more screws he has sold, the better
he is. This attitude is already obvious from the term ‘‘research productivity’’,
which according to Hein (2005, p. 24) is an absolutely central unit of measure in
research management. Thus, pages published in scientific journals become ends in
themselves.
The competition for top rankings established by the requirement for as many
publications and citations as possible and the already perverse incentives due to
the peer-review process have induced a great deal of perverse behavior among
scientists. In particular, the following trends can be observed:
• Salami tactics
Knowing that the ultimate goal is to maximize research output, researchers are
trying to make as much out of very little and apply so-called ‘‘salami tactics’’.
New ideas or records are cut as thin as salami slices in order to maximize
number of publications (Weingart 2005). Minor ideas are presented in complex
models or approaches in order to fill up an entire article. As a consequence,
further publications can be written by varying these models and approaches. No
wonder that in average the content of these papers gets increasingly irrelevant,
meaningless, and redundant. Hence, it is becoming increasingly difficult to find
new and really interesting ideas in the mass of irrelevant publications.
The most extreme form of a Salami tactic is to publish the same result twice or
even more often than that. Such duplication of one’s own research output is of
course not allowed, but in reality proves to be an entirely effective way to
increase one’s research productivity. As we have seen above, the peer-review
process often fails to discover such double publications. Therefore, an anony-
mous survey on 3,000 American scientists from the year 2002 shows, at least
4.7 % of the participating scientists admitted to have published the same result
several times (Six 2008).
62 M. Binswanger
• Increase of the number of authors per article
It can be observed that the number of authors publishing articles in scientific
journals has largely increased over the recent decades. For example, in the
Deutsche Ärzteblatt the average number of authors per article has risen from 1
author per article in 1957 to 3.5 in 2008 (see Baethge 2008). This is, on the one
hand, due to the fact that experiments in particular have become increasingly
complex and that experiments are no longer carried out by a single scientist, but
rather by a team. An evaluation of international journals showed that today’s
average number of authors per article in modern medicine is 4.4, which is the
highest number; this is followed by physics with 4.1 authors per article. In
psychology, the average is 2.6 authors per article, while in philosophy, a field
still free of experiments, the average number of authors of an article is 1.1
(Wuchty et al. 2007).
However, the increase in team research is not the only reason for the constant
increase of authors per article. On the other hand, there is the incentive to
publish as much as possible and to be cited as often as possible. So, especially
those who have some power in the academic hierarchy (professors or project
leaders) try to use their power by forcing all team members to include them as
authors in all publications of their research team. And the larger the team, the
more publications with this kind of ‘‘honorary authorship’’ are possible. Con-
versely, it may also be attractive to young scientists to include a well-known
professor as a co-author because—thanks to the dubious nature of anonymity
within the peer-review process—this improves the chances of publication (see
above).
Instead of ‘‘honorary authorship’’ it would be also appropriate to speak of
‘‘forced co-authorship’’, as Timo Rager wrote in a letter to editor of the Neue
Zürcher Zeitung at the end of 2008. There we read: [Forced co-authorship] …
exists even at prestigious institutions at the ETH and at Max-Planck institutes. If
you protest against it, you are risking your scientific career. So in addition to the
real authors of scientific articles there are more and more phantom authors, who
did not actually contribute to an article, but want to increase the number of their
publications. In medicine, this trend appears to be particularly prevalent, which
also explains why the average number of authors per article in modern medicine
is so high. Based on the articles published in 2002, every tenth name in the
author list of the ‘‘British Medical Journal’’ and one in five in the ‘‘Annals of
Internal Medicine’’, were phantom authors (see Baethge 2008). Furthermore,
60 % of all articles published in the ‘‘Annals of Internal Medicine’’ cited at least
one phantom author. No wonder are there clinical directors with over 50 pub-
lications per year (see Six 2008), which, if they had really contributed to all
these publications, would be beyond the capacity of a normal human being.
With the number of co-authors, however, not only the publication list of par-
ticipating authors per article is growing, but also the number of direct and
indirect ‘‘self-citations’’ (Fröhlich 2006), which triggers a snowball effect. The
more authors an article has, the more all participating authors will be quoting
this article again, especially if they are again involved as co-authors in another
Excellence by Nonsense: The Competition for Publications in Modern Science 63
article: ‘‘I publish an article with five co-authors and we have six times as many
friends who quote us’’ (Fröhlich 2006).
• Ever-increasing specialization
To meet this enormous need for publication, new journals for ever more finely
divided sub-areas of a research discipline are launched constantly. Thus, the
total number of worldwide existing scientific journals is estimated between
100,000 to 130,000 (Mocikat 2009), and each year there are more. By getting
increasingly specialized and narrow-minded, chances for publication are
improved (Frey et al. 2009). It is advisable to be specialized in a very exotic but
important-sounding topic which is understood only by very few insiders, and
establish a scientific journal for this topic. Consequently, the few specialists
within this field can promote their chances of publication by writing positive
reviews in the peer-review process, so that they will all get published.
Let us just take the topic of ‘‘wine’’ as an example: There is the ‘‘Journal of
Wine Economics’’, the ‘‘International Journal of Wine Business Research’’,
‘‘Journal of Wine Research’’, the ‘‘International Journal of Wine Marketing,’’
and so on. All of these are scientific journals that deal with wine on a ‘‘highly
scientific’’ level, covering topics such as wine economics, wine marketing, or
sales. Probably we will soon also have specialized journals for red-wine and
white-wine economics and we also await the ‘‘Journal of Wine Psychology’’.
• Forgery and fraud
Last but not least, the whole competition for as many publications and citations
as possible leads to fraud and forgery. The higher the pressure to increase
productivity, the more likely it is to resort to doubtful means. (Fröhlich 2006).
The assumption that universities are committed to the search for truth (Wehrli
2009) becomes more and more a fiction. Modern universities are exclusively
committed to excellence and the search for truth does not help very much in this
respect. No wonder that quite a few cases of fraud have become publicly known
more recently.
A good example is the former German physicist Jan-Hendrik Schoen, born
1970, who was celebrated as the German Excellence prodigy until his case of
fraud was discovered. For some time it was believed that he had discovered the
first organic laser and the first light-emitting transistor, and accordingly he was
highly praised and received a number of scientific awards. At the peak of his
career, as a 31-year-old rising star at Bell Laboratories in the United States, he
published an article in a scientific journal on average every eight days, of which
17 were published in highly respected journals such as ‘‘Nature’’ or ‘‘Science’’.
No one seemed to notice that this is simply impossible if you do proper research.
Instead the German scientific community was proud that they were able to come
up with such a top performer. It took some time until co-researchers doubted his
results and soon the data turned out to be forged in large parts. A lot of the
results were simply simulated on the computer. The interesting thing is, as Reich
(2009) writes in her book ‘‘Plastic Fantastic’’, that these forgeries would
probably never have even been discovered if Schoen had not exaggerated so
64 M. Binswanger
much with his publications. Otherwise, he would probably be a respected pro-
fessor at a top university by now and part of an excellence cluster.
Cases of fraud such as the example of Jan Hendrik Schoen mainly affect the
natural sciences, where the results of experiments are corrected or simply get
invented. Social sciences often have gone already one step further. There,
research is often of such a high degree of irrelevance that it does not matter
anymore whether a result is faked or not. It does not matter one way or the other.
The overall effect of all these perverse incentives is that scientists produce more
and more nonsense, which adds nothing to real scientific progress. In turn, because
the articles also become increasingly out of touch with reality, they are read less
and less. Moreover, the increase in citations is not a sign of increased dispersion of
scientific knowledge because, presumably, most articles get quoted unread. This
has been shown by research that documents how mistakes from the cited papers
are also included in the articles which cite them. (Simkin and Roychowdhury
2005). Therefore, more and more articles are published but they are read less and
less. The whole process represents a vicious circle that leads to a rapid increase in
the publication of nonsense. In the past, researchers who had nothing to say at least
did not publish. However, today, artificially staged competitions force even
uninspired and mediocre scientists to publish all the time. Non-performance has
been replaced by the performance of nonsense. This is worse because it makes it
increasingly difficult to find the truly interesting research in the mass of insig-
nificant publications.
Side Effects of the Production of Nonsense in Science:
‘Crowding-Out’ Effects and New Bureaucracy
The artificially staged competitions in science for publications and citations, but
also for third-party projects (financing), have caused the emergence of more and
more nonsense in the form of publications and projects. This is associated with a
variety of side effects, some of which have serious consequences. The intrinsic
motivation of those scientists involved in research is increasingly replaced by a
system of ‘‘stick and carrot’’. Indeed, this is not the only crowding-out effect that
we can observe. In addition, a new bureaucracy has evolved which ensures that
more and more people employed in the research system spend more and more time
on things that have nothing to do with true research. Both effects cause a gradual
deterioration within many scientific disciplines, but they are advertised under
labels such as ‘‘more excellence’’ and ‘‘more efficiency’’.
Crowding-Out Effects
Some of the crowding out effects triggered by competitions for publications and
projects were already previously addressed in this contribution. Here we will show
how this crowding-out effects harm universities and the scientific world.
Excellence by Nonsense: The Competition for Publications in Modern Science 65
• Crowding-out of intrinsic motivation by stick and carrot
Carrots and sticks replace the taste for science (Merton 1973) which is indis-
pensable for scientific progress. A scientist who does not truly love his work will
never be a great scientist. Yet exactly those scientists who are intrinsically
motivated are the ones whose motivation is usually crowded out the most. They
are often rather unconventional people who do not perform well in standardized
competitions, and they do not feel like constantly being forced to work just to
attain high scores. Therefore, a lot of potentially highly valuable research is
crowded out along with intrinsic motivation as well.
• Crowding-out of unconventional people and approaches by the mainstream
Both original themes and unconventional people have little in the way of
chances in a system based on artificially staged competitions. The peer-review
process causes potential authors and project applicants in both the competition
for publication and the competition for third-party funding (their projects are
also judged by peers) to converge upon mainstream topics and approaches, as
novel ideas and approaches get rarely published or financed. However, scientific
geniuses were hardly ever mainstream before their theories or methods were
accepted. They are often quite unconventional in their way of working and,
therefore, do not perform well in assessments which are based upon the number
of publications in professional journals, citations, or projects acquired. Neither
Albert Einstein nor Friedrich Nietzsche would be able to pursue a scientific
career under the current system.
• Crowding-out of quality by quantity
In the current system, scientific knowledge is replaced by measurable outputs.
Not the content of an article or a project counts, but the number of published and
cited articles or the number and the amount of money of the acquired projects.
Since the measurable output is considered to be the indicator of quality, the true
quality is more and more crowded out. The need to publish constantly leaves no
time to worry too long about the progress of knowledge, although this should be
the real purpose of scientific activity.
• Crowding-out of content by form
Closely related to the crowding-out of quality by quantity is the crowding-out of
content by form. As content gets more trivial and inconsequential, one tries to
shine with form: With complicated formulas or models, with sophisticated
empirical research designs, with extensive computer simulations, and with
gigantic machinery for laboratories. The actual content of research drifts into the
background and the spotlight is turned on formal artistry. For example, it does
not matter anymore if comparing different data sets really brings benefit to the
progress in knowledge. Important is the sophistication of the method which was
used for the comparison of the data sets.
• Crowding-out of research by bureaucracy
This crowding-out effect makes a significant contribution to the new bureau-
cracy described in the following section. The people employed in research such
as professors, heads of institutes, research assistants, or graduate students
actually spend an ever-larger portion of their time coping with research
66 M. Binswanger
bureaucracy. This obviously includes the time it takes to write (often unsuc-
cessful) research proposals, and later on interim and final reports: This is time a
researcher must spend as a price for actually participating in the project com-
petition. In this way, the actual research is paradoxically repressed by its
advancement because the administrative requirements no longer permit
research. Furthermore, each journal article submitted for publication requires an
enormous effort (strategic citing, unnecessary formalization, etc.), which has
nothing to do with its content. Bureaucracy, and not research, ultimately con-
sumes most of the time that is spent on writing scientific publications in journals
and on carrying out projects that do not contribute anything to scientific
knowledge, but have the goal of improving the measurable output.
• Crowding-out of individuals by centers, networks, and clusters
Competitions for projects also cause individual researchers to disappear more
and more behind competence centers, networks, and clusters. Research insti-
tutions prefer to pump research money into large research networks which are
supposed to provide excellence. Scientists see themselves under pressure to
reinvent preferably large cooperative and long-range projects with as many
research partners (network!) as possible, bringing third-party funds to their
institution. Large anonymous institutions such as the EU give money to other
large anonymous institutions (e.g. an excellence cluster) where the individual
researcher disappears, becoming a small wheel in a big research machine.
• Crowding-out of ‘‘useless’’ basic research by application-oriented, ‘‘useful’’
research
The competition for third-party funded projects is especially driven in this way
because it is believed to initiate more and more ‘‘useful’’ research; this will
rapidly lead to marketable innovations and further on to more economic growth.
In this way, both humanities and basic research is gradually crowded out
because in these disciplines immediate usability can hardly be shown or pos-
tulated. For example, ‘‘useful’’ brain research displaces ‘‘useless’’ epistemology.
However, anyone who is familiar with the history of scientific progress knows
that often discoveries which were considered ‘‘useless’’ at their inception led to
some of the most successful commercial applications. The at first sight ‘‘use-
less’’ field of philosophical logic has proven to be absolutely central to the
development of hardware and software for computers.
The crowding-out effects described above vary from one scientific discipline to
another, but nowadays they can found in almost every discipline. They have
become obvious to such an extent that they cannot be ignored. However, politi-
cians and managers in charge of science do not actually care about this because
they want quick success that can be proven by measurable output. In turn, young
scientists are socialized by the established scientific system in a way that the
perverse effects caused by this development already appear to be normal to them.
Excellence by Nonsense: The Competition for Publications in Modern Science 67
The Emergence of a New Research Bureaucracy
One of the crowding-out effects that was just described concerns the crowding-out
of research by bureaucracy. From the outside, it looks just as if research activities
grow at a fast pace. There are more and more people employed at universities and
research institutions,6 the number of scientific publications increases steadily, and
more and more money is spent on research. However, the crowding-out effect
gives rise to people who seem to do scientific work, but mostly are not engaged in
research at all. Most scientists know about this phenomenon. What scientists at
universities and other research institutions are mostly doing are things such as
writing applications for funding of research projects, looking for possible partners
for a network and coordination of tasks, writing interim and final reports for
existing projects, evaluating project proposals and articles written by other
researchers, revising and resubmitting a rejected article, converting a previously
published article into a research proposal so that it can be funded retrospectively,
and so on.
It is clear that there is hardly time to do research under such conditions. Project
proposals of more than 100 pages are not uncommon today, and the application
process at some institutions has become like a maze which only a few specialists
can guide you through. An expert in this field, the sociologist Münch (2009, p. 8)
writes:
Staged competitions devour extensive stuff and resources for coordination, for application
processes, for evaluation and implementation which eat into actual research work, so that
exactly the very best researchers are falling into a newly created control machine and run
the risk of drowning in its depths.
The actual research today rests largely on the shoulders of assistants and
graduate students whose low hourly compensations still allow them to improve
scientific knowledge. In contrast, opportunity costs of doing research are often too
high for professors and research leaders, because they can contribute more to the
measurable output of their institution by focusing on the organization and man-
agement of project acquisitions and publications. In turn, because the postgradu-
ates are in fact often also forced to name their professors or institute directors as
co-authors of their publications, the list of publications of professors and research
leaders still grows despite their lack of continued research.
However, the above-described increase in the proportion of time that is lost due to
the bureaucracy associated with the artificial competitions is only the first step of the
new bureaucracy. The second step becomes evident by the ever-increasing number
of people working in governmental committees who are in charge of the
6 In Switzerland, the total number of employees at universities in Switzerland shows an increase
of 24,402 in 1995 to 32,751 in 2008, which is about one-third. Of the 32,751 employees in 2008
were 2,900 professors, 2,851 were lecturers, 15,868 were assistants and research assistants, and
11,132 people were outside of research and teaching in the administration or engineering work
(SBF 2007).
68 M. Binswanger
organization and the course of these artificially staged competitions. This is essential
to both the traditional research institutions such as universities or research institutes
as well as to the committees dealing with the organization and financing of research
(European Research Council, Federal Ministry for Education and Research, etc.).
Artificial competitions have enormously complicated research funding. Universities
and their respective institutes do not directly receive money for research anymore.
Instead, they have to write proposals for government-initiated research programs,
which have to get evaluated and administered. This is a complex and time-con-
suming process in which each process is associated with complicated procedures and
endless forms to be filled out. What is proudly called research-competition becomes,
at a closer look, a labor-intensive and inefficient re-allocation of funds from some
public institutions (Ministry of Research or the appropriate Federal Agency) to other
public institutions (universities, research institutes).
The second step of the increase in bureaucracy is also due to the fact that
universities, institutes, and even professors need to be evaluated and ranked.
Numbers are needed to decide which institutions or professors are really excellent,
and where it is worthwhile to promote excellence clusters or competence networks
and what institutions can be awarded the status of a ‘‘lighthouse in research’’.
There are already several public agencies (e.g. Centre d’Etudes de la science et de
la Technology in Switzerland) and university institutions (e.g. Centre for Science
and Technology Studies at the University of Leiden in the Netherlands), which
deal exclusively with the measurement of research inputs and outputs on the basis
of bibliometric and scientometric research. There the metrics are fabricated which
are necessary for the artificial competitions and which form the basis for the
rankings and, in turn, stimulate the publication and project competitions.
Research funding has reached its far highest level of bureaucracy by the EU
research programs,7 which appear to be especially sophisticated Keynesian
employment programs. None of the ‘‘Research Staff’’ working for the EU actually
does research because inventing increasingly complex application processes and
new funding instruments already creates a sufficiently large workload for them.
Therefore, a large portion of the research is just used to maintain this bureaucracy.
Already in 1997, the European Court of Auditors criticized the EU in relation to the
4th Research Program as an ‘‘enormous bureaucracy and a useless waste of money’’.
According to experts, only about 60 % of the 13 billion euros, which were provided
to the 4th Research Program, were actually received by research institutions. Not
much has been changed in the following programs. The responsible coordinator of
the Budgetary Control Committee of the European Parliament, Inge Gräßle (CDU),
after the end of the 6th Research Program (€ 16.7 billion in 4 years), came in 2007 to
the conclusion that ‘‘the effort to simplify funding allocation within the EU research
framework program has not yet been sufficient.’’ That is an understatement. The 6th
Program has invented a whole new, previously unknown form of networking
bureaucracy, which has led to much more, and not less, bureaucracy.
7 See also Binswanger (2003).
Excellence by Nonsense: The Competition for Publications in Modern Science 69
The increase in bureaucracy outside of the actual research institutions also
fosters an increase in bureaucracy within the research institutions. The large
number of EU-research officers has managed to complicate the application process
tremendously. In Germany alone hundreds of advisor posts have been created just
to help scientists to make a successful project application. Even if you have
completed the tedious work involved in making an application, the chance of
success is low. Since the 6th Research Program which began in 2002, the success
rate of applications lies between 15 and 20 %, while before the success rate of
applications was at about one quarter. In other words, between 80 and 85 % of
applications, involving about 12 researchers on average today, are not successful.
You can imagine what enormous amounts of time and money are here wasted just
to get funding from an EU project.
Of course, the new research bureaucracy has its positive impact on the econ-
omy. It creates new jobs and contributes to full employment. Keynes would
probably be amazed at what creative level his ideas of a national employment
policy have been developed within science. Back in the 1930s, the time of the
Great Depression, he mentioned that, in such situations, even totally unproductive
and useless activities would stimulate the economy and would therefore eliminate
unemployment. The example he gave referred to a governmental construction
project in which workers were employed digging ditches and then filling them up
again. Even if useless, such a program paid by government funds will cause an
increase in the demand for construction activities and thus stimulate the economy.
Today’s nonsense production in science is much more sophisticated than the
nonsense of digging ditches and filling them up again as the nonsense is disguised
to the general public.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Science Caught Flat-Footed:
How Academia Struggles with
Open Science Communication
Alexander Gerber
Change happens by listening and then starting a dialogue with
the people who are doing something you don’t believe is right.
—Jane Goodall, ethologist and devoted science communicator.
Abstract As high as the potential of Web 2.0 might be, the European academia,
compared to that of the US, mostly reacts hesitantly at best to these new oppor-
tunities. Interestingly enough this scepticism applies even more to science com-
munication than to scientific practice itself. The author shows that the supposed
technological challenge is actually a cultural one. Thus possible solutions do not
primarily lie in the tools or in the strategies used to apply them, but in the
adaptation of the systemic frameworks of knowledge-creation and dissemination
as we have practised them for decades, if not centuries. Permeating an ‘Open
Science Communication’ (OSC) under closed paradigms can only succeed if
foremost the embedding frameworks are adapted. This will include new forms of
impact measurement, recognition, and qualification, and not only obvious solu-
tions from the archaic toolbox of enlightenment and dissemination. The author
also illustrates the causes, effects, and solutions for this cultural change with
empirical data.
The swan song of what was meant to be an era of ‘‘Public Understanding of
Science and Humanities’’ (PUSH) rings rather dissonantly today, given the wailing
chorus of disorientation, if not existential fear, intoned by science communicators
across Europe. Almost 30 years after the game-changing Bodmer report (Royal
Society 1985), another paradigmatic shift is taking place, probably even more
radical than any of the transitions during the previous four eras (Trench and Bucchi
2010; Gerber 2012). This fifth stage of science communication is being predom-
inantly driven by interactive online media. Let us examine this from the
same perspectives which the very definition of ‘‘science communication’’ as an
A. Gerber (&)
German Research Center for Science and Innovation Communication, Berlin, Germany
e-mail: a.gerber@innokomm.eu
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_4,  The Author(s) 2014
73
umbrella-term also comprises of: (1) the communication about science, and (2) the
communication by scientists and their institutionalised PR with different publics.
Communication about Science
Journalists are witnessing a widespread disintegration of mass media outlets and
their underlying business models. In terms of circulation and advertising revenue,
this demise may not be as abrupt as in the U.S. (see Fig. 1), but is surely just as
devastating for popular science publishers in Europe, and consequently their staff
and freelancers in the long run (Gerber 2011, 2012). Brumfiel (2009) was among
the first scholars to make the scientific community aware of the extent of the
science journalism crisis, quoting blatant analyses by experienced editors, such as
Robert Lee Hotz from The Wall Street Journal: ‘‘Independent science coverage is
not just endangered, it’s dying’’ (p. 275). Afflicted by low salaries and even lower
royalties from a decreasing number of potential outlets, science journalism is
additionally (or maybe consequently) suffering from a continuous decrease in
credibility.
Fig. 1 The business models of traditional print journalism in the U.S. have eroded remarkably
fast: the industry has lost as much market share in five years as they had gained in the 50 years
before. The disintegration of mass media outlets in terms of circulation and advertising revenue
may not be as abrupt in Europe as it is in the U.S. Nonetheless, popular science publishers in
Europe are also heavily under pressure
74 A. Gerber
Questioned about who is most appropriate to explain the impact of science upon
society, only 16–20 % of Europeans nowadays name journalists—a further
decrease from 25 to 32 % five years before (see Fig. 2). In fact, a majority expects
the scientists themselves to deliver their messages directly: 63 %, increasing from
52 % five years earlier (European Commission 2010). Unfortunately, it has not yet
been investigated properly as to what extent this credibility also (or particularly)
extends over the science blogosphere and other online platforms, or whether
interactive online media have even been catalysts, and not just enabling technol-
ogies, in this development.
Every discourse or effort to reinvent journalism regarding media economics
(e.g. crowdfunding), investigation methods (e.g. data-driven journalism in the deep
web), formats (e.g. slide casts), and distribution (e.g. content curation) almost
inevitably seems to circle around interactive online media. Obviously the web is
not only seen as the main cause of the crisis, but also as the main opportunity for
innovations to solve it.
On the other hand, one could also argue that due to an increasing number of
popular science formats on television, science journalism now reaches a much
wider audience as compared to print publications which have always merely
Fig. 2 Who is most appropriate to explain the impact of science upon society? Less than one out
of five Europeans nowadays name journalists, and the numbers are constantly decreasing (light
grey 2010, dark grey 2005). In fact, a majority expects the scientists themselves to deliver their
messages directly. Interactive online media offer new opportunities to do exactly that
Science Caught Flat-Footed: How Academia Struggles with Open Science Communication 75
catered to a small fraction of society. Especially on TV, however, we as com-
munication scholars should be wary of the distorted image of science reflected by
conventional mass media. Coverage and content are mostly limited to either the
explanation of phenomena in everyday life (‘‘Why can’t I whip cream with a
washing machine?’’) or supposed success stories (‘‘Scientists have finally found a
cure for Cooties’’). Thereby journalism neither succeeds in depicting the ‘big
science picture’ of policy, ethics, and economics holistically, nor the real com-
plexity of a knowledge-creation process authentically, which is everything but
linear, being a process in which knowledge is permanently being contradicted or
falsified, and is therefore never final.
However, the notion of what the essence of science really is could perfectly
well be vulgarised through web technologies, in the sense of making the different
steps within this process of knowledge-creation transparent, for instance by means
of a continuous blog or other messaging or sharing platforms. Yet there are still
only very few examples for such formats (see below), and they are particularly
sparse in journalism.
The tendency to reduce science to supposed success stories is certainly also a
result of its mediatisation, i.e. science and science policies reacting and adapting to
the mass media logic by which it is increasingly being shaped and framed (Krotz
2007; Fuller 2010; Weingart 2001). This brings us to the second dimension of
science communication.
Communication by Scientists and the Institutionalised PR
Self-critical scholars as well as practitioners of science communication wonder
how far we have effectively come since 1985, when the Royal Society envisioned
a public which would understand ‘‘more of the scope and the limitations, the
findings and the methods of science’’. Even then the ‘‘most urgent message’’ went
to the scientists themselves: ‘‘Learn to communicate with the public, […] and
consider it your duty’’ (The Royal Society 1985, p. 24).
Almost 30 years later the resources for institutionalised science PR and mar-
keting have multiplied remarkably. Compared to the early 1990s when profes-
sional communicators were rare exceptions, there is hardly a single university or
institute left today without a communication department. Larger institutions
employ up to 70 full-time communicators in the meantime. Yet less than one out
of ten citizens in Europe actually show any interest whatsoever in science centres,
public lectures, or science fairs (European Commission 2010, see Fig. 3)—albeit
with a blanket coverage across the continent. Such obvious contrasts between the
supply and demand of certain formats make science an easy prey for critics
arguing that its communication is inherently elitist. At least the often misinter-
preted decrease in naïve trust in science—66 % in 2010 compared to 78 % in 2005
(European Commission 2010)—is an encouraging sign of an increasingly critical
public (Bauer 2009).
76 A. Gerber
PR professionals have been ‘PUSH-trained’, so to speak, to focus on the dis-
semination of research results, ideally in the form of a well-told success story, as
described above, and thereby significantly contribute to the distorted media image
of scientific reality. Unfortunately just now that science, at least on an institutional
level, seems to have come to terms with the mechanisms of mass media; PR and
marketing are being shattered by a seismic shift towards a horizontalisation of
Fig. 3 Presumably as a direct result of mediatisation in the era of PUSH, the so-called ‘‘myth of
science’’, the naïve trust in science being able to solve any problem, has decreased significantly.
In some countries like Germany (inner pie) this trust is even lower than the European average.
This development is often misinterpreted as a problem. It should instead be seen as an
encouraging sign of an increasingly critical public
Science Caught Flat-Footed: How Academia Struggles with Open Science Communication 77
communication better known as social media. Particularly the new media-savvy
student generations have an entirely different understanding of how the relevant
information should ‘find them’. Even most communication scholars are still
amazed of the pace of this transition. In countries like Germany, for instance, the
Internet overtook television two years ago in terms of activated and structured
demand for information, i.e. the medium of choice to look for quality informa-
tion—an increase from 13 % in 2000 to 59 % in 2011 (IfD Allensbach 2011).
Unanimously most studies show, however, that science has as of yet largely
avoided adapting both its communication efforts and its own media usage to the
above mentioned changes in the information behaviour of laypeople (Procter et al.
2010; Bader et al. 2012; Allgaier 2013). In a web technology use and needs
analysis Gerber (2012, 2013) showed that the use of even the most common online
tools is still a niche phenomenon in the scientific community. Furthermore, the few
well-known tools were also the ones to be the most categorically rejected, e.g.
Twitter by 80.5 % of the respondents.
Yet the diffusion of Web 2.0 tools in academia is only partly a question of
technology acceptance. For instance, online communication is still not taken into
account in most cases of evaluation or allocation of research funding. Most experts
in a Delphi study on science communication (Gerber 2011, 2012) therefore demand
a critical discourse about possible incentives for scientists. If online outreach,
however, became a relevant criterion for academic careers, we would also have to
find more empirically sound ways to measure, compare, or even standardise and
audit the impact of such an outreach. Approaches like ‘Altmetrics’ are promising
but still in a conceptual phase. At least for another few years we will therefore have
to deal with a widening gap between the masses of scientists communicating
‘‘ordinarily’’, on the one hand, and the very few cutting edge researchers and
(mostly large and renowned) institutions experimenting extensively with the new
opportunities, on the other. Thus the threat of increasing the already existing
imbalances between scientific disciplines is just as evident as the opportunities
for increasing transparency, flattening hierarchies, or even digitally democratizing
the system itself, as sometimes hyperventilated by Open Science evangelists.
We must not forget that technologies only set the framework, whereas the real
challenges and solutions for an ‘Open Science Communication’ are deeply rooted
in scientific culture and the system of knowledge creation itself (Gerber 2012).
Much will, therefore, depend upon the willingness of policy makers to actively
steer the system in a certain direction. Yet they also have to reconsider whether
they thereby risk fostering (even unintentionally) the above mentioned distortion
of scientific practice. The ultimate challenge lies in balancing incentives and
regulations, on the one hand, with the inevitable effect of a further mediatisation of
science, on the other, since both remain two sides of the same coin.
Public relations and science marketing professionals will keep struggling with
the increasing ‘loss of control’ as long as they hang on to their traditional paradigm
of dissemination. An increasing number of social media policies in academia
shows that the institutions have realised the challenges that they are facing in terms
of governance. By accepting ‘deinstitutionalisation’ and involving individual
78 A. Gerber
scientists as authentic and highly credible ambassadors (see above), PR can make
the most essential step away from the old paradigm to the new understanding of
Open Science Communication (OSC).
The common ground for both above mentioned trends—the ‘deprofessionali-
sation’ of science journalism and the ‘deinstitutionalisation’ of science commu-
nication at large—is the remarkable amount of laypersons finding their voices
online and the self-conception of civil society organisations demanding to be
involved in the science communication process. As much as this inevitably shat-
ters the economic base of science journalism and as much as it may force the
science establishment to reinvent its communication practice, we should be
grateful for the degree of communication from ‘scientific citizens’. Thereby the
challenge lies less in permitting (or even embracing) bottom–up movements as
such, but rather more in resisting the use of public dialogue as a means to an end.
While valorising ‘citizen science’ as an overdue ‘co-production’ of authoritative
social knowledge, Fuller warns us not to treat broadcasts of consensus confer-
ences, citizen juries, etc. simply as better or worse amplifiers for the previously
repressed forms of ‘local knowledge’ represented by the citizens who are now
allowed to share the spotlight with the scientists and policy makers. (2010, p. 292)
The questionable success of most of these public engagement campaigns has
increasingly been challenged recently. Grassroots initiatives like ‘Wissenschafts-
debatte’ or ‘Forschungswende’ in Germany criticise openly the fact that pseudo-
engagement has merely served as a fig leaf excuse for the legitimisation of
research agendas which are still being built top–down. Instead it will be necessary
to supplement the dragged-in rituals of ‘end of pipe’ dissemination with a fresh
paradigm of ‘start of pipe’ deliberation.
Undoubtedly such initiatives cater to the transparency and true public
engagement pursued by the ideal of Open Science. Thus within the ‘big picture’
we should embrace the opportunities of the OSC era, and in particular the inter-
active online technologies driving it.
Outlook
Driven by interactive online media, OSC has the potential to exceed the outdated
view of communication as a ‘packaging industry’. In the next few years we can
expect a second wave of professionalisation in science PR and marketing, e.g.
through specialised social media training. The performance of communication
professionals (and probably also the performance of scientists) will increasingly be
measured by whether they succeed in truly engaging a much wider spectrum of
society or not. New cultures of communication may foster a scientific citizenship
but will also raise new questions regarding imbalances and distortion within the
scientific system, and thus the challenge to measure communication impact
properly, and even normalise and standardise these new measurements.
Science Caught Flat-Footed: How Academia Struggles with Open Science Communication 79
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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80 A. Gerber
Open Science and the Three Cultures:
Expanding Open Science to all Domains
of Knowledge Creation
Michelle Sidler
How knowledge circulates has always been vital to the life of
the mind, which all of us share, just as it is vital, ultimately, to
the well-being of humanity.
—John Willinsky
Abstract The Open Science movement has been most successful in transforming
disciplines traditionally associated with science. Social science and humanities
disciplines, especially those in the United States, are less well represented. To
include all domains of knowledge, the Open Science movement must bridge these
‘three cultures’ through projects that highlight multiple lines of inquiry, research
methods, and publishing practices. The movement should also consider changing
its moniker to Open Knowledge in order to include academic disciplines that do
not self-identify as science.
In 1959, C. P. Snow’s lecture, ‘The Two Cultures,’ argued that the sciences and
the humanities were divided and at odds: ‘‘Literary intellectuals at one pole—at
the other scientists, and as the most representative, the physical scientists.
Between the two a gulf of mutual comprehension—sometimes (particularly among
the young) hostility and dislike, but most of all lack of understanding’’ (p. 4).
These divisions are felt perhaps most poignantly in American universities. Several
cultural, economic, and historical events have led to increased divisions between
not only the sciences and the humanities, but also the social sciences. Within each
of these ‘three cultures’ (a term coined in Kagan’s 2009 book of the same name),
sub-disciplinary divides persist as well, creating pockets, or ‘silos’ of knowledge
communities with their own methods, languages, professional organizations,
identities, and so on.
These divisions have roots in the rise of American universities at the turn of the
twentieth century. At that time, the liberal arts tradition of a shared curriculum was
replaced in many schools by German educational philosophies that emphasized
Lernfreiheit, freedom in learning. Lernfreiheit models encouraged students to
M. Sidler (&)
Auburn University, Alabama, USA
e-mail: sidlema@auburn.edu
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_5,  The Author(s) 2014
81
choose their own courses and majors and prompted faculty to pursue specialized
research; these changes eventually led to a model of higher education that replaced
generalist courses and professors with individualist disciplinary departments
(Hart 1874). Several national legislative acts were also passed during and after this
philosophical change, and these moves privileged the sciences, writ large, over the
humanities and fine arts while more or less neglecting the social sciences. First,
the Morrill Act of 1862 established institutions of higher learning in service to the
rising economic needs in industry and agriculture. Accordingly, these universities
emphasized science-related disciplines over those in the social sciences and
humanities.1 Next, the mid-twentieth century saw the establishment of government
agencies that fund science research, and the amount they make available to
scientists far outweighs that of funding for the humanities and fine arts.2 Add these
historical factors to the rather abysmal job prospects for most humanities and
social science majors in the present day, and one can easily understand how
disciplinary divisions persist in American universities. Indeed, some scholars like
Kagan argue that the privileging of the sciences ‘‘created status differentials that
eroded collegiality and provoked defensive strategies by the two less advantaged
cultures’’ (2009, p. ix).
Proponents of Open Science must understand these cultural divides as we move
forward into a new era of knowledge, discovery, and collaboration. Nielsen (2012)
and others3 have noted that the immediate challenge for the Open Science
movement is its ability to change the culture of science itself, which continues to
operate within a print-based, proprietary, and closed framework for scientific
discovery and communication. But a larger challenge looms on the horizon: if the
Open Science movement hopes to advance change among all areas of knowledge
and discovery, it must overtly articulate a larger mission, one that acknowledges
the potential impact of networked technologies on all fields of knowledge. Perhaps
this mission is tacitly assumed, but it cannot be so, especially when the movement
has adopted the moniker, Open Science. At best, humanities and social science
scholars, especially those in the U.S., will assume that this movement does not
apply to them because ‘science’ is a term generally reserved for disciplines that
employ the scientific method. Such a designation does not include most areas of the
1 The Morrill Act of 1862 apportioned 30,000 acres (120 km2) to each state. The land was sold
or used for ‘‘the endowment, support, and maintenance of at least one college where the leading
object shall be, without excluding other scientific and classical studies, and including military
tactics, to teach such branches of learning as are related to agriculture and the mechanic arts … in
order to promote the liberal and practical education of the industrial classes in the several pursuits
and professions in life’’ (The Morrill Act 1862).
2 The National Institutes of Health (NIH) and the National Science Foundation (NSF) provide
approximately $38 billion in research funding annually (NIH: http://officeofbudget.od.nih.gov/
br.html; NSF 2012). This number far outweighs the amount afforded by the major humanities and
arts organizations, the National Endowment for the Humanities (NEH) and the National
Endowment for the Arts (NEA), which combined have an annual budget of approximately $308
million (NEH: http://www.neh.gov/divisions/odh; NEA 2012).
3 See, for example, Lin (2012) and Willinsky (2006).
82 M. Sidler
humanities and social sciences. At worst, non-science scholars will perceive Open
Science as a threat, another way in which scientific disciplines dictate methods for
knowledge production and maneuver for more resources.
This challenge is not insurmountable, but it will involve intensive collaboration
and understanding among scientists, social scientists, and humanists. Most
immediately, we should examine the term ‘Open Science.’ Scholars of language,
rhetoric, and writing (like myself) are keenly aware of the power of words and
their associations, and the word ‘science’ carries associations of division and
inequality for some humanities and social science scholars.
Either the movement will have to create and foster a broader definition of
‘science’ or it will have to replace the term altogether. To use the moniker
effectively, the Open Science movement will have to acknowledge and address
disciplinary divisions and monetary reward systems that led to this acrimony.
A first step might be a broader exploration of the potential impact that networked
technologies will have on different knowledge areas. Knowledge discovery and
communication practices vary among different disciplines, but no thorough tax-
onomy of these differences currently exists within the Open Science community.
Figure 1 offers just a few examples of the differences between current publishing
practices in the sciences and the humanities (although the social sciences are not
included here, a similar comparison could be made between the sciences and social
sciences). The figure makes clear that the scientific communication is already
utilizing digital channels of communication much more deliberately and com-
pletely than the humanities. Moreover, digital technologies and Open Access
principles (like those employed in Open Science initiatives) have so far achieved
minimal impact on publications in the humanities. Such comparisons will be
necessary to inventory the status of publishing and research in various disciplines,
and then to devise ways new technologies might enhance research and commu-
nication across all fields of knowledge.
Another strategy that would alleviate the potential conflict with the moniker
‘Open Science’ is to substitute it with the phrase ‘Open Knowledge.’ This broader,
more inclusive alternative is already employed by at least one organization, the
Open Knowledge Foundation (OKF)4; their vision statement includes compre-
hensive, inspirational passages that foreground a desire to be as inclusive as
possible: ‘‘We promote the creation, dissemination and use of open knowledge in
all its forms, from genes to geodata and from sonnets to statistics. Our projects,
groups and tools work with this principle in different and varying ways to increase
user access and ensure transparency.’’ OKF includes investigative examples from
the social sciences and humanities (‘sonnets and statistics’), emphasizing that their
mission includes all forms of knowledge. Compare this to the description of the
Science Online Conference, a major organizing body for Open Science advocates
in the U.S.: ‘‘ScienceOnline5 is a non-profit organization … that facilitates
4 OKF: http://okfn.org/about/vision/
5 ScienceOnline: http://scienceonline.com/about/
Open Science and the Three Cultures 83
conversations, community, and collaborations at the intersection of Science and
the Web.’’ Few humanities and social science scholars in the U.S. would read this
description and assume that their attendance at the conference would be appro-
priate, or that the issues discussed at the conference have much impact on their
own disciplines.
Perhaps the most pragmatic strategy for bridging the ‘three cultures’ is to
develop projects that adapt the digital tools of Open Science for research projects
in the humanities and social sciences. OKF is a leader in this regard as well: they
are pursuing initiatives such as the Shakespeare Project, a website that aims to
provide multiple annotated editions of Shakespeare’s plays. The Shakespeare
Project leverages open source annotation tools, crowdsourcing, and literary works
in the public domain to create a free, open, interactive, and ongoing space of
knowledge production and access. These same principles are at the heart of per-
haps the most extensive and exciting U.S. program that uses methods similar to
Open Science in the humanities, the Office of Digital Humanities (ODH). ODH is
a set-aside program within the National Endowment for the Humanities (NEH),
and its express purpose is to enhance humanities research through technology.
More specifically, ODH has been aggressively pursuing ways to promote digital
methods of preserving, archiving, and accessing scholarly materials. One of its
initiatives is a series of institutes around the country that train humanities
researchers in the methods and tools of digital analysis, data curation, and text
coding. Like OKF, ODH recognizes that humanities scholars have a different set of
Fig. 1 Publishing practices of the sciences and the humanities
84 M. Sidler
publishing priorities and technological expertise than that of scientists, so it
leverages the most immediately applicable and effective tools to promote digital
research in a community that has traditionally understood knowledge production
as a print-based, textual enterprise.
While a similar claim can be made about a traditionalist culture in the sciences—
and indeed, it has been made most emphatically by Nielsen (2012)—the degree to
which the humanities and social sciences are engaged with technology is expo-
nentially smaller. Moreover, the humanities and social sciences have a qualitatively
different perspective—a different culture, to use Kagan’s (2009) term—that
requires investigation, interpretation, and engagement by Open Science advocates
in order to expand this project more fully into other domains of knowledge.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
Hart, J. M. (1874). German Universities: a narrative of personal experience, together with recent
statistical information, practical suggestions, and a comparison of the German, English and
American systems of higher education. New York: Putnam & Sons.
Kagan, J. (2009). The three cultures: natural sciences, social sciences, and the humanities in the
21st century. New York: Cambridge University Press.
Lin, T. (2012, January 16). Cracking open the scientific process. NY Times, p.D1.
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Resolutions of Congress, 1789–1996; Record Group 11; General Records of the United States
Government; National Archives.
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http://www.neh.gov/about/legal/reports.
National Science Foundation, (2012). FY 213 Budget Request to Congress, Available at: http://
www.nsf.gov/about/budget/fy2013/index.jsp.
Nielsen, M.A. (2012). Reinventing discovery: the new era of networked science, Princeton, NJ,
Princeton University Press.
Snow, C.P. (1961). The two cultures and the scientific revolution 7th ed., New York: Cambridge
University Press. Available at: http://sciencepolicy.colorado.edu/students/envs_5110/
snow_1959.pdf.
Willinsky, J. (2005). The access principle: the case for Open Access to research and scholarship.
Cambridge, MA: MIT Press.
Open Science and the Three Cultures 85
Part II
Tools
(Micro)Blogging Science? Notes
on Potentials and Constraints of New
Forms of Scholarly Communication
Cornelius Puschmann
Abstract Academic publishing, as a practice and as a business, is undergoing the
most significant changes in its 350-year history. Electronic journals and books,
both Open Access and behind digital pay walls, are increasingly replacing printed
publications. In addition to formal channels of scholarly communication, a wide
array of semi-formal and informal channels such as email, mailing lists, blogs,
microblogs, and social networking sites (SNS) are widely used by scientists to
discuss their research (Borgman 2007, p. 47; Nentwich and König 2012, p. 50).
Scholarly blogs and services such as Twitter and Facebook are increasingly
attracting attention as new channels of science communication (see Bonetta 2007;
Kjellberg 2010; Herwig et al. 2009). Radically different conceptualizations of
scholarly (micro)blogging exist, with some users regarding them as a forum to
educate the public, while others see them as a possible replacement for traditional
publishing. This chapter will provide examples of blogs and microblogs as tools
for scientific communication for different stakeholders, as well as discuss their
implications for digital scholarship.
Framing the Issue: New Forms of Scholarly
Communication and Science 2.0
There is a broad consensus that modern science is undergoing profound structural
changes afforded by the rise of digital technology, and that this change is occuring
on multiple levels of the scientific work process at once (Nielsen 2012; Nentwich
and König 2012). The abundance of massive storage capacities, high volumes of
processing power, and ubiquitous network access enables new forms of research
which are contingent on large quantities of digital data and its efficient
C. Puschmann (&)
Humboldt University, Berlin, Germany
e-mail: puschmann@ibi.hu-berlin.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_6,  The Author(s) 2014
89
computational analysis (Weinberger 2011). This development is underscored by
the rise of data science, that is, science that is driven by the analysis of large
quantities of data from a wide range of sources such as sensors, scanners, MRI,
telescopes, but also human-generated data from social media and digital libraries,
and interrogated through statistical procedures, machine learning algorithms, and
other computational instruments, allowing researchers to discover previously
unrecognized patterns. Such approaches are innovative in the sense that they
surpass the capabilities of traditional research in making observations of changes
in very complex systems as they unfold, and in that they potentially allow pre-
dictions regarding the future behavior of such systems (Golder and Macy 2012).
Whereas research has in the past been based upon comparably scarce evidence, the
promise of data science is that it will be both scalable and reproducible on a
previously unimaginable level, providing novel insights into a wide array of areas,
from climatology to social science (Lazer et al. 2009) (Fig. 1).
Fig. 1 myExperiment is one of a number of new virtual research environments (VREs)
90 C. Puschmann
Beyond innovation of research methods, other aspects of how science is
undertaken are also changing visibly, both as a result of technological shifts and
because of economic and cultural changes in how research is financed and orga-
nized (cf. several contributions in this volume). From teaching and funding to
publishing and peer review, it seems that a variety of aspects of how scientists
work are changing, and that communication is at the forefront of this change, a
change brought about primarily by the proliferation of technologies which are
themselves the result of publicly funded scientific research. These technologies not
only make it easier, cheaper, and quicker for scientists to exchange information
with peers around the globe, they also have the potential to blur the line between
internal communication among researchers and communication with the wider
public. New formats must be adopted for scholarly use to fit the needs of aca-
demics while established genres evolve as a result of new technologies for the
production and dissemination of scholarly publications (Cope and Kalantzis 2009).
Scientists have, of course, always been avid communicators. From Darwin’s
notebooks to the Large Hadron Collider, getting complex scientific issues across
both to colleagues and laypersons has been at the top of the agenda for researchers
for as long as modern science has existed. Successful communication is integral to
scholarship because it allows scientific knowledge to proliferate, enable practical
applications, and become entrenched societal knowledge, but also because fre-
quently the outcomes of scientific research have far-reaching societal implications
and are highly controversial (e.g., climate research, nuclear energy, genetics).
Scientists must be able to explain what they do to a broader public to garner
political support and funding for endeavors whose outcomes are unclear at best
and dangerous at worst, a difficulty which is magnified by the complexity of
scientific issues. They do so in an increasingly challenging environment, engaging
with a public that has access to a wide range of sources of (by scientific standards)
often dubious quality, many of them online (Puschmann and Mahrt 2012; König
2011). This public is increasingly critical and unimpressed by scientific authority
and simple promises of scientific progress as an enabler of economic growth and
societal welfare, and must be both won over and brought on board, rather than
talked down to. Civil society expects to be engaged in a dialog with science, rather
than being lectured. The affordances of social media (blogs, wikis, social net-
working sites) should accordingly be regarded as supporting a general long-term
shift towards a more egalitarian relationship between experts and the lay public,
rather than driving it (Fig. 2).
Intra-scientific discourse is changing as well, as a result of the move from paper
to digital, which seems almost completed in much of the hard sciences. The
majority of formal publishing in the STM disciplines takes place in academic
journals and conference proceedings, with pre-prints, post-prints, reports, technical
manuals, posters, and other formats also playing an important role (Borgman
2007). Increasingly, traditional academic genres (journal articles, conference
papers, scholarly monographs) are published online, rather than in print, and
disseminated through a variety of channels (email, blogs, online book reviews,
(Micro)Blogging Science? Notes on Potentials and Constraints 91
social media). Preprint archives such as arXiv1 and Social Science Research
Network (SSRN)2 have proliferated in a variety of disciplines and continue to grow
in popularity. Beyond Open Access, there is an increased push for adding features
that make use of the affordances of digital publishing, such as interactive charts
and figures, and towards providing raw data along with papers to encourage fol-
low-up research, for example on sites such as Figshare.3
Science Blogging as a New Form of Engaging with Science
While still an emergent phenomenon, new and genuinely digital forms of scholarly
communication play an increasingly significant role in discussions about the future
of academic discourse, especially as the existing system of knowledge dissemination
is increasingly characterized as threatened or even dysfunctional (cf. Cope and
Kalantzis 2009; Stein and Puschmann 2010). The phenomenon of science blogging
has attracted significant attention and discussion in papers (e.g., Batts et al. 2008;
Tola 2008; Shema et al. 2012) and at conferences (e.g., ScienceOnline ‘09, Science
Blogging 2008: London). Sites such as Nature Network, ScienceBlogs.com, and
Fig. 2 CERN’s Twitter page
1 http://www.arXiv.org
2 http://www.ssrn.com/
3 http://www.figshare.com/
92 C. Puschmann
Hypotheses.org act as hosting platforms of such specialized academic content,
allowing researchers to present and discuss their work before a global audience,
some with commercial publishers backing them, others funded publicly. Increas-
ingly, universities and research institutes offer blog hubs which either aggregate
externally-hosted content contributed by students and faculty members or allow
direct publishing through the institutional website. Many academic researchers also
rely on commercial hosting platforms such as Wordpress.com and Blogger.com to
exchange information with peers and to document their projects.
A non-scholarly genre that has been adopted for scholarly communication, blogs
are just one tool in a wider array of new formats. New platforms for publishing such
as Open Journal Systems (OJS) and Annotum seek to make the processing and
presentation of scholarly texts better adjusted to their digital environment.
Monographs are also redefined in new approaches from initiatives such as Press-
Forward or OpenEdition, which seek to modify both the dissemination of academic
publications and the economics behind its distribution (costly production
techniques, long delays between authoring and publication). Beyond making the
results of scholarly research available online, efforts are being made to make
scholarly formats themselves more innovative and better adjusted to the Internet
(cf. Jankowski et al. 2012). The need to facilitate new means of knowledge pro-
duction, presentation, and dissemination is widely felt, not only inside science itself,
but also among policymakers. This need is fuelled both by the exponential growth of
scholarly publishing (Jinha 2010) and the costs associated with the current model of
subscription-based access. Different models have been proposed among the different
varieties of Open Access (i.e. the ‘gold road’ model of immediate Open Access and
the ‘green road’ model of delayed Open Access after an embargo period). Alternate
funding schemes include author fees and institutional consortia, as well as direct
public funding, for example via libraries (Houghton 2010).
Beyond the use outlined above—researchers using blogs to communicate their
work, primarily to peers, a wide variety of other approaches to science (or, more
broadly, scholarly) blogging exist, depending on communicators, target audience,
and function. For example, it is widely assumed that because they are public,
science blogs should be used to present the results of scientific research to a wider
audience. Often blogging is seen as a new component of science journalism which
is consequently something not just done by scientists, but also by journalists, or by
enthusiasts with knowledge in a given area of science (Bonetta 2007). Frequently
when the term science blogging (or scholarly blogging) is used, it is only
implicitly clear which kind of blogging is meant, the variety that complements
scholarly communication in journal articles and scholarly monographs, or the one
that complements science journalism. It seems likely that different variants will
continue to exist as a result of the freedom to innovate and create new genres
online. In the following I will briefly discuss two different science blogs as
examples of these different approaches in an attempt to underscore how blogging
complements the needs of scientists and science communicators (journalists,
activists, hobbyists) alike. While there is some overlap, it is important to be aware
of the different needs of these actors.
(Micro)Blogging Science? Notes on Potentials and Constraints 93
When approaching science blogs and an emergent communicative practice, it is
helpful to first outline the roles they play for different stakeholders in the eco-
system of scholarly communication. Tables 1 and 2 give an overview of the
various roles played by different actors, and of some of the motives of scientists
who blog for different reasons, respectively.
Case 1: Rosie Redfield (RRResearch)
RRResearch is the blog of Rosemarie (‘Rosie’) Redfield, a microbiologist at the
University of British Columbia, Vancouver, and head of the Redfield Lab at
UBC’s Department of Zoology. The blog was initially published on the com-
mercial service Blogspot, but has since then moved to the independent blog net-
work Field of Science4 which uses the Google Blogger platform5 as its technical
backbone but is maintained so as to feature high quality scientific content con-
tributed by experts from different fields.
Since August 2006, Redfield has posted over 900 entries on the blog, discussing
various issues of her research. Her initial post gives a good idea about the direction
of the blog:
Table 1 Examples of actors, audiences, and functions of science blogs
Actor Target audience Function Analogy
Lab leader in genetics Funders, general public provide rationale f. research
inform public & funders
Report
PhD student in physics Peers, senior researchers promote self
practice writing
Lab notebook
Science journalist General public explain science broadly
educate readers
Magazine piece
Table 2 Example motives of science bloggers
Motive A: Visibility Motive B: Networking Motive C: Information
Increase own impact Connect with peers Be up to date
Be found by peers and
other stakeholders
Stay in touch with colleagues Be part of a conversation
Present self/own work Be(come) part of a community Anticipate trends
4 http://www.fieldofscience.com/
5 http://www.blogger.com/
94 C. Puschmann
(from: http://rrresearch.fieldofscience.com/2006_08_01_archive.html)
While many posts are devoted to documenting and describing her research—
often, as emphasized in the post above, seemingly with herself in mind as reader,
quite a few touch related issues relevant to a less specialized audience. For
example, several early posts cover Bayesian statistics and discuss its use in
genetics research. Many posts are related to meta-issues in scientific work, i.e.
grant proposals, journal submissions, and other aspects that are part of work
processes at a genetics laboratory.
While Redfield’s blog was known to an expert audience before, she attained
major success as a result the post ‘‘Arsenic-associated bacteria (NASA’s claims)’’
(Redfield 2010) that strongly critiqued the paper ‘‘A Bacterium That Can Grow by
Using Arsenic Instead of Phosphorus’’ (Wolfe-Simon et al. 2010) which had been
previously published in the journal Science. In the blog post, Redfield initially
reports the findings of the paper and then proceeds with a detailed criticism of the
methodology used by the authors of the study. As in other entries, she mixes a
somewhat informal style with the vocabulary of a scientific paper. She also
includes numerous figures, illustrations, and references, making the post compa-
rable to a review in a scientific journal (Fig. 3).
The post received over 250 comments and a polished version was later published
by Science, though the original article was not retracted. Redfield’s success in using
her blog to voice her criticism, rather than using the traditional channels, was seen by
many as a turning point in the dynamics of science communication—a journal
widely recognized for its rigour saw itself forced to react to criticism posted in a blog.
RRResearch is the blog of a scientist, who accordingly uses it as part of a wider
communicative agenda. While most writing done by academics is geared towards
peers and written to withstand their scrutiny and criticism, writing a blog ‘‘for
oneself’’ amounts to a space where freer, less regimented expression is possible.
Redfield is, of course, aware that her blog is widely read, but its status as something
This is my first post to this new blog.
The purpose of keeping the blog is to give me a semi-public place to describe the
ongoing process of doing and thinking about my lab’s research. I hope I’ll use it
to describe/explain (mainly to myself) the scientific issues I’m thinking about:
• what experiments we’ve done
• what the results were if they worked (or possible explanations for why they
didn’t work)
• what I think the results mean for the questions we’re trying to answer
• what experiments I think we might do or should do when time and
resources permit.
The purpose of this post, however, is mainly to see what happens when I
click on ‘Publish Post’
(Micro)Blogging Science? Notes on Potentials and Constraints 95
other than a formally recognized publication is an asset, because it allows her to
address issues that wouldn’t generally fit into a formal publications. Yet RRRe-
search is also not a typical science blog in the sense that most journalists or science
educators would interpret the term—understanding much of what is published in it
presupposes in-depth knowledge of biochemistry and Redfield makes no attempt to
dumb down her writing to make it more palatable to a lay audience.
Case 2: Bora Zivkovic (A Blog Around the Clock)
Bora Zivkovic is a well-known blogger and science educator with a background in
veterinary medicine and biology. He teaches introductory biology at North Caroline
Wesleyan College, organizes ScienceOnline conference series, and has been a
visiting scholar at New York University’s Arthur L. Carter Journalism Institute.
Zivkovic started his site A Blog Around the Clock (ABATC) in 2006, after
moving from Blogger.com to ScienceBlogs. In 2011, the blog was moved again,
this time to Scientific American, where Zivkovic became the first Blog Editor.
After he took up blogging in 2002, Zivkovic gradually gained wide recognition as
a science blogger, not least because of the impressive volume of his activity. In the
time before moving from Blogger to ScienceBlogs alone, he produced a total of
2420 posts about a variety of topics. While frequently these are short texts pointing
to a news item, video, or other piece of information, many are detailed essays
about (broadly) science and politics, science and the general public, etc. His style
is not only less formal than that of Rosie Redfield, but he also uses considerably
Fig. 3 The first post published on RRResearch in August 2006
96 C. Puschmann
less scientific terminology. The considerable volume of content that flows through
ABATC make it a virtual popular science magazine, covering a breadth of issues
and formats (including blog carnivals and other outreach mechanisms that aim to
strengthen connections with other blogs). Zivkovic both relays information from
other sources, commenting on it and explaining it to readers, and provides longer
commentaries, for example on issues of science policy. He assumes that his
readers are interested in science, but does not generally presume in-depth
knowledge of scientific topics. This approach is in line with Zivkovic’s own
background: while he is a trained scientist and writes from a first-hand perspective,
his agenda is not that of someone paid for full-time research.
While RRResearch presents both the results of research (rarely) and frames sci-
entific issues for a scientific audience, ABATC translates scientific topics for a more
general, non-specialist audience. The issues are much broader there than they are in
RRResearch, where they align much more strongly with the blogger’s own research
interests. The latter blog is a window into the mind and daily work of the researcher,
not a friendly conversation with a lay audience. This is not to say that RRResearch
doesn’t engage—its success illustrates how well it achieves this goal—but whom it
targets as its readership and what function it wants to realize remains at least partially
unclear. Redfield uses her blog to frame issues for herself and her peers, while
Zivkovic blogs for a readership with their needs squarely in mind. Much of the
research that he relays is not his own, while much of what is discussed in RRResearch
is Redfield’s own work, or closely related to it. Whereas Redfield regards her blog as
an instrument for communicating what she is currently working on or issues she is
more generally interested in, Zivkovic provides a service and measures its success, at
least in part, by its popularity and the amount of feedback he receives, a form of
impact that may well be less relevant to a blogger like Redfield, who might be
primarily concerned with her blog’s reception among her students and peers (Fig. 4).
The Uses of Microblogs for Science: Two Scenarios
Compared to blogging, which has a history that reaches back to the beginning of
the Web itself, microblogs are still a relatively new form of communication.
Microblogs share with ‘‘normal’’ blogs the sequential organization of information
in dated entries, but they are usually constrained in length to facilitate scanning a
large number of posts rapidly. Another point of distinction is that microblogs are
typically centralized services rather than decentralized software packages that can
be run from one’s own webserver. Twitter is by far the most popular service,
though competitors exist, both related specifically to science and for general use.6
6 An example for a specialized microblogging for scientists is ScienceFeed, which is part of the
social networking functionality offered by ResearchGate, while App.net is an advertising-free
microblogging service that promises to put the interest’s of its (paying) members first.
(Micro)Blogging Science? Notes on Potentials and Constraints 97
As with blogs, the potential uses of microblogs for scholarly communication are
highly varied, ranging from virtual journal clubs (Reich 2011) and debates about
current, science-related events, to self-help for graduate students (for example,
under the #phdchat hashtag). Microblogs are also a way for scientists to stay up to
date about what their colleagues are working on, while at the same time providing
a window into current scientific research for science journalists and facilitating
interaction between scientists and the general public (Puschmann and Mahrt 2012).
The lack of a dividing line between scientists and non-scientists, as well as the
great variety of topics that even scientists tweet about mean that Twitter is not
comparable to the orderly world of science publishing, where every piece of
information is assumed to be relevant. Instead, a typical user’s timeline is likely to
be populated both by scholarly content and personal remarks, more or less side by
side. As the size of the network and the thematic broadness of Twitter is what
makes it interesting to most users, it seems unlikely that this ‘‘problem’’ will ever
be remedied at its core, but the ability to filter information from Twitter and similar
services is likely to resolve the issue.7 Tweets and other social media information
can congregate around a journal article or piece of data—an approach that may
also be beneficial for the development of dedicated science services. Such services
could eventually become a reality as the strengths of services like Twitter are at
once also a weakness: while timely, tweets are not accessible in the long term,
and increased brevity also means less nuanced information in each tweet.
Fig. 4 A Blog Around the Clock, Bora Zivkovic’s blog at the scientific American Blog network
7 As one example of a new approach to publishing powered by Twitter aggregation, see http://
digitalhumanitiesnow.org/
98 C. Puschmann
Wide proliferation and ease of use may eventually be offset by problems regarding
access to and long-term preservation of data. As with FriendFeed,8 which was
enthusiastically embraced by a small community of scientists, it is completely
unclear how Twitter will evolve and the concerns of academics are likely to matter
very little in respect to this. It is conceivable that policymakers will eventually put
into place an infrastructure that will support the kind of communication taking place
on Twitter, at least between scientists, rather than leaving vital issues to private
companies that do not have scientific issues at the center of their attention. While it
is impossible to tell how many scientists are already using Twitter and similar
services and in what ways, it is safe to say that the significance of microblogging
is growing, while its role for science communication continues to evolve
(cf. Puschmann and Mahrt 2012). In the following, two common scenarios for the
use of microblogs will be described in more detail: tweeting at scientific confer-
ences and using Twitter to cite papers in Open Access journals and repositories.
Case 1: Twitter at Conferences
Conferences are all about communication. When used in the context of scientific
conferences, Twitter acts as a backchannel, in other words, it complements what
happens at the conference itself, allowing attendees, and quite frequently also
people who are unable to attend, to comment, ask questions, and participate in the
discussion taking place. It is important to point out that this complements the face
to face activity, rather than replacing it. It is a major advantage that a talk can take
place uninterrupted while a lively discussion takes place about it on Twitter.
A drawback of this approach is that the presenter cannot participate in the debate
while it is underway and while being the the subject of discussion, sometimes also
criticism. The use of a Twitter wall, i.e. a projection of hashtagged tweets usually
shown next to or behind the presenter, can aggravate this problem. In November
2009, social media researcher Danah Boyd held a talk at the media industry event
WebExpo New York that was accompanied by a Twitter wall showing tweets
posted under the conference hashtag. As Boyd delivered her presentation, which
was beset by technical difficulties, she was the subject of intense polemical
remarks from spectators via Twitter; all the while, she herself could not see the
projection of the offensive tweets as she spoke. Though this kind of incident is
rare, it underlines the double-sidedness of a technology that is open and easy to
use, but therefore also easy to abuse under certain circumstances. Twitter walls,
apart from being a distraction, seem to add fairly little communicatively to the
overall conference, although their precise placement (e.g. in the lobby, rather the
main conference hall) seems a key issue to be aware of (Fig. 5).
8 http://friendfeed.com/
(Micro)Blogging Science? Notes on Potentials and Constraints 99
Examining the usage of Twitter during conferences, it is notable how specific the
usage of scientists is compared to users of different backgrounds, and that at the
same time microblogging is always more informal communication than traditional
publishing, not just because of its brevity. Rather than chatting idly, researchers
share information via Twitter—they point to papers and posters, to datasets online,
and to websites related to their research (Weller and Puschmann 2011). Passing on
(retweeting) this information is extremely popular, more so than just exchanging
pleasantries or gossip. At the same time, academics also link to the same resources
that other users do, such as online picture services such as Instagram or Twitpic or
video platforms like YouTube and Vimeo (Thelwall et al. 2012), and they link to a
variety of popular science content, i.e. science articles from newspapers (Weller
et al. 2011). The continuum between personal and professional is frequently blurred
on microblogging platforms. Conferences act as a sort of content filter—because
the event that a conference hashtag is associated with is all about a professional
activity, what is tweeted under the hashtag is usually related fairly closely to the
topic of the event, though meta-topics such as the conference program or pointers to
the venue of conference venue are also popular.
Fig. 5 Network visualization of retweets among users at the World Wide Web 2010 Conference
(#www2010), held in April 2010 in Raleigh, North Carolina
100 C. Puschmann
Case 2: Twitter for Citations
Beyond conferences, Twitter also plays an increasingly important role for day-to-
day communication among scientists. Academics are often interested in a variety
of topics outside of their specific field of research and accordingly tweet about
many things which are the not subject of their work or comment in ways that differ
from traditional scholarly communication. This highlights an issue of informal
digital communication online: it is extremely hard to determine what constitutes a
scientifically ‘‘valuable’’ contribution and what does not. While some tweets are
related to scholarly issues and others are obviously personal in nature, many
occupy a meso-level between what is traditionally considered scholarly content
and what usually is not. Counting every tweet mentioning a scholarly source as
scientifically valuable is obviously too simplistic, as is discarding every personal
remark as irrelevant.
This is a particularly salient issue because an increasing number of studies
examine the relevance of social media for scientometrics, in other words, the role
that social media can play in measuring and predicting the impact of scientific
research (e.g., Weller and Puschmann 2011; Eysenbach 2011). By conservative
estimates, popular sites such as arXiv received around 5,000 links per month9 and
this is bound to increase in the future. If the popular reception of scholarly liter-
ature among scientists and non-scientists alike via Twitter can be considered a form
of impact (and many agree that it can), this means that citations on Twitter and via
other channels may be introduced as a valid impact measure into the scientometric
toolkit in the future [cf. the suggestions of Priem et al. (2011) in this direction].
Who Uses Blogs and Microblogs for Scholarly
Communication, and Why?
In the environment of change outlined above, it is only logical to ask why new forms
of communication online—blogs, Twitter, social networks—haven’t proliferated to
a greater extent. If the examples of innovative usage of blogs and Twitter to
communicate among scientists and more broadly about science give a reason to be
optimistic, actual usage of such tools among scientists—defined here as the broad
base of academics employed for research and teaching at universities and research
institutes—should caution enthusiasts. International studies on the acceptance rate
of social media among scientists vary considerably in their results, but many suggest
widespread skepticism (cf. Procter et al. 2010; Bader et al. 2012).10 While pointing
to examples where new formats have succeeded is useful, it is also worth noting that
9 Author’s own estimate based on ongoing tracking of all tweets linking to the arXiv website.
10 But see Priem et al. (2011), who suggests that usage of Twitter is steadily growing.
(Micro)Blogging Science? Notes on Potentials and Constraints 101
scientists are conservative when it comes to embracing new technologies, both for
internal communication and in relation to new means of engaging with the general
public. This may be changing, but it seems important to consider both the much-
cited potential of social media for science communication and the reality of its
yet-nascent acceptance among faculty members—especially those in senior posi-
tions. For policymakers it is imperative to have an accurate picture of the situation
and the immediate future, beyond lofty promises. It is exceedingly likely that in
those areas where change is occurring because it is being driven, at least in part, by
researchers themselves, the changes will be more lasting than where new tech-
nologies are not well-integrated into established practices. Further factors able to
spur innovation are payback in the form of funding, increased reputation, and other
critical competitive aspects of institutional science. Yet it remains unproven
whether social media tools are essential to improving scholarly communication or
whether their usefulness is restricted to the margin of science and scholarship, rather
than extending to the center.
Two key components that could facilitate the success of social media tools
(blogging, microblogging, but also wikis and social networking sites for scientists)
are the spread of alternative means of measuring scientific impact beyond tradi-
tional bibliometric indicators (a) and the increasing adaptation of social media
formats for science and integration into ‘‘proper’’ scientific discourse (b). The
former is at the focus of innovations in scientometrics and initial suggestions are
likely to be made in the coming years to funders and research organizations about
how to measure impact more holistically, though it remains to be seen whether
established (and widely criticized) measures such as Thompson Scientific’s Impact
Factor (IF) can be displaced. In order to achieve the latter, the institutional ena-
blers of science communication—publishers, libraries, science organizations and
scholarly societies—will have to invent not only new technologies, but also re-
brand familiar labels that scientists rely on. The French site Hypotheses.org and
the lab platform OpenWetWare.org are examples of this approach: while the
former is a technically a blog platform based on the popular Wordpress software
and the latter is a wiki based on Wikimedia’s MediaWiki, both clearly present
themselves as pieces of scientific infrastructure, built for an academic audience.
Success in these approaches lies not in engaging with the ‘‘newness’’ of social
media to win skeptics over, but in promising that social media tools can be adapted
to achieve similar aims as were previously realized through other channels, only
quicker, cheaper and with broader effect.
The current consensus among scientists appears to be that blogs and Twitter are
somewhat interesting to promote one’s own research (to journalists and perhaps a
few colleagues), and more broadly, one’s field (to potential students, the general
public), but that the payoff is not always worth the time and effort (Bader et al.
2012). If science was solely concerned with getting scholarly content across to as
many people as possible, blogs would have displaced the established system of
academic publishing by now, but it is no coincidence that the journal article has not
been abandoned in favor of the blog post. In addition to overall conservatism, the
lack of peer review in social media channels also hampers its adoption as a
102 C. Puschmann
replacement for traditional publications. Scholarly content, regardless of the dis-
cipline, must be valorized by the judgement of others, and frequently only after the
criticism of peers has been taken into account and the original manuscript has been
adjusted is a piece of writing deemed a genuine scholarly publication. Time is the
scarcest resource in research and investing it in an activity of peripheral importance
is widely regarded as wasteful. Taking the extreme goal-orientedness of scholarly
communication into account is essential in understanding the perceived advantages
and disadvantages of social media in the minds of many scientists (Table 3).
Conclusion
A comparably small number of people across the globe actively works on complex
scientific issues, communicating through channels and genres established over the
course of decades, or in some cases centuries, which have been carefully designed
to suit the needs of the respective communities. How can those on the outside
reasonably argue for the need to profoundly change such a system without pro-
fessing their own status as outsiders? The underlying claim of those challenging
science to be more open is that it is closed to begin with, a perception not uni-
versally shared by scientists. Those who espouse the view that social media should
be used to discuss scientific research tend to fall into one of either two camps:
adaptionists or revolutionaries. Adaptionists believe that social media tools need to
suit researchers needs in doing what they are already doing. Hard adaptationists
believe that new formats should replace established ones because they are more
efficient, cheaper, faster, and better than the established formats of institutionalized
academia (e.g. that blog posts should replace journal articles). Soft adaptionists
believe that new forms should augment existing ones, often filling unaddressed
needs. A soft adaptionist would use Twitter to promote his research, but not
Table 3 Advantages and disadvantages of blogging and microblogging
Blogs Twitter
+ – + –
Rapidly
disseminate
content
Lack of formal recognition Communicate with
colleagues
Time-consuming
Cheap Lack of prestige in the
scientific community
Promote your
research
Benefits unclear
Easy to use No clear topical focus Disseminate
information
Increased self-
exposure
Open to
individuals
Time-consuming Build personal
influence
Not sufficiently
informative
Promotional
tool
Long-term availability
unclear
Stay up to date about
your field
Perceived as
trivial
(Micro)Blogging Science? Notes on Potentials and Constraints 103
publish a paper in his blog rather than Nature. In practice, most adaptionists
probably act as soft adaptionists, but some would prefer to follow the hard,
uncompromising route if they could. Adaptionists have in common the basic belief
in the legitimacy and relevance of the existing system of institutional science, but
see it as being in need of reform. They believe that certain aspects of the system
need change, but are convinced of its overall soundness. Revolutionaries, by
contrast, call more than just specific aspects of the system (e.g. publishing) into
question, being, in fact, opposed to the system as such, which they perceive as
elitist and deeply flawed. While to the adaptationists science is fundamentally
open, it is fundamentally closed to the revolutionaries, who are rarely themselves
part of the entrenched academic system, but tend to be either junior faculty
members or amateurs. Whereas the adaptationists have been co-opted to varying
degrees to uphold the established order, the revolutionaries imagine a future in
which the the entrenched system is overturned. Though the latter seems much less
likely than the former, both groups actively advance the significance of social
media for science, in spite of widespread inertia on the part of much of the
academic establishment.
It has yet to be seen how exactly blogs and microblogs will fit into the existing
ecosystem of scholarly publishing. Their role could be complementary, providing
an outlet for purposes which traditional publishing does not address—from
reflections about teaching to the promotion of a researcher’s work. Miscellaneous
writing that does not fit into recognized publications however is strongly contin-
gent on the time that a researcher has at their disposal. Blogging on a regular basis
is time-consuming, therefore it is likely that full-time academics will actively blog
only if they find it benefits their career. In the end, blogs and microblogs sup-
plement, rather than replace, traditional formats, and act as tools for the promotion
of one’s research, rather than tokens of prestige and academic excellence.
Changing blogs in order to make them functionally equivalent to recognized
formal publications would mean changing them to a degree that could nullify their
benefits (for example, by introducing peer review). Instead, they have a place in
the larger ecosystem of science communication 2.0 which includes protocols
(OpenWetWare) and workflows (myExperiment) as examples of entirely new
scientific genres which are functionally different from blog posts.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Academia Goes Facebook? The Potential
of Social Network Sites in the Scholarly
Realm
Michael Nentwich and René König
This network is the seat of scientific opinion which is not held
by any single human brain, but which is split into thousands of
different fragments… each of whom endorses the other’s
opinion at second hand, by relying on the consensual chains
which link him to all the others through a sequence of
overlapping neighborhoods.
—Michael Polanyi (1962) in the Republic of Science
Abstract Social network sites (SNS) have not only become a fundamental part of
the Web, but also increasingly offer novel communicative and networking possi-
bilities for academia. Following a short presentation of the typical functions of
(science-specific) SNS, we firstly present the state of knowledge regarding aca-
demic usage practices, both in general purpose SNS and in science-specific SNS.
Secondly, we assess potential impacts by addressing identified key issues such as
privacy, the role of pseudonymity, and the specific form of informal communi-
cation in question. In particular, we focus on the issue of network effects and the
challenge of multiple channels, which presents itself as a major hurdle for an
effective implementation of SNS in academia. Despite these difficulties, we come
to the conclusion that SNS are, in principle, functional for scholarly communi-
cation and that they have serious potential within academia.
M. Nentwich (&)
Institute of Technology Assessment, Austrian Academy of Sciences,
Strohgasse 45/5 1030 Vienna, Austria
e-mail: mnent@oeaw.ac.at
R. König
Institute of Technology Assessment and Systems Analysis, Karlsruhe Institute of
Technology, PF 3640 76021 Karlsruhe, Germany
e-mail: rene.koenig@kit.edu
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_7,  The Author(s) 2014
107
Introduction
Starting approximately around the year 2000, a growing number of social network
sites (SNS) began populating the Internet, offering novel communicative possi-
bilities; above all they link-up its members and map their offline networks. As this
seemed to offer an attractive potential for academic communication as well, from
the mid-2000s onwards, with a certain peak in 2007/2008, science-specific SNS
also entered the market, both disciplinary-focused ones (like AtmosPeer or Edu-
meres) and more general examples with increasingly large numbers of members
(like ResearchGate, Mendeley, Academia.edu). Most SNS provide a central web-
based platform which cannot itself be modified by users. Some services give more
options for this, for example Ning, which allows for the design of SNS for specific
needs within the framework of the software. Vivo, a US-based science-specific
SNS, offers even more flexibility as its software is open source and can be hosted
on local servers.
Due to their manifold functions and complexity, various definitions exist of
what a SNS constitutes (e.g. Mack et al. 2007; Richter and Koch 2007; Schmidt
2009; Boyd and Ellison 2007; Beer 2008; Fuchs 2009). As SNS have multiple
functions, it is difficult to impose a selective definition of these; hence, it depends
on the specific definition as to whether a platform will be here counted as a SNS.
Following Schmidt (2009), we base our definition for this chapter on the possi-
bility of setting up a sophisticated personal ‘profile’ with information about one-
self, such as interests and activities, within a digital space that can usually only be
reached after registration. Starting from this profile, users initiate and entertain
social relationships with others, making them explicit through interlinking; the
members interact and navigate on the platform, which is basically formed by these
networks of ‘contacts’. Focusing on the central function of profiles enables us to
distinguish SNS from other services: Networking alone is also a characteristic of
other platforms that are typically not seen as SNS such as the voice-over-IP service
Skype or the microblogging service Twitter. As for the latter, the profiles are
minimalist and the timeline of messages, not the profile, is at the center of the
platform (see chapter C(Micro)Blogging Science? Notes on Potentials and
Constraints of New Forms of Scholarly Communication). Similarly, online ref-
erence management platforms are organized around publications (see chapter
Reference Management). We observe, however, that also in these other services
increasingly SNS-like functions are added, so that the distinction is dynamic and
not clear-cut.
Even among the SNS in the narrow sense, there are many differences, in par-
ticular when it comes to the available communication tools or how users can
configure their profiles. Two core functions are always present: identity manage-
ment and contact management (cf. Richter and Koch 2007). The profiles map—
more or less in the public domain—the contacts of a person and enable access to
further members on various paths, i.e. networking.
108 M. Nentwich and R. König
As the technical functionality and target groups vary, we may distinguish dif-
ferent types of SNS: There are variations according to the intended usage forms. In
some SNS, private purposes prevail, in others professional fields of application
dominate; furthermore, in others private and professional use overlap. Require-
ments for access also vary: some are open, that is, they only require a simple
registration which, in principle, can be done by all Internet users (cf. Richter and
Koch 2008). This is the case with many popular SNS. Other platforms offer limited
free access, but charge user fees for the full service. However, most platforms are
free of charge in order to attract a sufficient audience and paying advertisers (see
section ‘‘Assessing the Potential Future and Impacts of SNS in Academia’’ for a
discussion of the problems related to such business models). Finally, there are
specialized networks that are open only for certain communities, such as a com-
pany or research group. The available communication forms vary according to
different needs. For example, to nudge someone online is used in a private context,
whereas many professional networks offer additional functions such as biblio-
graphic searching (see section ‘‘Typical Functions of SNS’’).
In this chapter we discuss SNS only from the viewpoint of use in academia,
based on, but extending the analysis in our book Cyberscience 2.0 (Nentwich and
König 2012, pp. 19–50). Following a short overview on types of SNS, their typical
functions, academic potential, and observable user practices, we focus on a few
key issues that are essential for answering our title question, namely whether
academia will indeed ‘‘go Facebook’’, that is, whether future communication
among scholars will take place predominantly on these platforms, or even on one
single dominant platform. These issues include privacy, the role of pseudonymity,
and the specific form of informal communication in question. In particular, we
focus on the challenge of multiple channels, which presents itself as a major hurdle
for an effective implementation of SNS in academia.
An Overview of Functions, Potential, and Usage Practices
Typical Functions of SNS
Various functions and forms of communication are typical for SNS, though not all
of them are necessarily available in each individual SNS. Science-specific SNS in
particular try to develop technologies which meet the requirements of their par-
ticular audience.
1. Profiles: User profiles are digital representations of users and as such the central
nodes of SNS. Various kinds of information can be made available to other
members in a pre-structured way, from contact information to tracking of user
activities. In some SNS it is also possible to have specific profiles for organi-
zations. Thus profiles are like enhanced calling cards of individuals, organi-
zations, and groups. Some SNS experiment with special scores to automatically
Academia Goes Facebook? 109
rate user activity on the basis of their activity in the SNS, thereby creating a
potential metric for reputation (e.g. RG Score in ResearchGate, see Fig. 1).
2. Communication: The integration of multiple communication channels within
one platform is a distinctive feature of SNS, as compared to various other web-
based communication tools. Various tools are available in order to commu-
nicate with other members: messaging, chatting, discussion forums/groups,
microblogging, nudging, videoconferencing, etc.
3. Networking: As networking is one of the basic functions of SNS, all sites offer
various tools to promote it: contacts/friends, automated or manual propositions
for further contacts, search functions (partly also automated), invitations,
bookmarking of profiles, automatically generated requests to welcome new
members or propose something or someone to them, and network presentation
in various forms.
4. Directing attention: The great variety of opportunities to communicate and
network in SNS suggests further tools to establish the relevance of content and
to direct the attention of its members towards particular items: current issues on
start page, external notifications (via e-mail), and the ‘‘Like this’’ button/
‘‘Share this’’ function. These data may be used in the future as indicators for
relevance, discussed under the label of social search (e.g. Biermann 2010).
5. Groups: All users can found thematic groups. By usually offering the following
functions, groups enable the detection of and networking with members with
similar interests and they provide a digital environment for discussion and
collaboration: discussion forum, file upload, collaborative writing environ-
ments, tools to administer participants in events, selective access to groups,
passive membership. Sometimes group-like options are labeled differently, e.g.
‘‘topics’’/‘‘projects’’ in ResearchGate.
Fig. 1 Excerpt of an RG score (screenshot)
110 M. Nentwich and R. König
6. Calendar: Some SNS offer their users calendars in order to coordinate dates,
plan, and market events of all kinds.
7. Literature-related functions: Given the central position of publications in
academia, science-specific SNS also offer a number of literature-related func-
tions: searching for academic literature by giving access to other, external,
mainly Open-Access databases, as well as internally in the publication lists and
database entries of members; similar abstract search; compiling bibliographies;
Open Access archive; various attention direction services like notifications,
based on topicality, semantic relationships, ‘‘Have read’’ buttons, commenting
or rating, ‘‘Share this’’ function, access statistics, and visualization of networks
of co-authors.
8. Further services: In addition to these functions, further specialized and target-
group-specific services are offered: job exchange services, blogging, embed-
ding of services of external providers via apps (thus creating interfaces between
the SNS and other services), and advertisement.
Given this broad variety of functions, services, and tools provided by SNS, one
is tempted to consider SNS an umbrella for all kinds of features that Cyberscience
2.0—or Science 2.0—offer to the current and next generations of academics. From
a technical point of view, this is a viable assessment. However, this is only a vision
which still needs to be realized in practice, given some of the challenges addressed
below.
Potentials of SNS for Science and Research
Our systematic analysis of the potential for academic SNS use starts with the four
core areas of scientific activity (Nentwich 2003, p. 24) and reveals that SNS
provide functions for all these areas, namely knowledge production, processing
and distribution, as well as institutional settings. The various functions of directing
attention may be helpful in the process of acquiring information, particularly with
regard to literature. Shared data archives potentially help working groups to
administer their files. As the multiple possibilities for communication are the core
of each SNS, they are, at least from a technical perspective, functional for aca-
demic communication as well. Through the various channels, knowledge can be
presented and offered for academic discourse. The group functions may support
collaboration. However, SNS are not currently an adequate place for publication,
even though in principle documents may be published within the network, while
access to documents is hampered, as it is usually necessary to register (which also
hinders indexing the items in external search-engines). In addition, there is no
formalized peer-review process in any of the observed SNS, thus the knowledge
distributed via this channel will not be reputable, and less relevant to many. Hence,
publication within SNS seem inappropriate at the moment. However, this could
potentially change if thorough peer-review mechanisms are established. In
Academia Goes Facebook? 111
contrast, SNS may be a valuable additional channel for pointing to texts that have
been published elsewhere. They provide a number of functions in that respect:
profiles, means of internal communication, tools to direct attention, group func-
tions, and literature-related services. SNS may also be used as e-learning platforms
and at the organizational level they are potentially useful as a digital infrastructure.
For example, SNS serve as a dynamic list of ‘‘digital calling cards’’ and may help
to set up networks of scientists with similar interests as a pool of potential
cooperation and communication partners. The popular general-purpose SNS such
as Facebook appear especially suitable for public relations, academic organiza-
tions such as research institutes, universities, scholarly associations, and networks,
as well as for individual researchers.
In line with the multiple services SNS offer, different user practices are con-
ceivable. SNS could serve multilaterally as a discussion forum or as a platform for
exchanging information, similar to other web forums and in particular e-mail list-
servers. Furthermore, they may be used as bilateral communication channels,
asynchronously via webmail or synchronously as chatting platforms. SNS may
serve as platforms for (micro-)blogging to exchange science-related or day-to-day
information, and for e-learning, and one can easily imagine more options for their
scholarly application. Thus we note that SNS seem to be functional for a number
of essential academic activities.
Academic Usage Practices in SNS
As of yet, there are only very few, and mostly limited studies of how academics
actually use SNS in practice. Therefore, the following account is based both on
these few studies and the authors’ own experiences and participatory observations
over the last few years.
The Diffusion of SNS in Academia and the Intensity
of Usage
The number of members, mostly published by the SNS themselves, and their
growth rates is a first indication for the diffusion of SNS in the academic world.
ResearchGate, for example, had 150.000 members in August 2008, 700.000 in
December 2010, announced a million by May 2011, and reached two million in
September 2012. We observed similar growth in other SNS, but the figures may
not hold in practice, given their origin. The member count, in any case, does not
necessarily correlate with actual use, because there certainly are some (partly)
inactive accounts. Therefore, more differentiated usage studies would be needed.
Existing studies provide only first insights into the diffusion of SNS among
112 M. Nentwich and R. König
scientists and students (Koch and Moskaliuk 2009; Kleimann et al. 2008; Procter
et al. 2010; Bader et al. 2012): They generally show a rather low active usage,
although the diffusion appears partly higher among young academics. The out-
comes of such studies also vary depending on the exact target group and design of
the surveys. We may expect that the proportion of SNS users among scientists will
increase as the younger generations move up in academia.
We observed the following types of activity levels and usage intensities and
propose to differentiate usage in future studies accordingly:
1. Me-too presence: Rudimentary profile; only occasional contacts and never, or
only sporadically, become active—probably the most frequent case.
2. Digital calling card: More detailed profile like a type of additional personal
homepage; practically no further activity—probably the second most frequent
case at the moment.
3. Passive networking: Searching the network in the beginning and thereafter in
irregular intervals for other (previously known) members, reacting to auto-
mated suggestions to contact other users, sporadic communication with other
members.
4. Active networking and communication: Being regularly online, using further
services, such as publication search, and participating in group forums, actively
searching for potential networking partners beyond those they already know.
5. Cyberentrepreneurship (Nentwich 2003, 175ff.): Not only active participants in
the network, but also serving as moderators or animators of group forums,
administering groups, are in charge of institutional profiles, giving feedback to
the site developers—obviously the rarest form of participation of researchers in
SNS.
These are certainly ideal types and in practice appear mixed. We observed
repeatedly the above usage types, but cannot offer results regarding their precise
empirical distribution. There may also be activities which are not observable from
an outside perspective, e.g. private messaging. In any case we need to consider that
the activity levels and usage types vary considerably. Consequently, member
counts do not lead to insights into the vitality of a SNS. This is confirmed by the
study of Procter et al. (2010) on the scientific use of Web 2.0: only 13 % of the
participants fall into the category ‘‘frequent users’’, 45 % are ‘‘occasional users’’,
and 39 % do not actively use Web 2.0. A large qualitative study with 160 inter-
views and focus group discussions with US researchers (Harley et al. 2010) notes
that SNS are not widely used in academia, with some exceptions.
Despite impressive and growing member counts, we may nevertheless draw the
conclusion that SNS are not yet part of the academic mainstream. Given the
theoretical potential (see above) we hypothesize that the trend will presumably
continue in the future. On the question as to whether scientific SNS may reach the
tipping point, see the section ‘‘The Hard Way to the Critical Mass’’.
Academia Goes Facebook? 113
Academic Usage Practices in Science-Specific SNS
Many academics have become members of multi-purpose SNS, such as Facebook,
LinkedIn, and Xing, not least because they are widespread and accepted. Usage
practices are heterogeneous because these SNS are not particularly focused on
academic users; among them we found communication with colleagues, e-teach-
ing, public relations of research institutes, and self-marketing, as well as job
exchange. While we have analyzed those practices elsewhere in more depth
(Nentwich and König 2012, 38ff.)—our main conclusion being that general SNS
play a minor role in the practice of research communication as of yet—we focus in
this chapter on science-specific SNS.
We found the following main practices:
Communication and cooperation: One specific strength of science-specific SNS
could be their potential to support communication and cooperation among
researchers. We did not observe, however, many efficient and successful working
groups in these SNS as of yet (they may, however, exist, but are hidden from
outsiders). A few self-experiments in our own area have been only modestly
successful. They mainly failed because of a lack of potential cooperation partners
inside the chosen SNS as we were not able to motivate all relevant colleagues to
actively participate in that particular SNS. In contrast to the attraction of large
general SNS, the rather young and small science-specific networks suffer from a
lack of sufficient numbers of active users (see the section ‘‘The Hard Way to the
Critical Mass’’). In addition, we observed the following further obstacles: technical
limitations, lacking experience of users, skepticism regarding file security, the
need to firstly develop a common culture of online collaboration, and, finally and
notably, the problem of multiple channels (see the section ‘‘Multiple Channels,
Information Overload and Filtering’’).
Public relations and self-marketing: Because of their limited target group
(mainly peers), these SNS are of limited use for public relations, as you can hardly
reach larger groups outside of the science communities in question. In contrast, it
is potentially easier to target workers in particular fields by means of the
sophisticated mechanisms for networking that most SNS provide. Similarly to
general SNS, self-marketing is also possible in science-specific SNS when focused
on one’s peer group, for example, by drawing attention to one’s own publications.
Based on our observations, this is currently probably the most widely used activity.
There is already a very high coverage of publications in certain fields in SNS like
Mendeley (Li et al. 2011; Bar-Ilan 2012).
E-teaching: We did not observe that science-specific SNS are frequently used in
teaching. There are specific functions to support it, for instance in research.iver-
sity, but there is not much known yet about their actual usage. Obviously, these
professional networks do not seem particularly attractive to students, in contrast to
Facebook and other general SNS, because they fit less well with their day-to-day
needs and more with the workaday life of a scientist. Hence, students are hardly
reachable via this channel, except in experimental settings. However, certain
114 M. Nentwich and R. König
effective student-orientated platforms exist, for example Carnets2 Descartes at
Paris Descartes University. In any case, SNS may turn out being a good platform
for exchange among teaching scientists when they prepare their courses.
Job exchange: These services have the advantage of having a preselected target
group in science-specific SNS as opposed to general ones. In September 2012, for
example, we found more than 1.000 job offers on ResearchGate, mainly from
biomedical enterprises, and some 130 on Academia.edu. The extent to which these
job exchanges are actually used is unknown to us.
Assessing the Potential Future and Impacts of SNS
in Academia
Will ever more and, perhaps at some point, most academics use SNS as they use
e-mail today? What consequences may this have? In order to answer these
questions, we will focus on the following puzzles: How important are network
effects and will science-specific SNS reach the tipping point (section ‘‘The Hard
Way to the Critical Mass’’)? What role will the very big players play in that game
(section ‘‘The Hyper-Critical Mass: Too Big to Fail?’’)? Is the necessity to observe
multiple channels in parallel possibly dysfunctional for science communication
and will the trend towards multi-functionality and one-stop-services generate the
necessary network effects (section ‘‘Multiple Channels, Information Overload and
Filtering’’)? And finally (section ‘‘Social Issues’’): What potential do SNS have for
informal communication among academics, and with what effects? What roles do
identity, pseudonymity, and anonymity play in scientific SNS? Which privacy-
related conflicts occur?
The Hard Way to the Critical Mass
Unlike many other Internet technologies, SNS necessarily require a certain critical
mass of active users to be functional. This leaves them in a dilemma: They are
only attractive with users, but users only come when they are attractive. SNS
providers use different strategies to overcome this issue. Of course, they try to
develop an infrastructure which is at least potentially useful once their target group
has populated the platform. This might attract a number of early adopters who will
eventually lure further users until a critical mass has been reached. While this
strategy has worked for many Internet start-ups, it is not easily applicable for SNS.
The problem here is that new members will only understand the early adopters’
attraction once they have built up their own networks within the platform.
Therefore, the effective usage of SNS requires a critical mass of users both on a
global and also on an individual level. Even a highly populated platform can be
dysfunctional for a single user if it does not represent people from his individual
Academia Goes Facebook? 115
network or potentially relevant users for future interactions. Building individual
networks takes time and effort, while the possible benefits of such an investment
are not immediately clear to new users. This might be one of the reasons for the
hesitant scholarly usage of SNS.
Another strategy to reach a critical mass can be to minimize the ‘‘mass’’ by
addressing a smaller and thus easier reachable target group. Facebook can be seen
as a successful example here, as it first aimed only at the Ivy League universities
before it was opened up for larger audiences. Carnets2 Descartes is a popular
example from the academic realm, focusing on students at the Descartes Uni-
versity in Paris. Although we are not aware of plans to reach beyond this circle,
this example shows how a platform can become relevant for a limited audience.
Another possible limitation for the academic realm is of course the focus on
specific disciplines or even smaller thematic or organizational entities, as it is done
by a number of academic SNS. However, smaller target groups can hardly be seen
as a general way to success. For example, once the market leader of SNS in
Germany, StudiVZ is now struggling with vast numbers of users migrating to
Facebook. One reason for this is probably that it failed to address an international
audience—in contrast to its main competitor.
Finally, a critical mass of scholars using SNS might be achieved by creating
extrinsic incentives. Indirectly, this could be done with altmetrics which extend
academic impact assessment beyond the established scientometrics by tracing SNS
activities. If relevant institutions acknowledge scholarly engagement on these
platforms via such measurements, it would certainly increase the participation of
academics in SNS. Directly, incentives could be created by faculties and univer-
sities themselves: To a large extent, it is in their hands as to which software should
be used for the various researching and teaching activities.
Evidently, such incentives need to be designed carefully and can easily result in
unwanted consequences. For instance, altmetrics might get manipulated by
‘‘buying’’ friends, comments, recommendations, etc. There are already numerous
companies offering such services for commercial users, which could in principle
be used for academic users as well. Also, scholars or students could revolt against
being pushed into SNS by their organizations. In particular, Facebook with its
privacy issues seems problematic here. Indeed, there were reports on students
rejecting approaches on Facebook from their university libraries due to privacy
concerns (Connell 2008; Mendez et al. 2009). This points us to another issue:
While SNS are undoubtedly dysfunctional without a critical mass, the mass itself
can become problematic when it reaches a certain size.
The Hyper-Critical Mass: Too Big to Fail?
Often Facebook is compared to nations or even whole continents in order to
illustrate its massive size. Indeed, the platform does not only outnumber most
countries’ populations, but also has an enormous economic power and impact on
116 M. Nentwich and R. König
various realms of modern societies. Therefore, one could argue that Facebook has
not just reached the critical mass to survive, but a hyper-critical mass, overpow-
ering its competitors. While the concentration of users in principle serves the
functionality of a SNS and thereby also its users, the power which is accumulated
in this way has to be critically observed. On a general level, this has been already a
subject of intensive public as well as academic debates which we cannot discuss
here in detail. The main concern seems to be privacy-related: Facebook gathers
vast amount of data about personal lives and gives its users only very opaque
options to control what can be seen by others, including external companies which
may have access to this data. This is also a major obstacle for the academic usage
of Facebook. On the one hand, the technical hurdle for the academic usage of
Facebook is very low for users who already apply the platform for their private
lives on a daily basis. On the other hand, this may be exactly what holds them back
if they do not wish to blend their professional and their private lives in this way. At
the same time, some researchers are reluctant to disseminate work in progress as
they feel it is imperfect or because they fear competitiveness (Harley et al. 2010,
p. 13). Although this is a general issue of academic social media usage, it is
particularly pressing when a company’s business model depends on the exploi-
tation of user data as in the case of Facebook or Google.
Moreover, once a SNS has reached a hyper-critical mass, it creates new
dependencies, as it works like a black hole: The bigger it gets, the more people are
drawn to it, the more content is produced, and so on. Therefore, it becomes
increasingly difficult to separate from it. A lot of time has been invested in learning
to operate the platform, building networks, discussing issues, editing profiles,
creating publication lists, tagging items, etc. Most of this data and effort cannot
easily be extracted and imported into another platform, so the hurdle to leave it
becomes higher and higher, especially if it is still frequently used by other relevant
communication partners. Then it may be (perceived as a) significant part of an
individual’s or organization’s social capital, leading to low mobility of users from
social network sites with a hyper-critical mass, creating a self-stabilizing force in
turn. This partially explains the ‘‘seductive power’’ (Oosthuyzen 2012) of such
sites, making users stay even if they do not agree with its policies or are unsatisfied
with its services. At the same time, it is very difficult for alternative SNS providers
to compete with this accumulated power which also attracts third-party services
like apps.
So, although the functionality of a SNS is increased when it concentrates a vast
number of users, such a monopoly-like market situation comes at a price. In the
academic context, this is particularly troubling when the SNS is outside the
influence of the academic realm. Commercial providers like Facebook do not only
have a questionable privacy policy, they also hardly let users participate in the
platform design—especially when it comes to such specific needs as scientific
ones. Science-specific SNS are more likely to take this into account but they are
still mostly in the hand of companies who ultimately decide about the platform’s
design, policy, and existence. The worst case scenario is, of course, that a service
is shut down because it is not profitable or does not fit anymore to a company’s
Academia Goes Facebook? 117
strategy. This is not just a hypothetical scenario: For instance, users of the blog-
ging and networking platform Posterous could only helplessly witness as the
company was acquired by its competitor Twitter, apparently just to let it die slowly
and benefit from its staff’s competence. In principle, this dependency also applies
to non-commercial developers, although they are less driven by the needs of
paying clients. They also might change their business model, as it was done in the
case of Couchsurfing or StudiVZ, both SNS which started as non-profits but were
later commercialized. Therefore, academic institutions should choose a platform
carefully if they plan for a sustainable organized engagement. This may be
challenging as a platform with a hyper-critical mass can be tempting, especially
because of the difficulties in reaching a critical mass on competing platforms.
Multiple Channels, Information Overload and Filtering
The current status of SNS in the scholarly realm is confusing: As pointed out
above, there is no clear market leader with a sufficient critical mass of active
scientists yet. Therefore, interested scholars are confronted with multiple potential
platforms that they can choose from. Establishing and maintaining various SNS
profiles is a time-consuming task, so most academics will rather select only one or
a few than be active on various platforms at the same time. This means that the
potential scholarly SNS users are spread over numerous services, instead of
concentrating on one or two. At the same time, the present ‘‘cyberscientists 2.0’’
who actually use various platforms simultaneously have to face the challenges of
these multiple channels: Maintaining their presences already becomes a time-
consuming task under these circumstances. Partly, interoperability across different
platforms via APIs solves this problem. For example, a number of social media
tools allow the sending of one status update to several platforms. Yet such options
are still very limited and not supported by all SNS. Due to the competition between
the providers, it is unlikely that this will change fundamentally soon. Apart from
maintaining multiple profiles, cyberscientists 2.0 also need to observe diverse
channels. Even within one platform this can be a confusing task: Communication
takes place in many virtual locations, e.g. via messaging, chatting, group con-
versations, or commenting. So far, SNS hardly provide options to effectively
organize this stream of information, especially when it comes to archiving and
retrieving older bits of communication. At the same time, the ongoing news stream
via multiple channels can easily overwhelm SNS users, resulting in an information
overload. Although the fear of such an information overload was already expressed
decades ago (Toffler 1970), some Internet critiques regard this as a pressing issue
of the fragmented and hyperlinked structure of the WWW, possibly swamping our
neuronal capacities and hindering deeper and coherent thoughts (Carr 2010;
Schirrmacher 2009; Spitzer 2012). Moreover, we need to remember that SNS only
add up to the already existing communication channels—from other social media
platforms to e-mail, telephone, and many more.
118 M. Nentwich and R. König
Partly, users will cope with the challenge of information overload by devel-
oping specific strategies. For example, they may limit their practices to intentional
active usage, which is less likely to lead to endless distraction through non-tar-
geted passive usage. One could also argue in favor of SNS that one of the main
ideas of these services is an effective information selection by people who we trust.
However, as soon as one’s digital social network reaches a certain size, it becomes
extremely time-consuming if not impossible to follow it. Then additional filter
mechanisms are needed. In sophisticated platforms these are organized in the
background through algorithms hidden from the user. While this creates additional
opacity, the selection of information through peers within SNS per se leads to an
individual bias depending on one’s networks. The common argument in favor of
this novel way of personalized gatekeeping is that it is more likely to deliver
content which is relevant to the user. Critiques, however, fear it will lead us into a
distorted ‘‘filter bubble’’ (Pariser 2011), lacking diversity and serendipity. In the
first place, this is a concern for the public sphere. Yet we may also wonder what
impact these new filter mechanisms will have upon the academic realm. Will this
work against the general tendency of blurring (disciplinary) boundaries in the
context of fluid digital networks? Might this re-define scholarly relevance and
visibility to a certain extent? Will new metrics such as ResearchGate’s RG Score
(see Fig. 1 above) one day become serious competition for established sciento-
metrics? On the one hand, the current low penetration of SNS into the academic
sector does not make these questions appear very urgent. On the other hand, these
questions are highly relevant to those scholars who already use the emerging
platforms. They change the way scientists interact and exchange information.
Since this differs according to the individual digital networks and the phenomenon
has not yet fulfilled its whole potential, one can hardly draw broader conclusions
on the exact impact of these developments at this point.
Social Issues
Informal Academic Communication 2.0. By offering multiple electronic paths to
reach and chat with members of the research community, SNS increase the pos-
sibility and likelihood of informal communication. Fully-fledged Cyberscience
2.0—or Science 2.0—would certainly look different to today’s interim state-of-
affairs. It may be characterized by massive, ubiquitous, possibly transdisciplinary
micro-communication among academics, and with interested lay observers
(Nentwich and König 2012, p. 200f.). This is already happening in certain niches
with very active academic cyberentrepreneurs, but it is anything but the norm as of
yet. It will be interesting to see what impact this may have on the structure of the
science system as it becomes more common. We may also ask whether SNS may
contribute to formalize the informal by making social networks of researchers—
the so-called ‘‘invisible colleges’’ (Crane 1972)—more transparent. Depending on
Academia Goes Facebook? 119
factors like privacy settings and the chosen communication channels, SNS partly
reveal who is connected to whom and how closely.
The Ambiguous Roles of Identity, Pseudonymity and Anonymity. With regard to
private use of SNS, pseudonymity instead of having a profile with one’s real
identity is frequently practiced, though discouraged. Thus it is possible to differ-
entiate between different roles. Anonymous accounts are usually not possible. By
contrast, in professional SNS which often also serve as public calling card
directories, pseudonymity would be counter-productive because the users need to
get in touch with ‘‘real’’ people. Similarly, pseudonymity is mostly dysfunctional
in academia. Science communication rests on the premise that you communicate,
whatever the medium, with actual persons in order to be able to cooperate or co-
author. In other words, merits need to be attributable: researchers definitely expect
that behind a profile in a SNS is another researcher who has actually written the
papers listed in the publications attached to the profile. Some SNS try to guarantee
this by verifying the identity on registration (e.g. BestThinking). In most cases,
researchers also desire to be recognized in order to better establish themselves and
increase their reputation. However, there are two cases where temporal or func-
tional anonymity is in the interest of academia: In many fields, the peer-review
process is usually double-blind. We may conceive that also the various rating
systems within SNS, most of which are not anonymous as of yet, may be
implemented in a way that allows anonymous rating. The other case is when it
comes to testing new ideas in a creative forum space or during collective brain-
storming. Here it may fuel creativity when the relation between callow thoughts
and the originator would not be registered permanently in a written archive. For
many cases, it seems desirable to create several personal ‘‘micro-publics’’ which
may overlap, but ‘‘allow for distinct foci’’ (Barbour and Marshall 2012), e.g. in
order to address different fields and audiences, such as peers and students.
Is Privacy an Issue in SNS? Mixing private and professional roles is an obvious
problem in general SNS (like Facebook) which almost inevitably blend both
identities. This is less so in science-specific SNS where the related privacy con-
flicts are attenuated: We observed that most researchers reveal only their profes-
sional identity here. This is usually supported by the set of information one is
supposed to enter when setting up one’s profile: the forms ask for biographical
information relevant to academia and less for private facts such as relationship
status. Note, however, that even ResearchGate asks for pet books and hobbies, but
only receives answers from a few according to our observations. In any case,
people using SNS leave their digital marks and traces, and so do researchers. There
is currently an intense discussion about privacy concerns in the general SNS. At
least some of researchers’ reluctance to join SNS may be explained by fear of
losing control over their privacy. In science-specific SNS, the data needed to
enable efficient networking based on automatically generated suggestions is to a
very large extent professional in nature, such as curriculum vitae, publications,
research interests, office contact information, etc. Nonetheless, if researchers are
very active on various Web 2.0 platforms, they create significant digital traces that
can be analyzed by data-mining tools. Identity theft (OECD 2008) is another
120 M. Nentwich and R. König
salient issue. Profiles may be hacked with the intention of damaging somebody’s
reputation, or false identity may be assumed in order to gain some benefits.
Barbour and Marshall argue that under these circumstances it is better to actively
shape one’s online persona than leaving this to others:
Although many academics do contribute to their online persona creation, there are just as
many who do not engage with new media in any meaningful way. However, this does not
mean that they are not present online. The risk of not taking control of one’s own online
academic persona is that others will create one for you. This is what we are terming the
‘uncontainable self’ (Barbour and Marshall 2012).
Conclusions
A close look at the technical functions of SNS shows that they are potentially
useful for a number of scholarly activities. In fact, they offer so many services that
they theoretically may serve as an encompassing platform, quasi a ‘‘one-stop-
service 2.0’’ of use for all major tasks within academia—from knowledge
production and distribution to communication and, even beyond the borders of
academia’s ivory tower, for public relations and other connections between
science and its environment. A large-scale implementation of SNS would imply a
number of major changes compared to the way scientists interact today. To begin
with, it would diversify the possibilities for interaction, creating a number of
fragmented pieces of information. Every single SNS does that due to the multiple
channels that it provides. The currently unclear market situation in the field of
science-specific SNS enforces this effect, since it creates even more channels. One
could argue that this might also lead to social diversification of academia, as it
comes with new possibilities for networking and increased transparency, including
novel perspectives for informal communication. It may become visible with whom
researchers interact with, what they read, discuss, and consider important. What is
more, other researchers from various fields and positions, even students and lay
people, might participate in these interactions. This tendency of lowering status-
based communication hurdles might be regarded as democratization of science.
Some would even argue that this would increase the quality of scientific work, as it
may be checked by more peers in an ongoing process that is much faster than the
regular circles of peer-reviewing.
However, we believe that these assumptions are far-fetched given the current
state of affairs. The diffusion of SNS in academia is still fairly low and even lower
when we focus on active scholars who make full use of the potential of these
platforms. As pointed out above, this is crucial for SNS because their whole
purpose is to create connections between active users. This limits the general
potential of SNS, no matter whether one sees it as desirable or problematic. Even
with today’s rather low participation, it is obvious that the vast amount of frag-
mented information distributed via multiple channels can quickly become
Academia Goes Facebook? 121
dysfunctional and lead to information overloads. This will rather work against
democratizing effects as scientists (or automated filter mechanisms) will have to
limit their attention even more—most likely to already well-established scholars
and channels. The transition to (Cyber-)Science 2.0 is to a large extent driven by
younger academics who may partly benefit in this context as they often know
‘‘how to play the game’’ of Web 2.0 better than their more senior colleagues. For
example, it is not unlikely that a young researcher will be rated higher by a novel
SNS-based metric like the RG Score. However, this advantage will probably
diminish if such numbers gain importance for evaluations. However, as long as
altmetrics still play a minor role, it is anyway questionable how much such a
benefit is really worth. In fact, it may even lower the status of a researcher because
others might regard the active usage of SNS as a waste of time.
Despite all of these difficulties, especially with regard to reaching the tipping
point of enough scientists actively using SNS, these services appear to be on the
rise. Since a critical mass of users can turn into a hyper-critical mass—which in
itself is problematic—we should make use of the opportunities of this current
transition period. Academics can (and should) shape future developments instead
of leaving it to commercial providers who follow their own interests. Coming back
to the title of this contribution: Academia might indeed ‘‘go Facebook’’ if it does
actively interfere by providing and further developing independent platforms.
There are already attempts to do exactly that, most-notably vivo, which gives
academic institutions a lot of freedom because it is based upon open source
software which can be run on local servers. However, it has apparently not reached
a critical mass yet and it will take more effort within academia to push such
independent projects to the point that they can compete with the temptations of the
global Internet players. Of course, commercial platforms may still simply create
the better platforms with more engaged scholars and it is debatable as to whether it
is desirable to interfere with this. This is very much a political question which can
be answered differently, depending on one’s point of view. Some will believe in
the free market, others will favor an active involvement of scientific institutions
and policy-makers, maybe even including regulation.
In the meantime, it seems likely that the unclear market situation in the field of
scientific social networks is not going to be clearly solved very soon. Therefore,
the best way to increase the functionality of these services is interface harmoni-
zation (e.g. via APIs), allowing the various services to connect to each other. There
are also academic initiatives in this direction; for example, ScholarLib connects
scientific thematic portals with SNS (Thamm et al. 2012). Again, such attempts are
limited by the will of the providers to create such openness via suitable APIs.
Obviously, we are witnessing a very dynamic development with both a promising
potential for the future of science and research—and for reflection.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
122 M. Nentwich and R. König
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124 M. Nentwich and R. König
Reference Management
Martin Fenner, Kaja Scheliga and Sönke Bartling
If I have seen further it is by standing on the shoulders of
Giants.
— Isaac Newton
Abstract Citations of relevant works are an integral part of all scholarly papers.
Collecting, reading, and integrating these references into a manuscript is a time-
consuming process, and reference managers have facilitated this process for more
than 25 years. In the past 5 years, we have seen the arrival of a large number of
new tools with greatly expanded functionality. Most of the newer reference
managers focus on the collaborative aspects of collecting references and writing
manuscripts. A number of these newer tools are web-based in order to facilitate
this collaboration, and some of them are also available for mobile devices. Many
reference managers now have integrated PDF viewers (sometimes with annotation
tools) for scholarly papers. Reference managers increasingly have to handle other
forms of scholarly content, from presentation slides to blog posts and web links.
Open source software and open standards play a growing role in reference man-
agement. This chapter gives an overview of important trends in reference man-
agement and describes the most popular tools.
M. Fenner (&)
Public Library of Science, San Francisco, CA, USA
e-mail: mfenner@plos.org
K. Scheliga
Alexander von Humboldt Institute for Internet and Society, Berlin, Germany
S. Bartling
German Cancer Research Center, Heidelberg, Germany
e-mail: soenkebartling@gmx.de
S. Bartling
Institute for Clinical Radiology and Nuclear Medicine, Mannheim University Medical
Center, Heidelberg University, Mannheim, Germany
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_8,  The Author(s) 2014
125
Introduction
Reference management is perceived to be tedious and time consuming by many
researchers, especially when it is done manually. In the past, references used to be
written on index cards and stored in boxes. Now, reference management software
allows for the digitalization of a personal collection of relevant scholarly publi-
cations. The earliest programs to manage the basic task of storing references and
adding them to manuscripts have been around for over 25 years (including End-
note and BibTeX/LaTeX-based programs which are still popular today), but each
individual entry had to be typed by hand. In the last 15 years we have seen a
number of significant developments that have made reference management much
easier for the researcher:
1. Retrieval of reference information from online bibliographic databases
2. DOIs and other persistent identifiers for bibliographic information
3. Automated management of PDF files
4. Open Access for easier access to full-text content
5. Web-based reference management for easier collaboration and use across
multiple devices
In this chapter we describe what reference managers are and provide an
overview of some reference management products. We do not make recommen-
dations as to which reference manager may be the best as this is a personal choice
and depends on the workflow of the individual researcher.
What is a Reference Manager?
A reference manager supports researchers in performing three basic research steps:
searching, storing, and writing (Fenner 2010a). It helps researchers find relevant
literature, allows them to store papers and their bibliographic metadata in a
personal database for later retrieval, and allows researchers to insert citations and
references in a chosen citation style when writing a text. To support those steps, a
reference manager should have the following functionalities as identified by
Gilmour and Cobus-Kuo (2011):
1. Import citations from bibliographic databases and websites
2. Gather metadata from PDF files
3. Allow organization of citations within the reference manager database
4. Allow annotation of citations
5. Allow sharing of the reference manager database or portions thereof with
colleagues
6. Allow data interchange with other reference manager products through standard
metadata formats (e.g. RIS, BibTeX)
7. Produce formatted citations in a variety of styles
8. Work with word processing software to facilitate in-text citation
126 M. Fenner et al.
A reference manager is a software package that allows scientific authors to
collect, organize, and use bibliographic references or citations. The terms citation
manager or bibliographic management software are used interchangeably. The
software package usually consists of a database that stores references and citations.
Once a citation is inserted into the database, it can be reused to create bibliog-
raphies which are typically found at the end of a scientific text.
Almost all reference managers allow direct importing from bibliographic dat-
abases through direct access from the reference manager and/or bookmarklets that
import content from the web browser. Alternatively, references can be imported
from other reference managers or from files in the BibTeX standard format with
the help of import tools.
The reference database can then be searched, indexed, and labeled. Most ref-
erence managers offer tools for organizing the references into folders and sub-
folders. Some reference managers allow the inclusion of full-text papers in PDF
format. References can be shared via the Internet and organized into workgroups
so that all members can use the same reference database.
Reference managers offer tools for exporting citations and references into word
processing programs by selecting relevant items from the database. The citation
style can be selected from a corresponding database which contains styles that aim
to cover the requirements of a large number of scholarly publishers. Some refer-
ence managers allow for styles to be edited and saved.
There is a wide variety of reference management software, and the strengths
and weaknesses of reference management software are perceived differently
depending on the workflows of individual scientists. The deciding factor for a
particular reference manager is often its popularity within a particular community,
as collaboratively writing a manuscript is facilitated if all authors use the same
reference manager (see chapter How This Book was Created Using Collaborative
Authoring and Cloud Tools). Reference managers have been commercially
available for a long time, but free solutions offer comparable functionalities and
are increasingly gaining importance.
Some reference managers allow sharing, collaborative editing, and synchroni-
zation of reference databases across a private workgroup and/or publicly via the
Internet. Public sharing of references is the focus of online-only social book-
marking tools such as CiteULike and Bibsonomy, but is also available with other
reference managers. This functionality makes it possible to share Open Access
papers online (see chapter Open Access: A State of the Art) and to generate usage
statistics as a novel means of measuring scientific impact (see chapter Altmetrics
and Other Novel Measures for Scientific Impact).
Getting References into the Reference Manager
All reference managers provide the functionality to manually enter bibliographic
data. However, it is more convenient if the references are automatically extracted
from an online bibliographic database such as Web of Science, Scopus, PubMed,
Reference Management 127
or Google Scholar. Most reference managers can also import references directly
from a webpage, usually using information embedded via CoinS. All reference
managers can import/export references in the BibTeX and/or RIS format; this is a
convenient way to share reference lists with colleagues.
Bibliographic Databases
Some of the largest bibliographic databases (Web of Science, Scopus, and others)
are only available via a subscription. In the last 10 years we have seen the
emergence of an increasing number of openly available bibliographic databases.
This trend started with PubMed in the late 1990s, includes Google Scholar, and,
more recently, Microsoft Academic Search and the CrossRef Metadata Search, and
now also includes bibliographic databases built by reference managers themselves
(e.g. Mendeley or CiteULike). The availability of these databases increases the
options for researchers to automatically import citation information, either via
direct integration into the reference manager, or via a bookmarklet that captures
the bibliographic content on the web page.
COinS: Hassle-Free Import of Bibliographic Data
ContextObjects in Spans (COinS) is a method that includes relevant bibliographic
metadata of a scientific publication into the HTML code of a web page. If
appropriate plugins are installed in a standard web browser, the bibliographic
information of a reference can be easily retrieved by a reference manager, thus
omitting tedious copy and paste processes. For example, if a reference is found in
PubMed, a little symbol appears in the browser address line if the Zotero plugin is
installed. At the click of a button, all important bibliographic information will be
transferred into the Zotero database. Many scientific databases, scientific social
networks, and journals support COinS (Fig. 1).
Digital Object Identifiers and Other Unique Identifiers
Most journal articles can now be uniquely identified by a digital object identifier
(DOI). DOIs for journal articles are issued by CrossRef, a non-profit organization
that has most scholarly publishers as its members. DOIs can also be used for other
content, e.g. conference proceedings or book chapters. DataCite is another non-
profit organization that can issue DOIs, focusing on DOIs for datasets. There are
also other unique identifiers for scholarly content, e.g. the PubMed ID, PubMed
Central ID, or the ArXiV ID. These identifiers make it much easier to handle
128 M. Fenner et al.
bibliographic information: reference managers can extract the DOI from imported
PDFs, obtain more citation information using the DOI, store the DOI internally to
help find duplicate records, etc. Authors only need to worry about the DOI (or
other unique identifier), all the other information they need (authors, title, journal,
link to the full-text) can be obtained from it.
Standardized Bibliographic Data Formats: BibTeX and RIS
BibTeX and RIS are the two most established file formats for storing bibliographic
data, and one or both of these formats are supported by all reference managers.
Exporting data in a standardized format is important because it allows users to
backup their reference lists independently of the reference management software,
to switch from one reference manager to another, or to use multiple reference
managers in parallel.
• BibTeX has existed since the mid 1980s and was designed to be used in
combination with the typesetting system LaTeX. The format is now widely
supported by reference managers that work with Microsoft Word and other
authoring tools, and by online bibliographic databases such as Google Scholar.
• Research Information Systems (RIS) is a standardized tag format originally
invented by Research Information Systems (now part of Thomson Reuters). The
format is widely supported and has been adapted over time, e.g. to include a
field for digital object identifiers (DOIs).
• Endnote XML and Citeproc JSON are newer formats which are not yet as
widely supported. BibTeX and RIS are plain text formats. XML and, more
recently, JSON have evolved into the standard data exchange formats of the
Web, and are easier to process automatically. They may therefore over time
become the predominant formats for exchanging bibliographic information.
Citation Styles and Citation Style Language
Citations can be formatted in many different ways: what information to include
(authors, title, journal, year, issue, pages), how to order and format this infor-
mation, and how to reference these citations in the main text (e.g. by number or
author/year). These so-called citation styles are important for printed documents,
but are not really relevant for digital content (where citations are exchanged in
BibTeX and other data formats). Unfortunately, most manuscript submission
systems do not accept references in digital format, and authors are forced to format
their references in the style requested by the publisher and include them as plain
text at the end of the manuscript (and, in turn, publishers then spend time and
money to get these references back into a bibliographic data format).
What publishers are really interested in are unique identifiers, such as the DOI, for
all references. This allows them to double-check the reference information against
Reference Management 129
bibliographic databases (using tools such as eXtyles), and to format the citations into
their preferred style. Citation information in free-text format can contain errors, and
these errors are propagated if citations are entered manually (see Specht 2010).
Citation styles are needed not only to correctly identify all references (for which
bibliographic formats and digital identifiers are better suited), but also to help the
researcher while reading the text. Citations are an important part of all scholarly
documents, and citation styles should facilitate that process. Even though a number
of common styles exist (e.g. APA, MLA, Chicago, Vancouver), there is no stan-
dard style for citations in scholarly documents, and with the differences in citation
practices between disciplines, it is not likely to ever happen. Some disciplines use
simple reference lists at the end of the document, whereas other disciplines use
footnotes at the bottom of pages and/or make heavy use of annotations.
Until it becomes standard practice to submit references in a bibliographic file
format together with manuscripts (some publishers do this already), authors must
resultingly deal with a large number of citation styles. This also often means
changing the citation style when a paper has to be resubmitted to another journal.
This is a time consuming endeavor, thus automating the process of adjusting to the
various citation styles is an important feature of all reference managers.
Most reference managers support a large number of citation styles: EndNote1
supports over 5,000 bibliographic styles, and Mendeley, Zotero, and Papers all
support 2,750 citation styles. Some reference managers include a style editor, in
Fig. 1 Showing COinS in action. At the click of a button, a reference is included into the
reference manager software (Zotero) from information that is contained in the COinS information
in the displayed web page: No need to manually copy references
1 EndNote Output Styles: http://endnote.com/downloads/styles
130 M. Fenner et al.
Examples of citation styles
DOI
http://dx.doi.org/10.1126/science.1197258
shortDOI
http://doi.org/dc3dhn
APA
Wolfe-Simon, F., Blum, J. S., Kulp, T. R., Gordon, G. W., Hoeft, S. E., Pett-Ridge, J., &
Oremland, R. S. (2011). A Bacterium That Can Grow by Using Arsenic Instead of
Phosphorus. Science, 332(6034), 1163-1166. doi:10.1126/science.1197258
Vancouver
Wolfe-Simon F, Blum JS, Kulp TR, Gordon GW, Hoeft SE, Pett-Ridge J, et al. A Bacterium That
Can Grow by Using Arsenic Instead of Phosphorus. Science [Internet]. American Association
for the Advancement of Science; 2011 Jun 2;332(6034):11636. Available from: http://
dx.doi.org/10.1126/science.1197258
Nature
Wolfe-Simon, F. et al. A Bacterium That Can Grow by Using Arsenic Instead of Phosphorus.
Science 332, 1163-1166 (2011)
BibTeX
@article{_Webb_Weber_Davies_et_al__2011, title = {A Bacterium That Can Grow by Using
Arsenic Instead of Phosphorus}, volume = {332}, url = {http://dx.doi.org/10.1126/
science.1197258}, DOI = {10.1126/science.1197258}, number = {6034}, journal = {Sci-
ence}, publisher = {American Association for the Advancement of Science}, author =
{Wolfe-Simon, F. and Blum, J. S. and Kulp, T. R. and Gordon, G. W. and Hoeft, S. E. and
Pett-Ridge, J. and Stolz, J. F. and Webb, S. M. and Weber, P. K. and Davies, P. C. W. and
et al.}, year = {2011}, month = {Jun}, pages = {1163-1166}}
RIS
TY—JOUR
T2—Science
AU—Wolfe-Simon, F.
AU—Blum, J. S.
AU—Kulp, T. R.
AU—Gordon, G. W.
AU—Hoeft, S. E.
AU—Pett-Ridge, J.
AU—Stolz, J. F.
AU—Webb, S. M.
AU—Weber, P. K.
AU—Davies, P. C. W.
AU—Anbar, A. D.
AU—Oremland, R. S.
SN—0036-8075
TI—A Bacterium That Can Grow by Using Arsenic Instead of Phosphorus
SP—1163
EP—1166
VL—332
PB—American Association for the Advancement of Science
(continued)
Reference Management 131
case a particular style is not yet supported. Citation styles used to be in proprietary
format and owned by the publisher of the reference manager, but the Citation Style
Language2 (CSL) has evolved as an open XML-based language to describe the
formatting of citations and bibliographies. Originally written for Zotero, CSL is
now also used by Mendeley, Papers, and many other tools and services. In 2012, a
web-based editor3 to create and edit CSL styles was launched, facilitating the
creation of additional styles.
Managing Full-Text Content
Reference management has traditionally been about managing information about
scholarly content (authors, title, journal, and other metadata). With the switch to
digital publication and the availability of content in PDF, as well as other formats,
reference management increasingly dealt with managing this digital content:
linking references to the full-text document on the computer, performing full-text
search, making annotations in the PDF, managing the PDF files on the hard drive,
etc. Papers was the first reference manager to focus on this aspect, but most
reference managers now have functionality to manage PDF files.
Most scholarly journal articles are currently distributed via subscription jour-
nals. This makes it important to store a copy on the local hard drive for easier
access, but it can also create problems when these PDF files are shared with
collaborators (which most publishers do not allow, even within the same institu-
tion). Reference management software therefore has to make decisions as to what
(continued)
DO—10.1126/science.1197258
PY—2011
UR—http://dx.doi.org/10.1126/science.1197258
ER—
Citeproc JSON
{‘‘volume’’:’’332’’,’’issue’’:’’6034’’,’’DOI’’:’’10.1126/science.1197258’’,’’URL’’:’’http://
dx.doi.org/10.1126/science.1197258’’,’’title’’: ‘‘A Bacterium That Can Grow by Using
Arsenic Instead of Phosphorus’’,’’container-title’’:’’Science’’,’’publisher’’:’’American Asso-
ciation for the Advancement of Science’’,’’issued’’:{‘‘date-
parts’’:[[2011,6,2]]},’’author’’:[{‘‘family’’:’’Wolfe-Simon’’,’’given’’:’’F.’’},{‘‘fam-
ily’’:’’Blum’’,’’given’’:’’J. S.’’},{‘‘family’’:’’Kulp’’,’’given’’:’’T. R.’’},{‘‘family’’:’’Gor-
don’’,’’given’’:’’G. W.’’},{‘‘family’’:’’Hoeft’’,’’given’’:’’S. E.’’},{‘‘family’’:’’Pett-
Ridge’’,’’given’’:’’J.’’},{‘‘family’’:’’Stolz’’,’’given’’:’’J. F.’’},{‘‘family’’:’’Webb’’,’’given’’:’’S.
M.’’},{‘‘family’’:’’Weber’’,’’given’’:’’P. K.’’},{‘‘family’’:’’Davies’’,’’given’’:’’P.
C. W.’’},{‘‘family’’:’’Anbar’’,’’given’’:’’A. D.’’},{‘‘family’’:’’Oremland’’,’’given’’:’’R.
S.’’}],’’editor’’:[],’’page’’:’’1163-1166’’,’’type’’:’’article-journal’’}
2 Citation Style Language: http://citationstyles.org/
3 Find and edit citation styles: http://editor.citationstyles.org/about/.
132 M. Fenner et al.
is technically possible and convenient for researchers vs. what is possible under
copyright law (see chapter Intellectual Property and Computational Science).
Content published as Open Access does not have these limitations. This not
only makes it much easier to share relevant full-text articles with collaborators, but
it also means that we often do not need to store a copy of the full-text on the local
hard drive, as the content is readily available.
Reference Management Tools
From the large number of available reference managers, we have chosen seven
popular products that are described in more detail below. We have included a table
that gives an overview of their basic features. A feature list is not the only criterion
in picking a reference manager though; ease of use, stability, price, and available
support in case of questions are equally important factors.
EndNote
EndNote is a commercial reference management software package produced by
Thomson Reuters. Endnote is one of the most popular reference managers and has
been around for more than 20 years. It allows collecting references from online
resources and PDFs. References from bibliographic databases can be imported into
EndNote libraries. Full-text can be imported too. EndNote provides plugins for
Microsoft Word and OpenOffice. References can be exported to BibTeX. While
EndNote does not include any collaborative features, EndNote Web provides the
functionality for collaboration with other users. Users can give group members
read/write access to their references and import references from other people’s
libraries. Endnote also integrates with other bibliographic tools produced by
Thomson Reuters, including Web of Science and ResearcherID.
Mendeley
Mendeley is a reference manager developed by a London based startup, but has
been bought by Elsevier earlier this year. Its strength lies in its networking and
collaborative features, and also in providing facilities for easily managing PDF
files. It offers both a desktop and a web version with synchronized bibliographic
information, allowing access from several computers and collaboration with other
users. PDF files can be imported into Mendeley desktop and metadata such as
authors, title, and journal are automatically extracted. It is possible to do a full-text
search, highlight text in PDFs, and add sticky notes.
The web version recommends papers to users based on their profiles and the
content in their libraries. Users can create both private and public groups and share
Reference Management 133
papers and annotations. Mendeley is free to use, but costs a monthly fee if the number
of documents in Mendeley web or the number of private groups exceeds a limit.
Zotero
Zotero is a popular open source reference manager, originally developed as a
plugin for the Firefox browser. The newer Zotero Standalone offers the same
functionality but runs as a separate program and works with Firefox, Chrome and
Safari. Zotero also includes a hosted version in order to synchronize references
across devices and share them in private or public groups.
Zotero allows users to collect and to organize a variety of web sources such as
citations, full-texts, web pages, images and audio files directly in the browser.
Citations from Zotero can be integrated into Microsoft Word and OpenOffice.
RefWorks
RefWorks is a commercial web-based reference manager by ProQuest. The Write
N Cite utility enables the integration of references into Microsoft Word where in-
text citations and reference lists can be formatted into various styles. RefWorks
makes it easy to collaborate with others as all references are stored in the web-
based version. The Write N Cite utility can also work offline, but RefWorks is not
the right tool for researchers with intermittent or poor Internet connectivity.
Papers
Papers is a commercial reference management software, now part of Springer Sci-
ence+Business media. Initially Papers was only available for Mac, but now there are
also versions for iPad and PC. Its main strength is its excellent handling of PDF
documents (including metadata extraction) and its polished user interface, whereas
the collaborative features are less developed than in some of the other products.
Papers uses the Citation Style Language and provides a word processor plugin.
JabRef
JabRef is an open source bibliography reference manager popular with LaTeX
users. It runs on Java and is thus compatible with Windows, Linux, and Mac. The
native file format is BibTeX which is the standard LaTeX bibliography format.
The strength of JabRef is that references can be formatted directly in LaTeX, thus
134 M. Fenner et al.
providing access and control over a wide range of citation styles. JabRef provides
direct search and downloads from PubMed and IEEEXplore. There are plugins for
word processing programs and also other Java based plugins, which expand the
general functionality.
CiteULike
CiteULike is a free online reference manager and social bookmarking tool. Ref-
erences are primarily entered via a bookmarklet that captures bibliographic content
in web pages. New entries are public by default and are added to the common
library, but entries can be also made private. Users can assign tags to entries which
make it easier to organize and search through content. References can be exported
in BibTeX and RIS formats. The social networking features are the strength of
CiteULike. Users can create profiles, connect with other researchers, and create
and join groups where they can collaborate on library content.
Fig. 2 Feature comparison of popular reference managers (see also Fenner 2010b)
Reference Management 135
Other Reference Management Products
Many other reference managers are available, including Citavi which is popular in
some disciplines and also helps with knowledge management, and ReadCube
which has a very nice user interface and a good PDF viewer. An extensive list and
comparison of available reference management software can be found on Wiki-
pedia (Fig. 2).4
Outlook
Reference management has become easier, cheaper, and more social in the past
few years, and this trend will continue. We will see the integration of unique
author identifiers (ORCID, etc.) into bibliographic databases and reference man-
agement tools (see case in chapter Unique Identifiers for Researchers), and this
will facilitate the discovery of relevant literature and the automatic updating of
publication lists. We will increasingly see citations of datasets and other non-text
content (see chapter Open Research Data: From Vision to Practice). Digital
identifiers for content and support for the open Citation Style Language will also
increase, as will the availability of open bibliographic information. Three areas
still need improvement. Firstly, the automatic importing of the references of a
particular publication, and the integration of reference managers into authoring
tools. Secondly, the word processor plugins for reference managers still do not
work together, and some of the newer online authoring tools (Google Docs, etc.)
need to be better integrated with reference managers. Finally, instead of having
references in plain text, which makes it difficult to get to the full-text and reformat
it into a different citation style, publishers, institutions, and funders should start to
ask for reference lists in standard bibliographic formats using digital identifiers.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
Fenner, M. (2010a). Reference management meets Web 2.0. Cellular Therapy and Transplan-
tation, 2(6), 1–13.
Fenner, M. (2010b). Reference manager overview. Gobbledygook. Available at http://
blogs.plos.org/mfenner/reference-manager-overview/.
4 Comparison of reference management software: http://en.wikipedia.org/wiki/Comparison_of_
reference_management_software
136 M. Fenner et al.
Gilmour, R. & Cobus-Kuo, L. (2011). Reference management software: A comparative analysis
of four products. In Issues in Science and Technology Librarianship. Available at http://
www.istl.org/11-summer/refereed2.html.
Specht, C. G. (2010). Opinion: Mutations of citations. Available at http://www.the-scientist.com/
?articles.view/articleNo/29252/title/Opinion–Mutations-of-citations/.
Reference Management 137
Open Access: A State of the Art
Dagmar Sitek and Roland Bertelmann
Open Access saves lives.
—Peter Murray-Rust
Abstract Free access to knowledge is a central module within the context of
Science 2.0. Rapid development within the area of Open Access underlines this
fact and is a pathfinder for Science 2.0, especially since the October 2003
enactment of the ‘‘Berlin Declaration on Open Access to Knowledge in the Sci-
ences and Humanities’’. Berlin Declaration on Open Access to Knowledge in the
Sciences and Humanities (http://oa.mpg.de/files/2010/04/berlin_declaration.pdf)
Introduction
The past years have shown that Open Access is of high relevance for all scientific
areas but it is important to see that the implementation is subject-tailored. In all
journal based sciences two both well-established and complementary ways used
are ‘‘OA gold’’ and ‘‘OA green’’. These two ways offer various advantages that
enhance scientific communication’s processes by allowing free access to infor-
mation for everybody at any time.
Furthermore, it is necessary to break new ground in order to expand, optimize,
and ensure free worldwide access to knowledge in the long run. All involved
players need to re-define their role and position in the process. This challenge will
lead to new, seminal solutions for the sciences.
D. Sitek (&)
DKFZ German Cancer Research Center, Heidelberg, Germany
e-mail: d.sitek@dkfz-heidelberg.de
R. Bertelmann
GFZ German Research Centre for Geosciences, Potsdam, Germany
e-mail: roland.bertelmann@gfz-potsdam.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_9,  The Author(s) 2014
139
Definition of Open Access
Open Access implies free access to scientific knowledge for everybody. In the
‘‘Berlin Declaration on Open Access to Knowledge in the Sciences and Human-
ities’’, the term scientific knowledge is defined as ‘‘original scientific research
results, raw data and metadata, source materials, digital representations of pictorial
and graphical materials and scholarly multimedia material.’’1 Most scientific
knowledge is gained in a publicly funded context; basically it is paid for by the
taxpayer. In many fields, journals are the main channel of scholarly communica-
tion, therefore Open Access has especially developed in this sector. At the
moment, most scientific journal articles are only accessible to scientists who are
working in an institution with a library that has licensed the content. According to
the ‘‘Berlin Declaration’’, not only a ‘‘free, irrevocable, worldwide, right of
access’’ should be granted, but also a ‘‘license to copy, use, distribute, transmit and
display the work publicly and to make and distribute derivative works, in any
digital medium for any responsible purpose (…) as well as the right to make small
numbers of printed copies for their personal use.’’1
Authors have several possibilities in publishing their research results. Open
Access maximizes the visibility and outreach of the authors’ publications and the
results of public funding (Fig. 1).
State of the Art
Open Access today is an accepted and applauded scientific publication strategy. In
summer 2012, a number of statements impressively showed the state of the art as it
was seen by national and international actors of science politics and science
management.
The European Commission published its vision on how to improve access to
scientific information. Two citations from this occasion’s press release and the
related communication represent this perspective:
As a first step, the Commission will make Open Access to scientific publications a general
principle of Horizon 2020, the EU’s Research & Innovation funding program for
2014–2020. As of 2014, all articles produced with funding from Horizon 2020 will have to
be accessible: articles will either immediately be made accessible online by the publisher
(‘Gold’ Open Access)—up-front publication costs can be eligible for reimbursement by
the European Commission; or researchers will make their articles available through an
Open Access repository no later than six months (12 months for articles in the fields of
social sciences and humanities) after publication (‘Green’ Open Access).2
1 http://oa.mpg.de/files/2010/04/berlin_declaration.pdf
2 Press Releases RAPID: http://europa.eu/rapid/press-release_IP-12-790_en.htm
140 D. Sitek and R. Bertelmann
The European Commission emphasises Open Access as a key tool to bring together people
and ideas in a way that catalyses science and innovation. To ensure economic growth and
to address the societal challenges of the 21st century, it is essential to optimize the
circulation and transfer of scientific knowledge among key stakeholders in European
research—universities, funding bodies, libraries, innovative enterprises, governments and
policy-makers, non-governmental organizations (NGOs) and society at large.3
Hence, we can expect that results from the European research program Horizon
2020 should be fully Open Access. Already in the current program, published
results from the fields of energy, environment, health, information and commu-
nication technologies, and research infrastructures are expected to be Open
Access, according to an Open Access Pilot for these subjects.
… beneficiaries shall deposit an electronic copy of the published version or the final
manuscript accepted for publication of a scientific publication relating to foreground
published before or after the final report in an institutional or subject-based repository at
the moment of publication. Beneficiaries are required to make their best efforts to ensure
that this electronic copy becomes freely and electronically available to anyone through this
repository:
Fig. 1 The Open Access process, an overview
3 European Commission: http://ec.europa.eu/research/science-society/document_library/pdf_
06/era-communication-towards-better-access-to-scientific-information_en.pdf
Open Access: A State of the Art 141
• immediately if the scientific publication is published ‘‘Open Access’’, i.e. if an elec-
tronic version is also available free of charge via the publisher, or
• within [X] months of publication …4
In June 2012, the Royal Society published a report named ‘‘Science as an open
enterprise’’ which aimed for research data to be an integral part of every
researcher’s scientific record, stressing the close connection of open publication
and open accessible research data (see chapter Open Research Data: From Vision
to Practice: Open Research Data). ‘‘Open inquiry is at the heart of scientific
enterprise … We are now on the brink of an achievable aim: for all science
literature to be online, for all of the data to be online and for the two to be
interoperable’’ (Boulton 2012).
In several countries, initiatives are afoot to build a legal foundation to help
broadening the road to a world of openly accessible scientific results. Just to name
a few:
• In Great Britain, the minister for universities and science, David Willetts,
strongly supports a shift to free access to academic research. The British gov-
ernment actually plans to Open Access to all publicly funded research by 2014
(cf. Willetts 2012).
• In the United States, an initiative called Federal Research Public Access Act
(FRPAA) is on the way, requiring ‘‘free online public access’’.5
• In Germany, the Alliance of German Science Organizations is a strong supporter
of an initiative for a change in German Copyright law which is supposed to
secure a basic right for authors to publish their findings in accordance to the idea
of providing scientists free access to information.6
Since the Budapest Open Access Initiative (BOAI 2002)7 was inaugurated, a
number of declarations of different bodies have paved the way. Indeed, the list of
signatories of the ‘‘Berlin Declaration on Open Access to Knowledge in the
Sciences and Humanities’’8 read like a gazetteer of scientific institutions and
organizations worldwide.
Due to a series of follow-up conferences, the number of supporters of the Berlin
Declaration is still growing. Moreover, some US universities took the opportunity
to join in when the conference took place Washington DC in December 2011.9
Already at an early stage, funding bodies like the National Institutes of Health
(NIH)10 stepped in and created rules for the openness of their funded research.
4 Annex 1: http://ec.europa.eu/research/press/2008/pdf/annex_1_new_clauses.pdf
5 Berkman: http://cyber.law.harvard.edu/hoap/Notes_on_the_Federal_Research_Public_Access_Act
6 Priority Initiative ‘‘Digital Information’’: http://www.allianzinitiative.de/en/core_activities/
legal_frameworks
7 Budapest Open Access Initiative: http://www.soros.org/openaccess/read
8 http://oa.mpg.de/files/2010/04/berlin_declaration.pdf
9 B9 Open Access Conference: http://www.berlin9.org/
10 NIH Public Access Policy: http://publicaccess.nih.gov/policy.htm
142 D. Sitek and R. Bertelmann
National funders like DFG in Germany, SURF in the Netherlands, JISC in GB, and
others not only asked for open results of their funded projects, but also featured
change by funding calls for projects to do research on Open Access and to develop
appropriate infrastructure.
One of the boosters for this rapid development of support for Open Access was,
of course, the ‘‘journal crisis’’. Since the early 1990s, we have seen a dramatic
change in the scientific publication landscape, especially for journals which had
basically not changed since they were established. One other aspect was a long
series of mergers of publication houses, leaving us with four big players controlling
about 60–70 % of journal titles worldwide. Here, stock exchange perspectives and
risk money from private equity funds are playing an important and shaping role.
Another change factor lies in the possibilities of the Internet. Already in the early
days of the Internet, ArXiv was established.11 ArXiv already displayed the benefits
of Open Access within a small, and for a long time closely cooperating community,
Particle Physics. Publishers used their monopoly and increased journal prices over
the years at very high rates; at least 10 % per year was the standard for years.
Although these rates have lowered a little in the last few years (five to six percent
per year), since the late nineties, most libraries have had problems keeping up with
the subscriptions to journals which they should hold for the benefit of researchers.
As a result, they have cancelled journal subscriptions. Even today, big and famous
libraries cancel journal subscriptions, like the Faculty Advisory Council suggested
for Harvard in 2012.12 In former days, moderate prices guaranteed that, at least in
the Western world, the possibility somehow of providing access to scientific out-
put. In these times of a continuing ‘‘journal crisis’’, fewer and fewer scientists get
access to journals, especially in science, technology, and medicine, and therewith
to scientific knowledge, because not all institutions can afford the ever rising
subscription fees. This day to day experience of many scientists enormously helped
to build the vision of science based on openly accessible publications.
In economic theory, Nobel laureate Elinor Ostrom extended her concept of ‘‘the
commons’’ to also include ‘‘understanding knowledge as a commons’’ (Hess and
Ostrom 2011). Open Access activist Peter Suber showed how Open Access fits
perfectly in such a theoretical background: ‘‘Creating an Intellectual Commons
through Open Access’’ (Suber 2011).
Interviews with scientists showed that a change in attitude has already taken
place. More and more scientists admit that they quickly change to another content
related, but accessible article if they experience access problems. Also, Open
Access seems to be a modern prolongation of a central traditional scientific habit
for many scientists: make your work accessible to colleagues. This was done in
former times with the help of offprints. Open Access to an article may be seen as a
modern solution for such scientific needs.
11 Arxiv: http://arxiv.org/
12 Faculty Advisory Council Memorandum on Journal Pricing: http://isites.harvard.edu/icb/
icb.do?keyword=k77982&tabgroupid=icb.tabgroup143448
Open Access: A State of the Art 143
Some numbers show how broad the idea of Open Access is, how many already are
using Open Access, and how quickly it is still growing. ‘Base’,13 a service built by the
University Library Bielefeld, is harvesting the world of repositories (Green Open
Access) and indexing their contents. It now contains over 35 million items, which
compares to a total of about 1 million collected items in 2005.14 The Directory of
Open Access Journals (DOAJ)15 gives an overview of Open Access Journals (Gold
Open Access). In 2002, they counted 33 journals. Currently, the steadily increasing
number has reached more than 8,000 Open Access journals (Fig. 2).
‘‘Green’’ and ‘‘Gold’’ are the two well established and complimentary used
roads to Open Access. Open Access depends upon these basic concepts, but, of
course, a number of mixed concepts have also been established (for detailed
information see Suber 2012b).
A Closer Look at Green and Gold Open Access
Traditional journal subscription denies access to all those whose institution cannot
afford to pay yearly subscription fees. In contrast, Gold Open Access describes a
business model for journal publishing which gives free access to all potential
readers and sees other models to cover expenses for a publication. Although there
Fig. 2 Number of Open Access journals by year (Source DOAJ)
13 BASE: http://www.base-search.net/?fullbrowser=1
14 BASE Statistics: http://www.base-search.net/about/en/about_statistics.php?menu=2
15 Directory of Open Access Journals: http://www.doaj.org/
144 D. Sitek and R. Bertelmann
is a broad range of models, a majority of notable Open Access journals rely on
publication fees, also known as article processing charges (ACP), which are often
also incorrectly referred to as author fees. These sums are usually paid by the
author’s institution. Especially in science, technology, and medicine, this follows
the practice of paying for color images, too many pages, or other factors, which
has been familiar since pre-Internet times in subscription-based journals. On the
other hand, society journals in particular have opened access to their journal
articles without charging authors. Others, like PNAS, give access after an embargo
of half a year.
In the early times of Open Access an often heard argument against golden
journals was the question of quality. Open Access journals differ only in terms of the
publisher’s business model from subscription-based journals; they pass through the
same peer review processes as the traditional journals and there are no differences
regarding the quality of the published articles. Newly founded journals need some
time to build a positive resonance in their research community. Today, Open Access
journals are, of course, embedded within the high ranking journals in their fields.
More and more get listed, e.g. in the Journal Citation Report of Thomson-Reuters
(more than ten percent of all ranked titles).16 Laakso et al. (2011) give a compre-
hensive overview of the development using a distinction between ‘‘The pioneering
years (1993–1999), the innovation years (2000–2004), and the consolidation years
(2005–2009)’’. For the future a more numerous migration from existing and valued
subscription journals to the Gold model is desirable.
However, Open Access is not only possible for articles in journals with these
respective business models. The Green Road of Open Access stresses the possi-
bility of authors to deposit their articles, which are primarily published on
publisher’s sites, additionally on another server. Usually an institutional or a
subject-based repository is used for this purpose. ‘‘A complete version of the work
and all supplemental materials, including a copy of the permission as stated above,
in an appropriate standard electronic format is deposited (and thus published) in at
least one online repository using suitable technical standards’’ (Berlin Declara-
tion).17 An institutional repository is organized by an institution and contains the
publications of its scientists. Opposed to the affiliation to an institution as a cri-
terion for content, a subject-based repository comprises publications which belong
to some specific fields of science. A famous example of a subject-based repository
is ArXiv18 which already laid the grass roots for Open Access in the nineties.
PubMedCentral19 is another example within the field of biomedical and life
sciences research.
16 Thomson Reuters Intellectual Property and Science: http://science.thomsonreuters.com/cgi-
bin/linksj/opensearch.cgi?
17 http://oa.mpg.de/files/2010/04/berlin_declaration.pdf
18 http://arxiv.org/
19 NCBI: http://www.ncbi.nlm.nih.gov/pmc/
Open Access: A State of the Art 145
In principle, between 70 and 80 % of scientific journals allow such a method of
self-archiving. Generally there are some restrictions which have to be complied by
the authors. In the majority of cases, depositing a version of the original article is
not allowed, but rather a preprint or a version of the final draft. Depending on the
journal, this can either be done immediately, or an embargo period between the
publication of the original article and the self-archiving has to be considered. This
period is usually between six (for STM journals) and twelve months (for social
sciences and humanities journals). All journals which allow self-archiving are
listed in the SHERPA/Romeo database which also lists the specific restrictions.20
Meanwhile, many scientific institutions run an institutional repository to deposit
green Open Access articles. Usually the library is responsible for this task. The
deposit of their articles in such a repository has numerous advantages for the
scientists.
One large benefit is made up of the support the repository operator offers its
users. The scientists in question may ask for advice in all questions concerning the
workflow of posting their publications. This includes, for example, when and in
which form there are allowed to deposit a journal article. Digital preservation of
the stored material is ensured as soon as explicit identification like persistent
identifiers is provided. Furthermore, they make sure that all publications are tagged
with precise and standardized metadata. Only in this way is cross-linking with
other sources or other repositories possible, and, in turn, future features like
semantic web functions will be possible. Correctly prepared data also enhances
search engine exposure. In future, the linking between publications and the data
which belongs to them, for example supplementary material or research data, will
be relatively easy to establish on this level.
The content of an institutional repository is not limited to journal articles. By
posting all of their publications, like reports, talks, conference proceedings,
teaching materials, and so forth, into the repository, the scientists can present their
work openly as a whole within the context of their research group and institution.
If this Open Access repository is combined with an institutional research infor-
mation system (CRIS), numerous benefits can emerge. Authors can re-use this
content by linking to their openly accessible publications from social networks for
scientists, their own homepage, etc. Through this linking, it is possible to connect
the advantages of an institutional repository with the scientist’s personal needs. It
can also be used as a helpful tool for the management of publications.
Scientists should demand such a database from their institutions if such a service
is not yet offered. Not only the scientists in question, but also the whole institution
benefit from a well-run institutional repository. A presentation of the research
output of an institution in this way can constitute an important element for science
marketing. For example, a Google search on articles shows the institutional
homepage with the repository output, instead of a publisher’s homepage.
20 SHERPA/RoMEO: http://www.sherpa.ac.uk/romeo/
146 D. Sitek and R. Bertelmann
It hence serves as a showcase of the research output of an institution (cf. Armbruster
and Romary 2010). Therefore, it should also be in their interest to build such
systems.
Institutional repositories mostly deal with final drafts. Subject-oriented repos-
itories are based on specific traditions in certain scientific fields. Surely the most
famous is ArXiv,21 founded in the early nineties for researchers in Theoretical
Physics. Today it is a preprint archive for a broad range of scientific subjects.
A recent Nature article states: ‘‘Population biologists turn to pre-publication server
to gain wider readership and rapid review of results’’ (Callaway 2012).
PubMedCentral,22 on the other hand, is a subject repository which, amongst
others, hosts NIH-sponsored manuscripts, while Research Papers in Economics
(RePEc)23 has successfully built a service based on the long tradition of publishing
reports in economics. In future, both repository types should get better linked. If an
article is deposited in one type, linkage to the other should become a standard.
Green or Gold: Which is the Better Way?
The recommendations of the British Working Group on Expanding Access to
Published Research Findings, the Finch Report24 preferred Gold to Green. This
triggered a discussion on what the best way to Open Access is (see, for example,
Harnad 2012 or Suber 2012a). Obviously, seen from a scientist’s point of view,
both ways have the right to exist. Looking at the green road means publishing an
article wherever one thinks it is the best for one’s career. At the same time, one
makes use of the opportunity to give access to all relevant readers. Funders’
mandates will not be a problem, the green road is compliant. If self-archiving is
supported by a well made institutional infrastructure, this way works quickly and
easily. Additional costs are generated from infrastructural needs. In too many
institutions there is still poor help for researchers; too often they are still left alone
to self-archive. Hence, institutions should not only state policies, but actively
support their scientists and strengthen infrastructural backing.
Meanwhile, the golden road is part of an axiomatic change of the publication
landscape. It is a primary publication, which gives immediate access to an article
in the context of its journal, including linkage to all additional services of a
publisher. ‘‘… journals receive their revenues up front, so they can provide
immediate access free of charge to the peer-reviewed, semantically enriched
published article, with minimal restrictions on use and reuse. For authors, gold
means that decisions on how and where to publish involve balancing cost and
quality of service. That is how most markets operate, and ensures that competition
21 http://arxiv.org/
22 http://www.ncbi.nlm.nih.gov/pmc/
23 RePEc: http://repec.org/
24 Research Information Network: http://www.researchinfonet.org/publish/finch/
Open Access: A State of the Art 147
on quality and price works effectively. It is also preferable to the current,
non-transparent market for scholarly journals.’’, as the secretary to the Finch
committee puts it (Jubb 2012).
Naturally of course, again, the respective traditions of research fields matter.
Björk et al. write ‘‘There is a clear pattern to the internal distribution between
green and gold in the different disciplines studied. In all the life sciences, gold is
the dominating OA access channel. The picture is reversed in the other disciplines
where green dominated. The lowest overall OA share is in chemistry with 13 %
and the highest in earth sciences with 33 %.’’ (Björk et al. 2010).
New Models
Of course, publishing has its costs and whatever way is followed, someone has to
cover these. In the case of green repositories, infrastructure needs to be supported
by institutions. Article processing charges for gold publishing are paid by funders
or institutions. Most funders have already reacted and financing at least a part of
open publishing fees has become a standard. Unfortunately, many institutions
currently lack an appropriate workflow for this new challenge. Traditional journal
subscription is generally paid for by libraries. In institutions it often is not clear to
scientists, by whom and how these Open Access charges can be managed. Ini-
tiatives like the ‘‘Compact for Open-Access Publishing Equity’’25 are paving the
way to such workflows in order to make them a matter of course. The Study of
Open Access Publishing (SOAP)26 gave evidence of this challenge: ‘‘Almost 40 %
[scientists] said that a lack of funding for publication fees was a deterrent …’’.
(Vogel 2011) Institutional libraries must face this challenge and take on a new
role. This new role is not too far from their traditional role: paying for access to
information as a service for scientists. Björn Brembs even sees the future in a
‘‘library-based scholarly communication system for semantically linked literature
and data’’ instead of the traditional processes dominated by publishers (Brembs
2012). Sponsoring Consortium for Open Access Publishing in Particle Physics
(SCOAP3) ‘‘is currently testing the transition of a whole community’’,27 inte-
grating scientific institutions, their libraries, and publishers.
Some publishers feature a hybrid model. They still back the traditional sub-
scription model, but also offer an opportunity to buy out an individual article for
Open Access. Seen from a scientist’s point of view this may be interesting, but
usually the price level is high and only a few authors utilize this option. Seen from
an institutional point of view, this is a problematic business model, as article fees
25 Compact for Open-Access Publishing Equity: http://www.oacompact.org/compact/
26 SOAP: http://project-soap.eu/
27 SCOAP3: http://scoap3.org/
148 D. Sitek and R. Bertelmann
and subscription fees are normally not yet combined. Therefore, institutions pay
twice to a publisher (Björk 2012). Obviously such a hybrid is not a transition path
to Gold.
Since traditional publishers have adopted the Open Access business model (e.g.
Springer in 2010), we have definitely reached a situation in which Open Access
has grown-up.
All stakeholders of the scholarly communication have to check carefully and
adjust their roles in future, as new stakeholders will arise. They have been closely
connected with each other for a long time. Open Access brought new functions and
tasks for each stakeholder within the classical distribution; not all have already
faced up to that challenge. For example, a lot of publishers are still locked up in
thinking in print terms. In parallel, a common fear is that ACPs will go up and up
when traditional publishers adopt the gold model and will become unaffordable.
Charges differ within a broad range of some hundred Euros up to over 3,000 Euros
(Leptin 2012).
We now see a mixture of the roles mentioned. As an example, libraries can, in
certain cases like grey literature, switch to a publisher’s role. On the other hand,
‘‘Nature’’ experimented with preprint publication, opening in 2007 ‘‘Nature
precedings’’,28 closing down the platform again in 2012. Due to Open Access, new
publishing houses were set up. Some have quickly become respectable and suc-
cessful, others may be under suspicion to be predatory.29 Besides, new players are
in the game and it is not yet decided as to where their role will lie. Publication
management systems and scientific social networks are merging, often relying on
Open Access full-texts.
But not only the players have to re-define their role within the scientific publi-
cations process; the format of scientific knowledge is also changing: ‘‘It no longer
consists of only static papers that document a research insight. In the future, online
research literature will, in an ideal world at least, be a seamless amalgam of papers
linked to relevant data, stand-alone data and software, ‘grey literature’ (policy or
application reports outside scientific journals) and tools for visualization, analysis,
sharing, annotation and providing credit. … And ‘publishers’ will increasingly
include organizations or individuals who are not established journal publishers,
but who host and provide access and other added value to this online edifice. Some
may be research funders, such as the National Institutes of Health in its hosting of
various databases; some may be research institutions, such as the European
Bioinformatics Institute. Others may be private companies, including suppliers of
tools such as the reference manager Mendeley and Digital Science, sister company
to Nature Publishing Group’’ (Nature 2012a).
28 Nature Precedings: http://precedings.nature.com/
29 see Code of Conduct of the Open Access Scholarly Publishers Association—OASPA: http://
oaspa.org/membership/code-of-conduct/
Open Access: A State of the Art 149
Books and Grey Literature
For a long time, scientific journals were in the center of Open Access discussions.
Since e-books in scholarly publication are also emerging very quickly, we will
soon see more developments in this field. Of course, business models are not one
to one transferable from journals to monographs. Nevertheless, it already is clear
that there is also a model for commercial publishers. Often an electronic version of
a book is published Open Access and the publisher gets revenues by selling the
printed version. In some fields, monographs mostly consist of a compilation of
articles. In these cases, their handling could be comparable to journals. Maybe
Springer’s move in August 2012 to introduce Open Access Books will be copied
by other publishers. In the sciences it is familiar and a long standing tradition that
an institution supports publishing a book by paying high contributions to pub-
lishers. It is just a matter of establishing a new culture to change the underlying
standardized contracts in two points. Firstly, to retain rights for authors and
institutions, secondly, to introduce some kind of Open Access, perhaps including
an embargo.30
So called ‘‘grey literature’’ has always played a role in scholarly communica-
tion and was mostly defined by its poor findability and dissemination. Usually,
grey literature was published by scientific institutions in print with a low circu-
lation rate. But if the content is trusted, reviewed, and is published Open Access by
a reputable institution in its own electronic publishing infrastructure, grey litera-
ture is up for playing a new and sustainable role in a future publication landscape.
Some even see this as a nucleus for a rearrangement of roles (see Huffine 2010).
Hereby, libraries could be one of the key players. Important for an Open Access
future of grey literature is a thoroughly-built infrastructure which guarantees
quality, persistence, and citability. New and emerging ways of scholarly com-
munication can be included in such a structure.
The important role of research data for an Open Science is discussed elsewhere
in this book (see Pampel and Dallmeier-Tiessen in this volume). Scholarly text
publication and data publication cannot be separated in the future. On the side of
Open Access for texts, a close connection to related data needs to become part of
scholarly common sense. Text and data should be seen as an integral unit which
represents the record of science of a researcher. As Brembs puts it: ‘‘Why isn’t
there a ‘World Library of Science’ which contains all the scientific literature and
primary data?’’ (Brembs 2011). Talking about data mining will always have both
parts in mind: text and related research data.
30 OApen: http://project.oapen.org/index.php/literature-overview
150 D. Sitek and R. Bertelmann
Impact of Open Access on Publishing
In addition to establishing new business models, Open Access has featured and
accelerated elementary changes in scientific publishing. The general impact of
Open Access upon the development of scholarly publishing is tremendous.
Peer review is one issue. Already ten years ago, for example, Copernicus
Publications, the largest Open Access publisher in geosciences, combined Open
Access publishing with a concept of open interactive peer review (Pöschl 2004).
The rise of PLOS One,31 an Open Access mega journal promising quick peer
review and publication and accepting articles from all fields, has changed the
scene. PLOS, which is now the largest scientific journal worldwide with around
14.000 articles per year, has been followed by a chain of new established journals
from other publishers copying the model. Just to name a few new Open Access
journals, and having a look at the publishers in the background, this following list
shows how important mega journals have become: Springer Plus, BMJ open, Cell
reports, Nature communications, Nature Scientific Reports, and Sage Open.
Giving away all rights to the publisher when signing an author’s contract has
been one of the strongest points of criticism for years. Discussions on how authors
can retain copyright brought Creative Commons licenses into the focus. Today,
Creative Commons licenses like CC-BY or CC-BY-SA have become a de facto
standard in Open Access publishing (see as an example Wiley’s recent move to
CC-by).32 ‘‘Re-use’’ is the catchword for the perspective which has been opened
by introducing such licenses.
Benefits
Open Access publications proved to have citation advantages (Gargouri et al.
2010) resulting from open accessibility of scholarly results formerly only available
in closed access. It guarantees faster communication and discussion of scientific
results. Therefore, it perfectly assists in fulfilling the most basic scholarly need:
communication. Open Access also promotes transparency and insight for the
public into scientific outcomes (Voronin et al. 2011). The outreach of scientific
work is stimulated.
Tools for a new and comprehensive findability and intensive data mining to
openly accessible texts will certainly be available. This will make interdisciplinary
work easier and productive. Reuse, due to open licenses, the ‘‘possibility to
translate, combine, analyze, adapt, and preserve the scientific material’’ is easier
and will lead to new outcomes (Carroll 2011).
31 PLOS: www.plosone.org
32 Wiley Moves Towards Broader Open Access Licence: http://eu.wiley.com/WileyCDA/
PressRelease/pressReleaseId-104537.html
Open Access: A State of the Art 151
The statement ‘‘In the online era, researchers’ own ‘mandate’ will no longer just
be ‘publish-or-perish’ but ‘self-archive to flourish’’’ (Gargouri et al. 2010) can be
extended to ‘‘researchers’ own ‘mandate’ will no longer just be ‘publish-or-perish’
but give Open Access to flourish’’.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Open Access: A State of the Art 153
Novel Scholarly Journal Concepts
Peter Binfield
It is not the strongest of the species that survives, nor the most
intelligent, but the one most responsive to change.
—Charles Darwin
Abstract Recent years have seen a great deal of experimentation around the basic
concept of the journal. This chapter overviews some of the more novel or inter-
esting developments in this space, developments which include new business
models, new editorial models, and new ways in which the traditional functions of
the journal can be disaggregated into separate services.
Introduction
For a long period following the invention of the Academic Journal in the 17th
Century, the journal evolved very little, and certainly did not change substantially
from the original format. It was perhaps with the formalization of the Peer Review
process (as we have come to know it) that the concept of the journal made its
greatest leap. Other than that development, very little changed in over 300 years…
However, since the advent of the Internet, the ‘traditional’ concept of the
Academic Journal has been under pressure. Although subscription titles moved
online quite early in the Internet era, it has only been with the more recent, and still
accelerating, move towards Open Access (an innovation which was made possible
thanks to the enabling technology of the Internet) that there has been a consid-
erable amount of experimentation around what a journal is, or could be.
Because of the size of this industry and the scale of experimentation which is
underway, the examples listed in this chapter are not intended to represent a
comprehensive list, but simply to highlight some of the representative experiments
that are happening today.
P. Binfield (&)
PeerJ, CA, USA
e-mail: pete@peerj.com
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_10,  The Author(s) 2014
155
Novelty in the Business Model
Clearly the predominant business model in journal publishing has always been the
subscription model, however there have been several recent developments which
aim to provide alternatives to this established model. The most obvious alternative
in today’s marketplace is the Open Access ‘author pays’ Article Publication
Charge (APC) model which has been successfully used by the likes of BioMed
Central, PLOS, Hindawi, and Frontiers, to name a few. This model will be well
understood by most readers, so we shall not dwell on it here. Instead it is inter-
esting to consider some of the other Open Access business models which are in the
marketplace today:
Free to Publish: e-Life1 is a highly selective, Open Access journal which is
entirely financed by research funders (Max Planck Society, the, Welcome Trust
and the HHMI). Because of this funding, the journal does not charge authors a
publication fee (hence it is free to publish, as well as free to read). E-Life is
perhaps the most recent, and visible, example of a ‘free-free’ journal but clearly
many other journals are funded by entities such as Institutions, or Societies to the
extent that their costs can also be entirely covered.
Individual Membership Models: PeerJ2 (see Fig. 1) is an Open Access journal
which offers authors a Lifetime Membership, in return for which authors can
publish freely for life. Membership fees start at $99 (allowing authors to publish
once per year) and rise to $299 (which allows authors to freely publish as many
articles as they wish). All co-authors need to be paying members with the correct
Membership level. Membership fees can be paid either before editorial Accep-
tance; or authors can submit for free and become a Member only at the time of
Editorial Acceptance (for a slight price increase). This model is very new, and
clearly orthogonal to a traditional APC fee (as it shifts payment from a ‘‘fee per
publication’’ to a ‘‘fee per author’’)—therefore it is being watched with some
interest, to see how the model might affect the approach of the publisher towards
its ‘customers’ (who are now Members).
APC Fees Centrally Negotiated: SCOAP3 (the Sponsoring Consortium for
Open Access Publishing in Particle Physics) has been a multi year project by a
consortium of high energy physics laboratories aiming to flip their entire field from
a subscription basis to Open Access. SCOAP3 has raised committed funding from
major libraries and research centers worldwide such that it has been able to take
that commitment and approach the publishers of the main subscription titles in
their field, requesting that they tender for the Open Access business of the com-
munity. The arrangement comes along with a promise that committed institutions
will no longer subscribe to journals, but will instead fund the Open Access fees of
the researchers in the field.
1 eLIFE: http://www.elifesciences.org/the-journal/
2 Disclosure: The author of this chapter is a Co-Founder of PeerJ.
156 P. Binfield
Novelty in the Editorial Model
For a very long period there was no formal peer review process in the sciences and
as recently as the 1970s, many journals did not formally peer review everything
they published. However, since that time, the process of peer review has become
firmly intertwined with the concept of the scholarly journal. The peer review
which has evolved in this way has largely tried to do two things at the same time:
(1) to comment on the validity of the science, and suggest ways in which it could
be improved; and (2) to comment on the interest level (or ‘impact’) of that science,
Fig. 1 The PeerJ homepage
Novel Scholarly Journal Concepts 157
in an attempt to provide a sorting, or filtering mechanism on the mass of submitted
content. However, with the advent of the Internet; the development of online only
‘author pays’ business models; and the development of improved methods of
search and discovery, this second aspect of peer review has become increasingly
less important.
It was in this environment that PLOS ONE (from the Public Library of Science)
was conceived 3 a journal which would peer review all content, but only ask the
reviewers to comment on the scientific and methodological soundness, not on the
level of interest (or ‘impact’, or ‘degree of advance’) of the article. PLOS ONE has
proven to be wildly successful—it launched in December 2006, and in 2012 alone
it published 23,464 articles4 (approximately 2.5 % of all the content indexed by
PubMed in that year, and making it the largest journal in the world by several
multiples) and continues to grow year on year. This success did not go unnoticed -
PLOS ONE coined a new category of journal, the ‘‘Open Access Megajournal’’
and, with it, gave rise to a number of similar journals (employing similar editorial
criteria).
The archetypal features of a MegaJournal seem to be: (1) editorial criteria
which judges articles only on scientific soundness; (2) a very broad subject scope
(for example the whole of Genetics or the whole of Social Sciences); (3) an Open
Access model employing a payment model (typically APC) which is set up for
each article to pay its own costs; (4) a large editorial board of academic editors (as
opposed to a staff of professional editors).
Although some megajournals are not completely transparent about their edi-
torial criteria (perhaps for fear of harming their brand), a non-exhaustive list of
these ‘MegaJournals’ would currently include (in no particular order) BMJ Open,5
SAGE Open,6 PeerJ, Springer Plus, Scientific Reports (from Nature),7 Q Science
Connect,8 Elementa,9 Optics Express,10 G3 (from the Genetics Society of
America),11 Biology Open (from the Company of Biologists),12 IEEE Access,13
AIP Advances, The Scientific World Journal, Cureus, F1000 Research and FEBS
3 Disclosure—the author of this chapter previously ran PLOS ONE
4 PLOS ONE Search: http://www.plosone.org/search/advanced?pageSize=12&sort=&query-
Field=publication_date&startDateAsString=2012-01-01&endDateAsString=2012-12-31&unfor-
mattedQuery=publication_date%3A[2012-01-01T00%3A00%3A00Z+TO+2012-12-31T23%
3A59%3A59Z]+&journalOpt=some&filterJournals=PLoSONE&subjectCatOpt=all&filterArticle
TypeOpt=all.
5 BMJ open:http://bmjopen.bmj.com/.
6 SAGE Open: http://sgo.sagepub.com/.
7 Scientific Reports: http://www.nature.com/srep/index.html.
8 Q Science Connect: http://www.qscience.com/loi/connect.
9 Elementa: http://elementascience.org/connect.
10 Optics Express: http://www.opticsinfobase.org/oe/home.cfm.
11 G3: http://www.g3journal.org/.
12 Biology Open: http://bio.biologists.org/.
13 IEEE Access: http://www.ieee.org/publications_standards/publications/ieee_access.html.
158 P. Binfield
Open Bio.14 In addition, it has been argued that the BMC Series,15 the ‘‘Frontiers
in…’’ Series,16 and the ISRN Series from Hindawi17 (all of which apply similar
editorial criteria, but attempt to retain subject-specific identity to each journal in
their Series) should also be considered megajournals, although they typically seem
to downplay that association.
Novelty in the Peer Review Model
Although the megajournals have successfully implemented a single change to the
peer review process (not judging based on impact), there are other innovators who
have journals (or ‘journal-like’) products which are trying other variations with
their peer review. Some illustrative examples are:
Hindawi’s ISRN Series algorithmically identifies suitable peer reviewers
(mainly from its pool of Editorial Board members), and then operates an anony-
mous peer review process which culminates in a ‘vote’ between the peer
reviewers. If all peer reviewers are in agreement to either accept or reject the
article then it is accepted or rejected. However, if there is any disagreement as to
the decision, then the reviews are shown to all the peer reviewers and they are
invited to change their opinion. After this second round, the decision goes with the
majority opinion (even if not unanimous). Once accepted, the authors are given the
option of voluntarily revising their manuscript based on the feedback. This process
is described, for example, at Hindawi’s Website.18
By contrast, F1000 Research receives submissions and places them publicly
online before peer review. Reviews are then solicited (typically from the Editorial
Board) and reviewers must sign their reports. The reports can be ‘approved’,
‘approved with reservations’, or ‘not approved’ (Fig. 2). Provided an article
accrues at least one ‘approved’ and two ‘approved with reservations’ (or simply
two ‘approved’ reports) then it is considered to be ‘published’ and subsequently it
is indexed in PubMed Central, and Scopus. Authors are not obliged to revise their
manuscripts in light of feedback. Because content is posted online even before peer
review has started, F1000 Research is thus composed of content in varying states
of peer review, and as such can be thought of as a cross between a journal and a
preprint server.
14 FEBS Open Bio: http://www.journals.elsevier.com/febs-open-bio/.
15 BMC Series: http://www.biomedcentral.com/authors/bmcseries.
16 Frontiers: http://www.frontiersin.org/about/evaluationsystem
17 Hindawi Journals: http://www.hindawi.com/isrn/.
18 Hindawi ISRN Obstetrics and Gynecology Editorial Workflow: http://www.hindawi.com/isrn/
obgyn/workflow/.
Novel Scholarly Journal Concepts 159
The PLoS Currents series19 of 6 titles can also be thought of as a cross between
a journal and a preprint server. There is no publication fee and any content posted
to a PLOS Current is submitted using an authoring tool which automatically
generates XML. The content is evaluated by an editorial board, and these boards
typically look to make very rapid decisions as to whether or not to publish the
content (they are not typically recommending revisions or performing a full peer
review). Because of this process, articles can go online within a day or two of
submission, and are then indexed by PubMed Central. Although an interesting
concept, to date PLOS Currents have not received a large number of submissions.
And then, of course there are the true ‘PrePrint Servers’. Two defining features
of the preprint server are that (i) the content is typically un-peer reviewed, and (ii)
they are usually free (gratis) to submit to. Because a preprint server is rarely
thought of as a ‘journal’ (and this chapter is devoted to innovations around the
journal concept) we shall not spend much time on them, other than to provide an
illustrative list which includes the arXiv (in Physics and Mathematics), PeerJ
PrePrints (in the Bio and Medical sciences), Nature Precedings20 (now defunct),
FigShare (a product which is ‘preprint-like’, and covering all of academia); the
Fig. 2 The article listing for F1000 Research, highlighting the way that evaluations are
presented
19 PLOS Currents: http://currents.plos.org/.
20 Nature Precedings: http://precedings.nature.com/.
160 P. Binfield
Social Sciences Research Network (SSRN), and Research Papers in Economics
(RePEc).
The Disaggregation of the Journal Model
Historically, a journal has performed several functions such as ‘archiving’, ‘reg-
istration’, ‘dissemination’, and ‘certification’ (there are others, but these four have
historically defined the journal). However it is increasingly evident that these
functions do not need to all take place within a single journal ‘container’—instead
each function can be broken off and handled by separate entities.
Several interesting companies are arising to take advantage of this way of
thinking. Although each service can be thought of as an attempt to ‘disaggregate’
the journal, they are also being used by existing journals to enhance and extend
their features and functionality. As with preprint servers, none of these products
are really thought of as a journal, but it is certainly useful to be aware of them
when considering the future development of the journal model:
Rubriq is a company which attempts to provide ‘‘3rd party peer review’’—
authors submit to Rubriq who then solicit (and pay) appropriate peer reviewers to
provide structures reports back to the author. Another example of this kind of
thinking is with the Peerage of Science (Fig. 3). By using services like this,
authors can either improve their articles before then submitting to a journal; or
they can attempt to provide their solicited peer reviews to the journal of their
choice in the hope that this will shorten their review process.21
‘Alt-Metrics’,22 or ‘Article Level Metrics’ (ALM) are tools which aim to
provide 3rd party ‘crowdsourced’ evaluation of published articles (or other
scholarly content). If we envision a future state of the publishing industry where
most content is Open Access, and a large proportion of it has been published by a
megajournal, then it is clear that there are considerable opportunities for services
which can direct readers to the most relevant, impactful, or valuable content for
their specific needs. Pioneered by the likes of PLOS23 (who made their ALM
program entirely open source), the field is being pushed forward by newly started
companies. The major players in this space are Impact Story (previously Total
Impact), Altmetric and Plum Analytics and all of them attempt to collate a variety
of alt-metrics from many different sources, and present them back again at the
level of the article (Fig. 4).
One particularly interesting provider of ‘alt metrics’ is F1000Prime24 (from the
‘‘Faculty of 1,000’’). Previously known simply as ‘Faculty of 1000’, F1000Prime
21 Disclosure—the author of this chapter is on the (unpaid) Advisory Panel of Rubriq
22 Altmetrics Manifesto: http://altmetrics.org/manifesto/.
23 PLOS Article-Level Metrics: http://article-level-metrics.plos.org/.
24 F1000Prime: http://f1000.com/prime.
Novel Scholarly Journal Concepts 161
Fig. 3 The peerage of science homepage
Fig. 4 The impact story homepage, highlighting which sources they index and how they present
metrics for a single article
162 P. Binfield
makes use of a very large board of expert academics who are asked to evaluate and
rate published articles regardless of the publisher. In this way, they form an
independent review board which attempts to direct readers towards the articles of
most significance. Although F1000Prime only evaluates perhaps 2 % of the lit-
erature (and hence is not comprehensive), it is clear that an approach like this fits
well with a concept of disaggregating the role of ‘content evaluation’ (or ‘content
filtering’) from the journal process.
Conclusion
As can be seen from this chapter, there is a great deal of experimentation hap-
pening in the journal space today. It remains to be seen which experiments will
succeed and which will fail, but it is certainly the case that the last decade has seen
more experimentation around the concept of the journal than its entire 350 year
history prior to this point. We live in interesting times.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
Novel Scholarly Journal Concepts 163
The Public Knowledge Project: Open
Source Tools for Open Access to Scholarly
Communication
James MacGregor, Kevin Stranack and John Willinsky
Abstract This chapter describes how the Public Knowledge Project, a collective of
academics, librarians, and technical genies, has been, since 1998, building open
source software (free) publishing platforms that create an alternative path to com-
mercial and subscription-based routes to scholarly communication. It sets out how
its various website platforms, including Open Journal Systems, Open Conference
Systems, and, recently, Open Monograph Press, provide a guided path through the
editorial workflow of submission, review, editing, publishing and indexing. Thou-
sands of faculty members around the world are now using the software to publish
independent journals on a peer-reviewed and Open Access basis, greatly increasing
the public and global contribution of research and scholarship.
Introduction
The digital transformation of scholarly communication has given rise to a wealth
of new publishing strategies, models, ands tool over the last two decades. Many of
these developments revolve around this new technology’s seeming promise to
increase the extent and reach of knowledge dissemination on a more equitable and
global scale (cf. Benkler 2006). At first reluctantly, but now with increasing levels
of interest, scholarly publishing is turning to the Internet as the preferred method
of dissemination. This poses a set of core challenges: How can scholars, regardless
of geographic location or institutional resources, participate in, learn from, and
contribute to the global exchange of knowledge? How can those involved in
J. MacGregor (&)  K. Stranack
Simon Fraser University Library, Burnaby, Canada
e-mail: jmacgreg@gmail.com
J. Willinsky
Stanford University, Stanford, USA
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_11,  The Author(s) 2014
165
publishing scholarly work maintain high standards of quality for this work, while
further advancing the long-standing research goals of openness and increased
access to knowledge?
The responses to such challenges are prolific and growing, proving to be source
of exciting research and development for scholarly communication. The whole
arena is complicated by the mix of, on the one hand, large corporate publishing
entities, seeking to preserve some of the highest profit margins in publishing, and,
on the other, small independent scholar-publishers who see their work as a service
to their colleagues and a commitment to the pursuit of learning, while in the
middle are scholarly associations that have grown dependent over the years on
publishing revenues for their survival (cf. Willinsky 2009).
Within this larger picture, the Public Knowledge Project (PKP) represents a
modest instance of research and experimentation in a new generation of publishing
tools that would lower the barriers among scholars and scientists interested in
playing a more active role in publishing and in seeing this work reaching a much
wider audience. What success the project has achieved over the years in devel-
oping software that is used by journals in many different parts of the world can be
attributed to the collective wisdom, as well as trial and error, of the team involved
in this project. This wisdom has found its expression in, for example, the early
adoption of open source and community development models; the active devel-
opment of the international PKP community; and the feedback of users in guiding
software and workflow design decisions that reflected principles of simplicity,
interoperability, accessibility, and openness, without sacrificing capability.
History of the Project
The Public Knowledge Project was started at the University of British Columbia
(UBC) in Vancouver, Canada in 1998 with a small number of student developers
working under the direction of John Willinsky, a faculty member in the Faculty of
Education. The project began on two levels. It was involved in researching various
models for creating a more coordinated approach to scholarly publishing that
would increase its public value (cf. Willinsky 1999). It also sought to develop
something practical and useful, in the form of online software, for the journal
community, which was only beginning, at the turn of the twenty-first century, to
think about moving its publishing operations and its journals to a web-based
environment. The project’s goal from the outset was to find ways of increasing
public and global access to research and scholarship. As such, it was an early
participant in the Open Access movement that sought to develop ways of creating
peer-reviewed journals that did not charge readers for access to their content
(cf. Suber 2012).
In 2005, Willinsky led PKP into a partnership with Lynn Copeland and Brian
Owen at the Simon Fraser University (SFU) Library. It was becoming clear that
the research library was emerging as a key player in efforts to reform scholarly
166 J. MacGregor et al.
communication around technology. And SFU Library quickly demonstrated how
productively this sort of partnership could work, by providing management and
systems support. In 2007, Willinsky left UBC to take up a position at Stanford
University in the Graduate School of Education, creating a strong institutional link
for the project between Stanford and the SFU Library, with a greater emphasis at
Stanford on the related research questions in scholarly communications, while
matters of technology development and innovation are centered around the SFU
Library.
As for PKP’s software, an early result of the project was the creation and 2001
release of Open Journal Systems (OJS) 1.0, a free, open source journal publication
management system, which provided an online platform for accepting submis-
sions, performing double blind peer review, editing, publishing, and dissemination.
OJS provided the necessary technological infrastructure for many journals making
the transition from print to online, as well as being the foundation for emerging
‘‘born-digital’’ journals. Today, PKP has not only created OJS, but also Open
Conference Systems (OCS, for managing conferences), Open Monograph Press
(OMP, for managing book publication), and Open Harvester Systems (OHS, for
metadata harvesting).
The Project currently employs over a dozen developers, librarians and library
staff at Simon Fraser University, Stanford University, and elsewhere in the world.
Most are active academically as students and/or as scholars. There is no physical
PKP ‘‘office’’: while most PKP associates live in Vancouver, others live in New
Brunswick, Palo Alto, Brazil, and elsewhere. As with other open source software
initiatives, community collaboration is at the forefront of the PKP development
model, and the PKP user community continues to grow and influence application
development.
To take one community example, the PKP Support Forum has over 4,500
members and more than 33,000 posts, and generates upwards of ten new posts a
day; many of these posts contain vital bug reports and feature requests.1
Translation support across all PKP applications is growing: OJS alone now
includes 27 community-contributed translations. And the global install base of
PKP applications expands from month to month, with over 3,500 active journals
currently using OJS. Half of these titles are edited and published in developing
countries. Close to 90 percent of the titles publish Open Access editions, with very
few charging article processing fees. Their secret is operating on very low-over-
head and obtaining some measure of institutional support (cf. Edgar and Willinsky
2010).
In both its research and development initiatives, PKP continues to be funded, as
it was originally, by a range of different government and foundation grants.2 In
2012, however, PKP introduced two new funding models to ensure its sustain-
ability, which involved growing responsibilities around the expanded number of
1 PKP: http://pkp.sfu.ca/support/forum
2 PKP Funding Partners: http://pkp.sfu.ca/funding_partners
The Public Knowledge Project 167
journals and conferences dependent on its software. The two models involved, first
of all, strengthening PKP’s hosting services for journals and conferences using its
free software, and, secondly, the creation of an institutional sponsorship program
for research libraries, many of which were now providing PKP software to their
institutions.
On the hosting side, what was once a fledgling, ad-hoc initiative at Simon
Fraser University Library to mount journals was established as a distinct venture,
dubbed PKP Publishing Services (PKP|PS).3
The growth and professionalization of PKP|PS has required a deeper level of
commitment to infrastructure: hardware, network uptime, and software manage-
ment across hundreds of installed instances of OJS, OCS and OMP. PKP|PS
currently hosts over 200 journals and conferences (with a small number of OMP
instances on the way), and now acts as a significant funding resource for PKP, not
to mention a critical vector for feedback from invested, informed, and day-to-day
users of the software.
Perhaps more significant for giving PKP a stronger institutional base, however,
is its sponsorship program, which has now over 30 participating institutions.4
Interested research libraries can sponsor the project directly on an annual basis,
or can become more involved as development partners. Development partners are
just that: they have access to the core PKP development team and are deeply
involved in long-term technical and administrative planning. This represents a new
model for PKP, which has traditionally been a very small and tight-knit group of
developers. Opening the team to a larger community is not without its challenges
in coordinating the work among different teams and locations. It is important to
stress here, however, that this isn’t simply a solution to PKP’s financial problem.
The sponsorship program provides a venue for PKP to interact with the larger
scholarly community in a way that previously did not exist. It is an open invitation
to participate as a patron and a peer in this project, and the investment of par-
ticipation is equally if not more important to the fundamental goals of the project
as any financial contribution.
The PKP Systems
In introducing the operating principles at work in PKP’s software, we are focusing
on the themes of simplicity and interoperability that underlie are approach to
supporting the managers, editors, authors, reviewers, copyeditors, layout design-
ers, and proofreaders, among others involved in the workflow that defines schol-
arly publishing. Our goal has always been to build systems that are not simply free
to use, but are easier to use to do the quality work that those in scholarly
3 PKP Publishing Services: http://pkpservices.sfu.ca
4 PKP Sponsorships: http://pkp.sfu.ca/sponsorships
168 J. MacGregor et al.
publishing has always involved. We have sought to build systems that not only
support the workflow that results in a sharing of research but that are instructive
and informative around the standards that have historically developed around these
practices, so that others who have not been previously part of the scholarly pub-
lishing community could begin to participate and contribute, as they were walked
through the process by the software design. We have, in this process, pursued a
number of holy grails, among them, the design of intuitive systems and the pro-
duction of automated systems. We continue down this path, not without our Monty
Python moments, having realized that scholarly publishing is not an inherently
intuitive process nor one that can be readily automated. We have reduced the
clerical tasks and greatly increased the portability of the editorial office, and a good
deal more than that, of course, as we outline in what follows.
(a) Simplicity in Technical Administration
All PKP application system requirements are both low and broad: all that is
needed to run OJS, OCS or OMP is a web server running PHP and a common
database system (MySQL or PostgreSQL). PKP also actively supports older ver-
sions of PHP, MySQL and PostgreSQL, out of consideration for users who may
not have access to newer technology. Users who download and install PKP
applications are often definitively non-technical, and so special care has been taken
to ensure that the installation and maintenance processes and documentation is
easy to understand and uncomplicated. The installation process is straightforward
and can be accomplished in a matter of minutes. Application maintenance,
including backing up files and general upkeep, is also simple and well docu-
mented. After installing OJS, OCS or OMP, the site administrator can create one
or more journal, conference or press instance on the site. Each instance takes only
a second to create; after they have been created, separate journal managers can be
enrolled in each instance, and these journal managers subsequently take over day-
to-day administration tasks.
(b) Simplicity in Management
After the journal, conference or press has been created, the manager completes
a guided setup process where all core components related to publishing workflow,
author and submission management, guidelines (author, reviewer, editing, etc.),
publishing, indexing, and the look and feel of the site are configured. (In OJS, the
setup is a five-step process; in OCS, it is a six-step process; in OMP the process is
a bit more extensive, with separate setup steps for press, website, publication,
distribution and user management; there is an initial wizard available for quick
press configuration, however.)
This stepwise workflow has been created and adhered to with different goals in
mind. For the new manager, these workflows provide a guided tour through many
of the options they must consider before they publish: OJS includes prompts for
ISSN information and a publication schedule, for example, while OMP provides
comprehensive series and category configuration options. For the experienced
The Public Knowledge Project 169
manager, these setup options are easily and centrally accessible for periodic
review.
This isn’t a case of ‘‘simple is as simple does,’’ however. A great deal of
behind-the-scenes automation and task/service management is included in OCS,
OJS and OMP, and all three applications offer far more capability than may be
assumed from their relatively straightforward configuration processes. Most of
these services involve promoting accessibility and visibility of the journal’s
published content on the web. For example, Google Scholar requires article
information to be available to its web crawlers in very specific ways; OJS does this
automatically, with no further configuration needed.5
(c) Simplicity of Submission
Each application’s submission process has been refined to be as simple as
possible for new and experienced authors alike. Each application uses a multi-step
submission process (no more than five steps in any application). Each step serves a
specific purpose, from informing the author of any copyright or other require-
ments; to providing submission indexing metadata; to requesting submission and/
or supplementary files; to confirming the submission. Authors are aware at all
times of which step they are on, and what is needed of them. This process ensures
that all information relevant to the submission is gathered at the very beginning,
saving editors valuable time during later stages.
(d) Simplicity of Review
While implementations differ as required by the publishing format, all three
applications approach review and editing workflows in a philosophically similar
way. Peer review is the key quality control for scholarly communication, as well as
a source of improvement for this work. The review process, in particular for
reviewers, must be kept as simple and quick as possible, as reviewers often have
the least incentive to use the system and may balk at any impediment between
themselves and the review proper. Typically, in the review process, the reviewer
needs to agree to complete the review; download the submission files; and upload
review comments and/or review files to the system. Reviewers may log in directly
to the system to complete the review process, or editors may act on their behalf. To
assist editors in selecting reviewers, the system tracks a reviewers previous record
on areas of interest, time taken, number of reviews, and editor rating.
(e) Simplicity of Editing and Production
All three systems handle editing differently. OCS includes a relatively minor
editing step only if full paper submissions are being accepted by the conference,
whereas OJS and OMP both have full-scale editing workflows which can include
input from copyeditors, proofreaders, layout editors, and others. In the case of
OMP, editing and production workflows are handled separately: copyediting of
5 See Google Scholar: http://scholar.google.ca/intl/en/scholar/inclusion.html
170 J. MacGregor et al.
final draft files are handled in an editing stage, and the creation of production-
ready files (eBooks, PDFs, and so on) and the completion of all catalog infor-
mation for that particular manuscript are managed in a final production stage.
The PKP Program
(a) Enhancing Interoperability
The PKP development team actively pursues software interoperability with
different applications, frameworks and platforms where appropriate. Interopera-
bility is ideally facilitated via open, widely used and time-tested APIs, standards
and protocols. A number of interoperability standards common to the library and
scholarly publishing worlds have enjoyed a long history of support within PKP,
and support for new standards is added regularly (and in many cases in the form of
contributed plugins from the larger community).
Various information interchange mechanisms enjoy broad support across the
PKP applications. All three applications support the Open Archives Initiative
Protocol for Metadata Harvesting (OAI-PMH), which provides machine access to
published article (or book, or presentation) metadata for the use of indexing sys-
tems. Another source of interoperability comes from following XML standards,
particularly journal publishing standards such as the National Library of Medicine
(NLM) Journal Publishing Tag Set, have proven crucial to PKP’s efforts to provide
open, structured access to published scholarly content. XML is particularly well-
suited to sharing data and metadata online between applications, as it is human-
and machine-readable.
Other discrete interoperability projects are currently underway. PKP is part-
nering with the Dataverse Network initiative at Harvard to develop a set of plugins
that will provide deposit and display functionality between OJS and Dataverse
repositories.6 At the same time, the project is also working with the Public Library
of Science (PLoS) to provide Altmetrics7 for PKP applications, and with ORCID
to provide author disambiguation services.8 These services are usually imple-
mented as plugins, and allow different levels of access to data and metadata for
different online services and platforms, typically with very little needed in terms of
additional setup. Most importantly however, the service standards and protocols
are open, understood, and widely accepted throughout the scholarly and academic
library communities, ensuring a broad level of support and interoperability for
PKP applications.
6 Dataverse Network. http://thedata.org
7 PLOS Article-Level Metrics: http://article-level-metrics.plos.org/alm-info/
8 Altmetrics: http://www.altmetric.com/; Orcid: http://about.orcid.org/
The Public Knowledge Project 171
(b) Enhancing Accessibility
PKP promotes access to scholarly content in a number of ways. Interoperability
via open standards and services, discussed above, is of key importance to acces-
sibility: providing multiple access methods to content will of course increase
exposure. In this fashion, journals may have their content harvested by OAI-
capable metadata harvesters, can provide article DOI information to CrossRef, can
deposit into PubMed’s MEDLINE indexing service.9 All PKP applications are
search-engine-friendly, and include special HTML ‘‘meta’’ tags that are used by
Google Scholar to identify and present content properly. In addition, the appli-
cation’s HTML is written to specific standards, and special care is taken to ensure
general accessibility across a wide variety of browsers and operating system.
(c) Enhancing Openness
Open Source Software
PKP software applications have always been released as open source software,
under the General Public License.10
The software is free in two ways: It is free to download and use; and the source
code is freely available to download, view, and modify. There are a number of
reasons why PKP publishes these applications as open source software.
Firstly, our mandate to improve the access to and quality of scholarly research
has been helped immensely by providing the software free of charge: Researchers
from all over the world can download and use our software; in a very real sense,
journals from Indonesia and Sri Lanka can operate on the same field (or quality of
platform) as journals from the United States and Germany.
Secondly, our software has benefitted immeasurably from source code contri-
butions from many, many members of the scholarly community. Almost all
translations of the software have been contributed as code; bugs have been
identified and in many cases fixed by community members; and new features
(many in plugin format) have been contributed as well. Simply put, we would not
have been able to attain the quality and breadth of our software without following
an open source software community model.
Thirdly, while Open Access to scholarly research and open source software
models are not necessarily explicitly interlinked, they do share some of the same
philosophical underpinnings: Open Access to material; community collaboration;
etc. Following an open Source licensing model makes as much sense as promoting
an Open Access approach to scholarly research and to Open Science, more gen-
erally (cf. Willinsky 2005).
Open Community
PKP is a community project in many ways. The Project provides direct
developer access to anyone, via the PKP support forums and wiki. Anyone can
9 CrossRef: http://www.crossref.org/
10 Specifically, the GPL V2. See GNU Operating System.
172 J. MacGregor et al.
register on the forum and interact directly with the PKP development team for
technical support or development inquiries. The support forum also exists as an
active venue for questions and conversations from editors, with questions ranging
from accessibility, indexing and DOIs to how typical review and editorial work-
flows typically work.
This community engagement is international. PKP seeks to cooperate with
different community partners around the world (including translators, developers,
and supportive institutions in Europe, Africa, Latin America, China, and else-
where). PKP has worked with INASP and other organizations in delivering
scholarly publishing workshops in many parts of the world. It was worked with
INASP to build portals such as African Journals Online and Asia Journals Online,
which have created an online presence in Google Scholar and elsewhere for
hundreds of title.11 In addition, many independent user and training groups have
popped up throughout the world, operating without direct PKP support—for
example, one partner in Spain has developed an entire OJS training program and
support forum, while another, with IBICT in Brasilia has been offering workshops
across the country for years.12 That these community initiatives are blossoming
internationally, and largely without direct support from PKP, is a welcome marker
of success, and an indication of the broad acceptance of PKP software as a
scholarly publishing standard internationally.
Open Access
A key goal of PKP is to promote Open Access to scholarly research. As such it
is part of growing Open Access movement. Open Access was initially, at the turn
of the century, a radical challenge to the old print model, but it is now increasingly
embraced not just by small independent scholar-publishers, where it got its start,
but by the largest of scholarly publishing corporations, just as it is being supported
by government legislation requiring Open Access for funded research, with every
sign that Open Access may well become the norm for publishing research (cf.
Laakso and Björk 2012; Björk and Peatau 2012). With the development of mega-
journals, such as PLoS One publishing tens of thousands of Open Access articles a
year, and increasing use of ‘‘article processing fees’’ to guarantee that Open
Access can be a source of revenue, the momentum and incentive is transforming
the industry (cf. Frank 2012). While PKP continues to largely serve smaller Open
Access journals operated by scholar-publishers, efforts are underway to adapt its
approach to make the mega-journal model among the options that it offers to the
academic community.
Open Education
One of the challenges for sustaining, enhancing, and increasing Open Access is
lack of professional publishing experience among many in the growing commu-
nity. A new initiative of PKP is the development of tuition-free, open, online
training courses in the use of PKP software and online publishing and management
11 African Journals Online: http://www.ajol.info/; Asia Journals Online: http://www.asiajol.info/
12 OJS.es: http://www.ojs.es/; IBICT: http://www.ibict.br/
The Public Knowledge Project 173
skills.13 Working in conjunction with partners such as the International Network
for the Availability of Scientific Publications (INASP) and the Publishing Studies
Department at Kwame Nkrumah University Of Science and Technology (KNUST)
in Ghana, this new education program will help build local capacity for online
publishing and sustainable Open Access.14
(d) Enhancing Knowledge
The fundamental purpose of the Public Knowledge Project is to enhance the
quality of scholarly communication and global knowledge sharing. By providing
free and open source tools for professional monograph, journal, and conference
management, PKP has enabled scholars from around the world to launch their own
online publications with little or no money, and to build scholarly communities
around their areas of interest, and to share the results of their research with all (cf.
Willinsky and Mendis 2007).
Discussions of the developing world and Open Access often revolve around
making research from the leading publications from the developed world more
widely available. Programs such as HINARI, AGORA, and OARE have made
significant gains in this area.15 While this is no doubt important, it is equally
important for researchers in the developed world (and elsewhere) to hear the
voices from the South. In the past, a leading publisher may have rejected the
knowledge generated by a Ghanaian researcher because the significance of her
work was not understood or valued. She could instead publish it in a local print-
based research publication, but it would have a very limited readership, with
physical copies not making it far beyond her country’s borders. With the
increasing availability of the Internet in Africa, although still a challenge, and the
existence of free and open source tools for professional publishing, she has new
options. Locally produced online journals, with a global community of editors,
authors, and reviewers are increasingly available as a forum for her work, and
where a suitable local option doesn’t exist, she can now choose to collaborate with
colleagues to start her own online journal.
Conclusion
The Public Knowledge Project has worked hard with a good number of
organizations and institutions, editors and publishers, over the course of the last
decade-and- a-half to increase the options and alternatives available to the global
community of scholars and researchers. In the face of this new publishing medium
that has transformed so many aspects of communication, and with even more
13 PKP School: http://pkpschool.org
14 INASP: http://www.inasp.info/; KNUST: http://www.knust.edu.gh/pages/
15 Research4Life: http://www.research4life.org/
174 J. MacGregor et al.
changes clearly in the offing, it is too early to know or even predict what models
and methods are going to prevail as the digital era of scholarly communication
continues to unfold. Our project has always been to demonstrate ways in which
these new directions and opportunities might uphold long-standing historical
principles of openness, community, cooperation, experimentation, and questioning
that continue to underwrite the work of research and learning. The continuing
success of this work relies not only on the open nature of the project, but on the
passion and interests of this larger community in their desire to contribute ideas
and knowledge, as well as the always appreciated instances of well-formed code.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
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The Public Knowledge Project 175
Part III
Vision
Altmetrics and Other Novel Measures
for Scientific Impact
Martin Fenner
Good science, original work, always went beyond the body of
received opinion, always represented a dissent from orthodoxy.
However, then, could the orthodox fairly assess it ?
—Richard Rhodes mod. from Michael Polanyi in ‘‘Making of
the Atomic Bomb’’
Abstract Impact assessment is one of the major drivers in scholarly communi-
cation, in particular since the number of available faculty positions and grants has
far exceeded the number of applications. Peer review still plays a critical role in
evaluating science, but citation-based bibliometric indicators are becoming
increasingly important. This chapter looks at a novel set of indicators that can
complement both citation analysis and peer review. Altmetrics use indicators
gathered in the real-time Social Web to provide immediate feedback about
scholarly works. We describe the most important altmetrics and provide a critical
assessment of their value and limitations.
Introduction
Impact assessment of researchers and their research is central to scholarly com-
munication. In the last 25 years, we have seen a shift from individual qualitative
assessment by peers to systematic quantitative assessment using citation analysis
of journal articles. Arguably the impact of research can not be quantified, and
citation analysis falls short of a comprehensive analysis, but the journal as a filter
for relevant scholarly content and the Journal Impact Factor as a tool to quantify
the relevance of journals are at the core of how research is communicated and
evaluated today.
The central role of the journal (to distribute, filter, and help evaluate scholarly
content) has dramatically changed with the shift from print to electronic pub-
lishing, and is no longer appropriate for the assessment of impact. We can look at
M. Fenner (&)
Public Library of Science, San Francisco, CA, USA
e-mail: mfenner@plos.org
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_12,  The Author(s) 2014
179
citations of individual articles, and at other measures of impact using usage stats
and the Social Web. Moreover, impact assessment does not have to be confined to
journal articles; research outputs such as data publication can also be assessed.
Altmetrics is a young discipline that looks at new metrics based on the Social
Web for analyzing scholarship. Altmetrics are complementary to the citation-
based filters which we have relied upon for the past 50 years and try to overcome
some of their shortcomings: citations are slow to accumulate, and often miss new
forms of scholarly content such as datasets, software, and research blogs (Priem
et al. 2012a).
Altmetrics are challenging this established system, and are therefore seen by
many as either an opportunity or a threat to the current system of scholarly
communication. This potential makes altmetrics both fascinating and challenging,
as many discussions about altmetrics are often intermixed with other ideas about
how to change scholarly communication.
Terminology
Scientometrics is the science of measuring and analysing science.
Bibliometrics is a major subdiscipline of scientometrics which measures the
impact of scientific publications. Citation analysis is the most popular application
of bibliometrics.
Usage-based metrics use usage data (pageviews, document downloads, etc.) to
assess scholarly impact. The concept was popularized by the COUNTER
(Counting Online Usage of NeTworked Electronic Resources) and MESUR
(MEtrics from Scholarly Usage of Resources) projects.
Altmetrics is the creation and study of new metrics based on the Social Web for
analyzing and informing scholarship (Altmetrics Manifesto1). Altmetrics is a sub-
discipline of scientometrics. Altmetrics typically looks at individual research
outputs, including journal articles or datasets.
Article-level metrics are a comprehensive and multidimensional suite of trans-
parent and established metrics at the article level.2 They collect and provide
metrics for individual articles, rather than aggregating them per journal. Article-
level metrics include citations, usage data, and altmetrics. Article-level metrics are
typically associated with the publisher Public Library of Science (PLOS), who
1 Altmetrics: http://altmetrics.org/about
2 PLOS Article-Level Metrics: http://article-level-metrics.plos.org/alm-info/
180 M. Fenner
introduced them for all of their articles in 2009. Altmetrics and article-level
metrics are sometimes used interchangeably, but there are important differences:
• Article-level metrics also include citations and usage data
• Altmetrics can also be applied to other research outputs, such as research data
Metrics for other research works—presentations, datasets, software, etc.—typ-
ically include usage statistics and altmetrics, but also citations.
Author-level metrics aggregate the metrics of all research by a specific author.
Metrics can also be aggregated by institution, discipline, etc.
Post-publication peer review is the process whereby scientific studies are
absorbed into the body of knowledge (Smith 2011). This definition is much
broader and does not just include activities that are traditionally described as peer
review. In contrast to metrics, the focus is on the discussion of a paper in com-
ments, blog posts, and citations. A broader term with similar meaning is post-
publication activity.
History
In 2008 Dario Taraborelli published a paper on soft peer review, advocating social
bookmarking tools for post-publication peer review (Taraborelli 2008). Neylon
and Wu described the PLOS Article-Level Metrics service launched in 2009 in an
article published the same year (Neylon and Wu 2009). Priem and Hemminger
published an article in July 2010 that describes scientometrics 2.0 and called for
new metrics based on Web 2.0 tools (Priem and Hemminger 2010). Groth and
Gurney studied chemistry science blogging about scholarly papers and presented
their findings at the Web Science Conference 2010 (Groth and Gurney 2010).
The Altmetrics manifesto was published in October 2010 by Jason Priem, Dario
Taraborelli, Paul Groth and Cameron Neylon (Priem et al. 2010).
ReaderMeter is a web service that tracks the number of Mendeley readers of all
papers of a particular author. ReaderMeter was launched in late 2010 and is the
first working altmetrics service. The first altmetrics workshop was was altmet-
rics11, held at the ACM Web Science Conference 2011 Workshop3 in June 2011.
Hackathons are an important part of altmetrics history: a working prototype for
Total Impact (now ImpactStory) was put together at the Beyond Impact conference
in May 2011, and the idea of the ScienceCard project started at the Science Online
London conference in September 2011. Three of the 11 finalists of the Mendeley/
PLOS Binary Battle programming contest in September 2011 were altmetrics
applications. In 2012, we saw the launch of several altmetrics services, more
publishers implementing altmetrics for their journal articles, and an increasing
number of presentations and workshops dedicated to altmetrics.
3 ACM Web Science 2011: http://www.websci11.org/
Altmetrics and Other Novel Measures 181
Scholarly Research
Two workshops dedicated to altmetrics research and associated with the ACM
Web Science conference were held: June 2011 in Koblenz, Germany and June
2012 in Evanston, IL.
PLOS ONE launched the Altmetrics collection in October 2012, with initially 7
research articles published since June 2009.4
Much early altmetrics research has examined reference managers, particularly
Mendeley and CiteULike. Li et al. (2011) found 92 % of Nature and Science
articles in their sample had been bookmarked by one or more Mendeley users, and
60 % by one or more CiteULike users. Bar-Ilan (2012) showed 97 % coverage of
recent JASIST articles in Mendeley. Priem et al. (2012) reported that the coverage
of articles published in the PLOS journals was 80 % in Mendeley and 31 % in
CiteULike. Sampling 1,397 F1000 Genomics and Genetics papers, Li and Thel-
wall (2012) found that 1,389 of those had Mendeley bookmarks.
Studies have consistently found moderate correlation between reference man-
ager bookmarks and Web of Science (WoS) citations. Li et al. (2011) showed
r = 0.55 of Mendeley and r = 0.34 of CiteULike readers with WoS citations
respectively. Weller and Peters (2012) report similar correlation values for a
different article set between Mendeley, CiteULike, BibSonomy, and Scopus.
Bar-Ilan (2012) found a correlation of r = 0.46 between Mendeley readership
counts and WoS citations for articles in JASIST. User-citation correlations for
sampled Nature and Science publications were 0.56 (Li et al. 2011); Priem et al.
(2012b) report a correlation of 0.5 between WoS citations and Mendeley users
articles published by the Open-Access publisher PLOS.
Twitter has also attracted significant interest from altmetrics researchers. Priem
and Costello (2010) and Priem and Costello (2011) report that scholars use Twitter
as a professional medium for discussing articles, while Eysenbach (2011) found
that highly-tweeted articles were 11 times more likely become highly-cited later.
Analyzing the use of Twitter during scientific conferences, Weller and Puschmann
(2011) and Letierce et al. (2010) report that there was discipline-specific tweeting
behavior regarding topic and number of tweets, as well as references to different
document types including journal articles, blogs, and slides. Other sources have
examined additional data sources besides reference managers and Twitter,
investigating examined citation from Wikipedia articles (Nielsen 2007) and blogs
(Groth and Gurney 2010; Shema et al. 2012) as sources of alternative impact data.
4 PLOS Collections: http://www.ploscollections.org/altmetrics
182 M. Fenner
Use Cases
Altmetrics can complement traditional bibliometrics in a number of scenarios:
• Metrics as a discovery tool
• Data-driven stories about the post-publication reception of research
• Business intelligence for a journal, university or funder
• Evaluation of the impact of research and researchers
Metrics as a Discovery Tool
Information overflow has become a major problem, and it has become clear that
relying on the journal as a filter is no longer an appropriate strategy. Altmetrics
have the potential to help in the discovery process, especially if combined with
more traditional keyword-based search strategies, and with the social network
information of the person seeking information. The advantage over citation based
metrics is that we don’t have to wait years before we can see meaningful numbers.
The free Altmetric PLOS Impact Explorer5 is an example for a discovery tool
based on altmetrics and highlights recently published PLOS papers with a lot of
social media activity. Altmetric.com also provides a commercial service for
content from other publishers.
Data-Driven Stories About The Post-Publication Reception
of Research
Altmetrics can help researchers demonstrate the impact of their research, in par-
ticular if the research outputs are not journal articles, but datasets, software, etc.,
and if the impact is best demonstrated in metrics other than citations. ImpactStory6
focuses on this use case. Often creators of web-native scholarly products like
datasets, software, and blog posts are hard pressed to demonstrate the impact of
their work, given a reward system built for a paper-based scholarly publishing
world. In these cases, ImpactStory helps to provide data to establish the impacts of
these products and allow forward-thinking researcher. ImpactStory also gathers
altmetrics to demonstrate wider impacts of traditional products, tracking their
impact through both traditional citations and novel altmetrics.
5 Altmetric: http://www.altmetric.com/demos/plos.html
6 ImpactStory: http://impactstory.org
Altmetrics and Other Novel Measures 183
Business Intelligence for a Journal, University or Funder
The focus is not on the individual article, but rather on overall trends over time
and/or across funding programs, disciplines, etc. This is an area that the typical
researchers is usually less interested in, but is important for strategic decisions by
departments, universities, funding organizations, publishers, and others. This area
has been dominated by large commercial bibliographic databases such as Web of
Science or Scopus, using citation data. Plum Analytics7 is a new service that also
provide altmetrics and is focusing on universities. The publisher PLOS8 makes a
comprehensive set of citations, usage data and altmetrics available for all articles
they published.
Altmetrics as an Evaluation Tool
Traditional scholarly metrics are often used as an evaluation tool, including
inappropriate uses such as using the Journal Impact Factor to evaluate publications
of individual researchers. Before altmetrics can be used for evaluation, the fol-
lowing questions need to be addressed:
• Can numbers reflect the impact of research, across disciplines and over time?
• Does the use of metrics for evaluation create undesired incentives?
• Do the currently available altmetrics really measure impact or something else?
• How can we standardize altmetrics?
• How easily can altmetrics be changed by self-promotion and gaming?
The first two questions relate to more general aspects of using scientometrics
for evaluation, whereas the last three questions are more specific for altmetrics. All
these issues can be solved, but it will probably take some time before altmetrics
can be reasonably used for evaluation.
Author-level metrics can also include citations and usage stats. Citations are a
more established metric for impact evaluation, and citations based on individual
articles are much more meaningful than the metrics for the journal that a
researcher has published in. The Hirsch-Index (or h index, Hirsch 2005) is a
popular metric to quantify an individual’s scientific research output. The h index is
defined as the number of papers with citation number C h, e.g. an h index of 15
means a researcher has published at least 15 papers that have been cited at least 15
times.
7 Plum Analytics: http://www.plumanalytics.com
8 PLOS Article-Level Metrics: http://article-level-metrics.plos.org
184 M. Fenner
Example Metrics and Providers
A growing number of metrics are used by the altmetrics community, and the most
important metrics and providers are listed below. Not all metrics measure schol-
arly impact, some of them are indicators of attention, and in rare cases self-
promotion. Some metrics are good indicators of activity by scholars (e.g. citations
or Mendeley bookmarks), whereas other metrics reflect the attention by the general
public (e.g. Facebook or HTML views) (Table 1).
Metrics describe different activities: usage stats look at the initial activity of
reading the abstract and downloading the paper, whereas citations are the result of
much more work, they therefore account for less than 0.5 % of all HTML views.
Altmetrics tries to capture the activities that happen between viewing a paper and
citing it, from saving an article to informal online discussions.
Mendeley
Mendeley is one of the most widely used altmetrics services—the number of
articles with Mendeley bookmarks is similar to the number of articles that have
ciations. Mendeley provides information about the number of readers and groups.
In contrast to CiteULike no usernames for readers are provided, but Mendeley
provides basic information regarding demographics such as country and academic
position. Mendeley is a social bookmarking tool used by scholars and the metrics
probably reflect an important scholarly activity—adding a downloaded article to a
reference manager.
CiteULike
CiteULike is another social bookmarking tool, not as widely used as Mendeley and
without reference manager functionality. One advantage over Mendeley is that
usernames and dates for all sharing events are publicly available, making it easier
to explore the bookmarking activity over time.
Table 1 Categorizing metrics into target audiences and depth of interaction (cf. ImpactStory
2012)
Scholars Public
Discussed Science blogs, journal comments Blogs, Twitter, Facebook, etc.
Recommended Citations by editorials, Faculty of 1,000 Press release
Cited Citations, full-text mentions Wikipedia mentiones
Saved CiteULike, Mendeley Delicious, Facebook
Viewed PDF downloads HTML views
Altmetrics and Other Novel Measures 185
Twitter
Collecting tweets linking to scholarly papers is challenging, because they are only
stored for short periods of time (typically around 7 days). There is a lot of Twitter
activity around papers, and only a small fraction is from the authors and/or journal.
With some journals up to 90 % of articles are tweeted, the number for new PLOS
journal articles is currently at about 50 %. The Twitter activity typically peeks a
few days after publication, and probably reflects attention rather than impact.
Facebook
Facebook is almost as popular as Twitter with regards to scholarly content, and
provides a wider variety of interactions (likes, shares and comments). Facebook
activity is a good indicator for public interest in a scholarly article and correlates
more with HTML views than PDF downloads.
Wikipedia
Scholarly content is frequently linked from Wikipedia, covering about 6 % of all
journal articles in the case of PLOS. The Wikipedia Cite-o-Meter9 by Dario
Taraborelli and Daniel Mietchen calculates the number of Wikipedia links per
publisher. In the English Wikipedia the most frequently cited publisher is Elsevier
with close to 35,000 links. In addition to Wikipedia pages, links to scholarly
articles are also found on user and file pages.
Science Blogs
Blog posts talking about papers and other scholarly content are difficult to track.
Many science bloggers use a blog aggregator, Research Blogging, Nature Blogs
and ScienceSeeker being the most popular ones. The number of scholarly articles
discussed in blog posts is small (e.g. less than 5 % of all PLOS articles), but they
provide great background information and can sometimes generate a lot of sec-
ondary activity around the original paper (both social media activity and
downloads).
9 Wikipedia Cite-o-Meter: http://toolserver.org/*dartar/cite-o-meter/
186 M. Fenner
Altmetrics Service Providers
Comprehensive altmetrics are currently only available from a small number of
service providers. This will most likely change in the near future, as more orga-
nizations become interested both in analyzing altmetrics for their content (pub-
lishers, universities, funders) or for providing altmetrics as a service.
The Open Access publisher Public Library of Science (PLOS) was the first
organization to routinely provide altmetrics on a large number of scholarly articles.
The first version of their article-level metrics service was started in March 2009,
and PLOS currently provides usage data, citations and social web activity from 13
different data sources. The article-level metrics data are provided via an open
API10 and as monthly public data dump.
Altmetric.com is a commercial start-up that started in July 2011. They maintain
a cluster of servers that watch social media sites, newspapers and magazines for
any mentions of scholarly articles. The data are available to individual users and as
service for publishers.
ImpactStory is a non-profit service providing altmetrics since late 2011. They
provide both altmetrics and traditional (citation) impact metrics for both traditional
and web-native scholarly products, and are designed to help researchers better
share and be rewarded for their complete impacts.
Plum Analytics is a start-up providing altmetrics data to universities and
libraries. They also provide usage stats and citation data, and track research out-
puts beyond journal articles, e.g. presentations, source code and datasets.
At this time it is unclear how the altmetrics community will develop over the
next few years. It is possible that one or a few dominant commercial players
emerge similar to the market for citations, that a non-profit organization is col-
lected these numbers for all stakeholders, or that we see the development of a more
distributed system with data and service providers, similar to how usage data for
articles are distributed.
Challenges and Criticism
Many challenges remain before we can expect altmetrics to be more widely
adopted. A big part of the challenge is the very nature of the Social Web, which is
much more difficult to analyze than traditional scholarly citations.
1. the constantly changing nature of the Social Web, including the lack of com-
monly used persistent identifiers
2. self-promotion and gaming, inherit to all Social Web activities, and aggravated
by the difficulty of understanding who is talking
10 GitHub: https://github.com/articlemetrics/alm/wiki/API
Altmetrics and Other Novel Measures 187
3. Altmetrics is more interested in things that can be measured, rather than things
that are meaningful for scholarly impact. We therefore measure attention or
self-promotion instead of scholarly impact.
These challenges are less of a problem for discovery tools based on altmetrics,
but are hard to solve for evaluation tools. Altmetrics is still a young discipline and
the community is working hard on these and other questions, including standards,
anti-gaming mechanisms, and ways to put metrics into context.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Altmetrics and Other Novel Measures 189
Dynamic Publication Formats
and Collaborative Authoring
Lambert Heller, Ronald The and Sönke Bartling
‘‘We are Wikipedians. This means that we should be: kind,
thoughtful, passionate about getting it right, open, tolerant of
different viewpoints, open to criticism, bold about changing our
policies and also cautious about changing our policies. We are
not vindictive, childish, and we don’t stoop to the level of our
worst critics, no matter how much we may find them to be
annoying.’’
—Jimmmy Wales—one of the founders of Wikipedia.
Abstract While Online Publishing has replaced most traditional printed journals in
less than twenty years, today’s Online Publication Formats are still closely bound to
the medium of paper. Collaboration is mostly hidden from the readership, and ‘final’
versions of papers are stored in ‘publisher PDF’ files mimicking print. Meanwhile
new media formats originating from the web itself bring us new modes of transparent
collaboration, feedback, continued refinement, and reusability of (scholarly) works:
Wikis, Blogs and Code Repositories, to name a few. This chapter characterizes the
potentials of Dynamic Publication Formats and analyzes necessary prerequisites.
Selected tools specific to the aims, stages, and functions of Scholarly Publishing are
presented. Furthermore, this chapter points out early examples of usage and further
development from the field. In doing so, Dynamic Publication Formats are described
as (a) a ‘parallel universe’ based on the commodification of (scholarly) media, and
(b) as a much needed complement, slowly recognized and incrementally integrated
L. Heller (&)
German National Library of Science and Technology, Welfengarten 1B 30167 Hanover,
Germany
e-mail: heller@ub.fu-berlin.de
R. The
University of Design, Schwäbisch Gmünd, Germany
e-mail: rt@infotectures.com
S. Bartling
German Cancer Research Center, Heidelberg, Germany
e-mail: soenkebartling@gmx.de
S. Bartling
Institute for Clinical Radiology and Nuclear Medicine, Mannheim University Medical
Center, Heidelberg University, Mannheim, Germany
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_13,  The Author(s) 2014
191
into more efficient and dynamic workflows of production, improvement, and dis-
semination of scholarly knowledge in general.
Introduction
The knowledge creation process is highly dynamic. However, most of current
means of scholarly publications are static, that means, they cannot be revised over
time. Novel findings or results cannot contribute to the publications once pub-
lished, instead a new publication has to be released. Dynamic publication formats
will change this. Dynamic publication formats are bodies of text/graphic/rich
media that can be changed quickly and easily while at the same time being
available to a wide audience.
In this chapter we will discuss dynamic scholarly publication formats with
respect to their chances, advantages, and challenges. First, we begin with a revi-
sion of existing and past publishing concepts. We discuss how the growing body of
scholarly knowledge was updated and evolved using static forms of publishing.
This will be followed by a structured analysis of the characteristics of dynamic
publication formats, followed by a presentation of currently implemented solutions
that employ concepts of dynamic publications.
Historic Dynamic Publishing Using Printed Journals
and Books: Revising Editions or Publishing Novel Books
and Articles
For centuries scholarly books were improved and updated with new content. This
happened through releasing new editions. In subsequent editions, mistakes were
corrected, recent results incorporated, and feedback from the readership used to
improve the overall book. A sufficient demand for the reprinting of editions was a
necessity. Before reprinting the publisher invited the author to revise the next
edition (‘revised editions’1). The changes were usually marked and introduced in
the preface of the consecutive editions.
Many encyclopedias, handbooks, and schoolbooks became established brands,
which have been revised over and over again, sometimes over the space of decades.
In many examples the authors changed. In successive revisions parts were changed,
paragraphs rewritten, and chapters removed or added. In particularly vivid fields a
different ‘genre’ of book—the loose-paper-collections—were invented. Here,
carefully revised pages were sent out to subscribers on a regular basis.
1 see Wikipedia: http://en.wikipedia.org/wiki/Edition_(book)#Revised_edition
192 L. Heller et al.
Libraries provided not only access to the most recent version of books, but also
kept earlier editions for interested readers. Earlier editions were of historical and
epistemological interest. Recurrent book editions made it necessary to add the
consecutive edition number when referencing revised books.
Revising books allowed authors to keep track with novel developments. A book
usually presented a closed ‘body of knowledge’, a mere collection of indisputable
knowledge, often with a review character. Textbooks or encyclopedias were
specially structured books.
In contrast to books, scholarly articles were a snapshot of certain scientific
knowledge. In most scholarly fields, research results were published only once.
Scientific journal articles were not to be revised—if new findings occurred, new
articles were published. Publishing became the currency of research and around
the journal article methods to measure the performance of researchers were
developed.
The scholarly journal article and its ‘life cycle’ are currently under debate and
development. New mechanisms to publish scientific results are being widely
discussed; most opportunities have been opened up by the new possibilities that
were enabled by the Internet.
Some of the most prominent changes that have already found wide acceptance
so far are being reviewed in the following:
Preprint, Postprint and (Open) Peer Review
The fundamental interest of researchers is to publish their research results in a
straightforward fashion. This is in conflict with the publishers’ duties of filtering
good research from faulty research, rejecting papers with methods that are
insufficient to draw the stated conclusion, or denying research publication that are
out of the scope of the journal’s audience. The current gold standard supporting
editorial decisions is the peer-review process as organized by the publishers. Since
the peer-review process takes a significant amount of time and is one of the main
causes of delaying publications, some research disciplines developed a culture of
publishing so-called preprints. Preprints are preliminary, pre-peer-review versions
of original research articles which are submitted to repositories such as arXiv
(arxiv.org). It is acceptable to exchange preliminary versions with updates, for
example updates with applied changes that occur during peer-review, but older
versions are always available and cannot be removed. A publication identifier
points to the most recent version of the article, but older versions can be assessed
through a history function. Preprints are legally accepted by most journals. Some
journals even combine the acceptance with preprints together with an open peer-
review process in which all comments and changes can be tracked (Pöschl 2012).
Postprints are versions of the article which are published after peer-review, usually
by storing the article in repositories (see chapter Open Access: A State of the Art)
Dynamic Publication Formats and Collaborative Authoring 193
or by sending them via email. They contain all the changes that have resulted from
discourse with the peer-reviewers.
Follow-Ups and Retractions
Publishing preprints, postprints, or even the peer-review process allows the
tracking of the development of a final version of a scholarly article. Usually, after
peer-review, a final version of an article exists which must not be further changed.
After publication of the article, it is of interest to see how the article is being
received by the community. Its impact is measured by counting the amount of
citations to it, references which result at article-level (see chapter Altmetrics and
Other Measures for Scientific Impact). Databases such as the Web of Science,
Scopus, Google Scholar, ResearchGate, and non-commercial services such as
Inspire count these citations and provide more meta-analysis of articles. Refer-
ences to the article are visible and so follow-up articles can be identified. Current
databases do not give information about the context of a citation. It remains
unclear as to whether the article is referenced as a citation within the introduction,
a reference to similar ‘Materials and methods’, or whether the cited article is being
disputed in the discussion.
In cases of scientific misconduct, articles are retracted. Obviously, this happens
more and more often (cf. Rice 2013). Similarly, universities retract dissertations
and other scientific publications. Obviously, printed or downloaded articles cannot
disappear in the same way that online version of articles can be deleted. Libraries
and repositories keep the retracted books, articles, or dissertations in the archives
and just change the catalogue status to ‘retracted’. Publishers delete the online
version and books are no longer sold.
Current Aspects of the Publication System in Regard
to the Dynamic Knowledge Creation Process
The Production Process is not Visible to the Reader
With the advancement of the Internet, methods to cooperatively compile a text
(‘collaborative authoring tools’) are finding wider application. It is now possible to
trace each individual’s contribution to a text. Technically, it would be possible to
track all versions and comments that occur during the production process. Cur-
rently, final versions of scholarly publications do not contain traces of their pro-
duction process. The final version of a publication is a kind of finalized consent
from all its authors without any traces of the production process itself.
194 L. Heller et al.
The Contribution of Individual Authors is not Visible
Usually contributors to a scientific publication are stated in the list of authors.
Large research organizations provide guidelines on the requirements to qualify as
an author of an article. Only contributors should be listed as authors and there exist
guidelines of good scientific practice so as to clarify what acceptable contributions
are. Honorary authorships, e.g. authorships that are based on political consider-
ations, are not considered appropriate.2 The listing itself happens independently of
the quantifiable amount of actual text contribution. However, often the placement
and order of the authors give a hint on the amount and of the kind of contribution.
Various cultures exist; in many disciplines (e.g. life sciences) the first and the last
authors contributed most significantly to an article.
The distribution of third party funding is more and more based upon sciento-
metric measurements which depend on authorship. Since the incentives are set that
way, it is now very important to a researcher to have his contribution appropriately
acknowledged. In this context, conventions on the good scientific practice guiding
authorship qualifications have become much more important than in earlier times.
This is especially true in the context of growing author lists and increasing
manipulations. In practice, the distribution of authorship positions is a complex
process, often involving a non-transparent system of social and professional
dependencies.
Finalized Versions do not Allow Changes, Thus Making
Corrections and Additions Nearly Impossible
Despite the fact that the Internet allows for other procedures, the publication of a
scholarly manuscript is organized around the release date of the publication. After
the release of a scientific publication no corrections, additions, or changes are
possible. Only in strong cases of scientific misconduct, falsicification or manip-
ulation of findings will a retraction occur, usually with sweeping consequences for
the authors in question. Minor mistakes cannot be corrected. Only a couple of
journals provide online commenting functionality and these are not currently being
used in a relevant matter. However, this might change quite soon, for example by
merging in trackback functions as already used by weblogs. A scientific discourse
around a publication cannot occur, and if so, channels such as comments or
discussion forums are being used which are currently not credited. Only the dis-
cussion sections of new peer-reviewed publications are a chance for accredited
scholarly criticism.
2 Wikipedia: http://en.wikipedia.org/wiki/Academic_authorship#Honorary_authorship
Dynamic Publication Formats and Collaborative Authoring 195
Redundancy in Scientific Publications—Currently no Reuse
and Remixing
The scientific knowledge creation process is usually incremental. Once a certain
amount of scientific knowledge is gathered, a novel publication is released.
Depending on discipline, a full paper, thesis, or book is the most accepted form of
publication. Each release is considered to be a self-contained body of text,
understandable to fellow-scientists in the field. This makes redundancy in the
introduction, material, and methods a necessity. With this come certain legal and
ethical implications. The authors are currently in danger of not only being accused
of self-plagiarism, but also of copying from fellow scientists. Even if sections are
correctly referenced and citations are placed, many scientists reword phrases for
fear of being accused of plagiarism and scientific misconduct. Current conventions
prevent scientific authors from reusing well-worded introductions or other para-
graphs, despite the fact that from a truly scientific point of view, this would be
totally acceptable if enough new content and results besides the copied and reused
parts is present (Fig. 1). Also, obvious remixing of phrases and content from
Time
On topic XYZ On topic XYZ On topic XYZ
....
On topic XYZ
Scholarly publications today
Dynamic scholarly publications
....
On topic XYZ
Fig. 1 Today’s scientific publications are static—meaning finalized versions exist that cannot be
changed. Dynamic publication formats have become possible with the Internet. The publication
can now evolve with the development of new knowledge. In dynamic publications many parts
and texts can be ‘reused’ (as represented by the parts of the text that keep the color; new additions
represent novel scientific knowledge)
196 L. Heller et al.
several sources is currently not accepted (Fig. 2). This results in unnecessary
rewording, a greater workload, and potentially sub-optimal phrasing. The same
introduction, methodology description, and statements are rewritten over and over
again, making it sometimes difficult to identify the truly new contribution to a
scientific field (Mietchen et al. 2011). It is important to notice that in many
disciplines and scientific cultures, mainly humanities, textual reproduction with
precious words and in a literary manner is a considerable feat which is beyond the
pure transportation of information. Here, the reusing and remixing of content has
to be seen in a different context.
Legal Hurdles to Make Remixing and Reuse Difficult
Most publishers retain the copyright of a publication, which strictly limits the
reuse of text, pictures, and media. This banishment of remixing seems outdated
in the age of the Internet. Novel copyright concepts such as Creative Commons
(CC-BY) (see chapter Creative Commons Licences) will change this and will
make reuse and remixing possible.
On topic XYZ On topic ABC
On topic UVW
Fig. 2 Remixing is the concept of using text and parts of earlier publications to build a novel
publication; remixing is currently restricted through legal and scientific cultures, however,
remixing may become much more acceptable in the future—remixing has to be distinguished
from scientific plagiarism
Dynamic Publication Formats and Collaborative Authoring 197
Technical Hurdles in Reusing Content—‘‘Publisher PDF’’
Files are Mimicking Print
Some scientific publications are being reproduced in a way that makes the reuse of
articles and parts of articles technically difficult. Sometimes, the PDF version is the
only available version of an article. More open formats, such as HTML, XML,
LaTeX source code, or word documents are not always released and remain with
the publisher. Even the author themself suffers from significant hurdles in
accessing the final version of his or her personal publications (cf. Schneider 2011;
Wikiversity3).
The current publication system is a consequence of a scholarly knowledge
dissemination system which developed in times before the Internet when printing
and disseminating printed issues of papers were the only means of distributing
scientific results. The Internet made it possible to break with the limitations of
printed publications and allowed the development of dynamic publication formats.
Dynamic Publication Format—General Concept
Science as a whole is constantly developing. Novel insights, results, and data are
permanently being found. The prevailing current publication system is dynamic,
but its changes and iterations are too slow. The publication system developed long
before the Internet. With the Internet came new possibilities for publishing,
transporting results, and defining the nature of ‘a publication’. Dynamic publica-
tions can adapt to the development of knowledge. Just as Wikipedia is developing
towards completeness and truth, why not have scientific publications that develop
in pace with the body of scientific knowledge?
Dynamic Publication—Challenges
In the past a modality of publication was mainly shaped by the prevailing medium
(paper) and its distribution (mailing). New scientific results had to cross a certain
threshold to be publishable. This threshold was defined by the amount of effort that
was necessary to produce this publication and to distribute it. A publication had to be
somewhat consistent and comprehensible by itself. The forms of publications that
were available in the past are abstracts, talks, papers, reviews, and books (Fig. 3).
Since the Internet, the available publication methods are no longer limited to
this list. It became possible that virtually everybody can publish at very little or no
3 Wikiversity: http://en.wikiversity.org/wiki/Wikiversity:Journal_of_the_future
198 L. Heller et al.
cost. The limiting factor of the medium of paper and its distribution vanished.
Novel publication methods such as blogs, microblogs, comments, wiki updates, or
other publication methods complement the prevailing publishing methods (Fig. 4)
(Pochoda 2012).
Aspects of Dynamic Publication Formats
Dynamic
Dynamic publication formats are—as the name says—dynamic (Fig. 5), meaning
that no static version exists. Dynamic publications evolve. The changes can be
done on several formal levels, from letters and single words (‘collaborative
authoring tools’, ‘wikis’), to a few sentences (‘status updates’) and whole para-
graphs (‘blogs’, ‘comments’). Changes include deletions, changes, and additions.
However, implementations vary in terms of how permanent a deletion may be.
Abstract / talk
Paper
Review
Book
Microblog
Status update
Comment/blog
Wiki update
Letter
Completeness /
Audience /
Maturity
Timeliness / 
Promptness
Fig. 4 Today’s publication formats
Abstract / Talk
Paper
Review
Book
Completeness / 
Audience / 
Maturity
Timeliness / 
Promptness
Letter
Fig. 3 Classical publication formats before the Internet
Dynamic Publication Formats and Collaborative Authoring 199
Authorship
In collaborative authoring tools it is quite technically easy to precisely trace who
typed which text and who drew which figures. However, authorship in a scientific
publication currently represents much more than just an actual textual contribution.
It defines whose ideas and theories lead to it, in addition to the actual work that was
necessary to gather the scientific results. This is not adequately represented by the
actual contribution of the text. Authorship is a guarantor of quality and here
the personal reputation of a researcher is at stake. Therefore, clear statements of the
kind of contribution to a work provided by an author should be associated with a
publication. For example, many scientific journals request a definition of the role
each author played in the production process of the work. The contributions range
from the basic idea, actual bench work to revision of the article.
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Fig. 5 Dynamic publications: Working and public versions. The working versions are
collaboratively edited by a small group of authors. The authors can decide when a version or
a revision should become widely available. Depending on the platform, a formalized ‘gate-
keeping’ mechanism (consent among authors) and/or ‘peer-review’ as organized by a quality-
granting authority (journal) has to be passed. Working as well as published versions can be reused
by other authors
200 L. Heller et al.
Openness
Technically the whole textual creation process including all typing and editing
could be open. Furthermore, all commentary and discussion can be open. Certain
consequences are related to such openness. Not all versions of a document and
discussions are meant to be public. While openness can be seen as a tool for
assuring quality and preventing scientific misconduct, at the same time it puts
researchers under great pressure. Usually early versions of documents are full of
spelling mistakes and errors and not meant to be seen by the public; furthermore,
they usually lack approval from all coauthors.
A possible solution allows for some parts of the publication and editing process
to take place with limited visibility in a working version. After all authors have
approved a version or a revision, this version can become part of the public version
(Fig. 5). The step from working version to public version would be based on some
internal ‘gatekeeping’ criteria, such as the discussion and consent of all authors,
making the process similar to that of the peer-review process. However, the peer-
review is done by people other than the authors themselves and the peer-reviewing
process can be organized by a quality-granting authority such as a journal.
Tranclusion, Pull-Requests, and Forking—Lifecycle
and History
The lifecycle of a dynamic publication is much harder to define than the life cycle
of a static, traditional publication. Concepts such as ‘transclusion’,4 ‘pull-
requests’, and ‘forking’5 allow for different kinds of remixing and reuse of earlier
publications (Fig. 6). An important feature of dynamic publications is the avail-
ability of a history functionality so that older versions of the publication are still
available and referencing to the older versions can occur. This might not only be of
interest to historians of science, but may also be very valuable in assessing the
merits of earlier scientific discoveries and documenting scientific disputes.
Many of these remixing and reuse concepts stem from collaborative software
development and many of these are in turn far removed from the current per-
ception of the life cycle of scientific publications. It remains to be seen whether
they can be integrated into the scientific publishing culture so that the systems in
question benefit from it, and usability, as well as readability, can be assured.
4 Wikipedia: http://en.wikipedia.org/wiki/Transclusion
5 A concept derived from software development, but also applicable to texts. Wikipedia: http://
en.wikipedia.org/wiki/Fork_(software_development)
Dynamic Publication Formats and Collaborative Authoring 201
Publication Formats
Various publication formats exist. There are books, chapters, abstracts, tweets,
reviews, full-papers, and so on. Some publication formats are more likely to
benefit from the many remixing concepts of dynamic publications than others. In
many science cultures, reviews are constantly being released on similar topics,
sometimes on an annual basis. A dynamic publication concept would be ideally
suited to providing reviews of current developments: instead of publishing com-
pletely novel reviews, reviews could constantly be updated.
Content and Quality Control
The authors as the primary guarantors of quality remain untouched by the concepts
of dynamic publications. However, while the pre-publication peer-review of static
publications and the decision of editorial boards or publishers assured content in
the past, this is more flexible in dynamic publications. An open commenting
functionality can be seen as a form of post-publication review. The public pressure
that is associated with potentially unmasking comments urges the authors to
provide a high standard of output. If the commenting functionality is anonymous it
Time
“Pull”  
request
Transclusion
Forking
Fig. 6 Dynamic publications allow many novel concepts such as ‘forking’ (dividing one
publication into two branches of working versions), ‘transclusion’ (reuse of text or images from
another publication, potentially with live updates) and ‘pull requests’ (a certain way of including
updates from one forked working version into another: a ‘one time transclusion’)
202 L. Heller et al.
should be clear to all readers that non-qualified comments may occur; at the same
time, an anonymous commenting functionality may encourage true criticisms.
In actual implementations of dynamic publications, visibility of transclusions as
well as pull-requests has to be assured—otherwise misunderstanding of the actual
authorship may occur.
Cultural Background
Dynamic publications are a completely novel tool in scholarly publishing. Sci-
entists have to learn to use them and to understand their benefits and limitations.
Furthermore, scientometrics have to learn how to assess contributions that occur in
dynamic publications.
Some research fields are more suited to dynamic publication concepts, while
others are less so. There are research cultures that might implement dynamic
publications faster than others. Hard sciences/lab sciences are more suited for
dynamic publications. Here, often novel, incremental findings just require small
changes to a text, whereas in humanities comprehensive theories and interpretations
might not be as suitable to be expressed in well-circumscribed changes of text.
Current Implementation of Dynamic Publications
Traditional scholarly publication methods exist—and novel tools and platforms
that technically allow the realization of dynamic publication concepts have orig-
inated from the web. Tools that implement aspects of dynamic publishing are
presented here and their limitations explained.
Journal Articles and Books
Journal articles and books represent the gold-standard of scientific publishing. In
the world of books and journal articles there exists a point in time that clearly
divides the production phase from the reception phase of a scientific publication.
This point in time is when the publication is released and available to readers.
With this comes the expectation that the results and discussion of released sci-
entific results provided are ‘complete’ and ‘final’. When the neutral and often
anonymous peer-review process is finished, a publication is ‘fit to print’6. In most
6 Journalists of the New York Times in 19th century stated ‘‘All the news that’s fit to print’’
(cf. Encyclopædia Britannica Online: http://www.britannica.com/EBchecked/topic/850457/All-
the-News-Thats-Fit-to-Print).
Dynamic Publication Formats and Collaborative Authoring 203
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204 L. Heller et al.
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og
s
W
ik
is
or
ot
he
r
co
ll
ab
or
at
iv
e
au
th
or
in
g
to
ol
s
So
ci
al
co
di
ng
/G
it
H
ub
C
ol
la
bo
ra
ti
ve
co
nt
en
t
co
nt
ro
l
T
he
co
nt
en
t
is
pr
ov
id
ed
on
ly
by
th
e
au
th
or
s;
co
nt
en
t
an
d
re
le
va
nc
e
co
nt
ro
ll
ed
by
th
e
ed
it
or
ia
l
bo
ar
d
of
th
e
jo
ur
na
l
Si
ng
le
au
th
or
is
re
sp
on
si
bl
e
fo
r
co
nt
en
t;
co
m
m
en
ti
ng
is
al
lo
w
ed
W
id
e
co
ns
en
t
on
th
e
sc
op
e
of
th
e
pr
oj
ec
t;
co
nfl
ic
ts
w
il
l
be
ad
dr
es
se
d
an
d
so
lv
ed
by
m
aj
or
it
y
de
ci
si
on
s;
ev
er
yb
od
y
ca
n
co
nt
ri
bu
te
‘M
ai
nt
ai
ne
r’
de
ci
de
s
ab
ou
t
ac
ce
pt
an
ce
or
re
je
ct
io
n
of
ch
an
ge
s;
if
co
nfl
ic
ts
ex
is
ts
,
pr
oj
ec
t
ca
n
be
di
vi
de
d
in
to
tw
o
su
b
pr
oj
ec
ts
(‘
fo
rk
in
g’
)
T
ec
hn
ic
al
re
us
ab
ili
ty
Po
te
nt
ia
ll
y
po
ss
ib
le
,
bu
t
pr
ac
ti
ca
ll
y
li
m
it
ed
by
PD
F
fo
rm
at
;
li
tt
le
us
e
of
m
ar
ku
p
la
ng
ua
ge
s
an
d
re
m
ix
in
g
m
ec
ha
ni
sm
s
su
ch
as
‘‘f
or
ki
ng
’’
or
‘‘t
ra
ns
cl
us
io
n’
’
U
sa
ge
of
m
ar
ku
p
la
ng
ua
ge
s
in
cl
ud
in
g
H
T
M
L
;
bu
t
no
im
pl
em
en
ta
ti
on
of
re
m
ix
in
g
or
fo
rk
in
g
m
ec
ha
ni
sm
s
H
ig
hl
y
re
us
ab
le
;
co
nt
en
t
is
hi
gh
ly
st
ru
ct
ur
ed
;
fle
xi
bl
e
m
ar
ku
p
la
ng
ua
ge
;
tr
an
sc
lu
si
on
w
it
hi
n
th
e
pl
at
fo
rm
;
ea
si
ly
tr
ac
ea
bl
e
tr
an
sc
lu
si
on
H
ig
hl
y
re
us
ab
le
;
ex
am
pl
es
fo
r
us
ag
e
of
m
ar
ku
p
la
ng
ua
ge
s
su
ch
as
M
ar
kd
ow
n
an
d
Pa
nd
oc
fo
r
te
xt
pu
bl
ic
at
io
ns
;
fo
rk
s
ea
si
ly
an
d
of
te
n
do
ne
;
tr
ac
ea
bl
e
L
eg
al
re
us
ab
ili
ty
M
or
e
an
d
m
or
e
us
e
of
m
or
e
op
en
co
py
ri
gh
t
li
ce
ns
es
;
A
lm
os
t
on
ly
us
e
of
op
en
co
py
ri
gh
t
li
ce
ns
es
th
at
al
lo
w
re
us
e
V
er
y
hi
gh
;
w
ik
ip
ed
ia
-l
ik
e
or
cr
ea
ti
ve
co
m
m
on
s
li
ce
ns
es
V
er
y
hi
gh
;
fr
ee
li
ce
ns
es
th
at
al
lo
w
re
us
e
Im
pl
em
en
ta
ti
on
s
Pr
op
ri
et
ar
y
jo
ur
na
l
pl
at
fo
rm
s
or
op
en
jo
ur
na
l
pl
at
fo
rm
s
su
ch
as
O
pe
n
Jo
ur
na
l
Sy
st
em
of
pu
bl
ic
kn
ow
le
dg
e
pr
oj
ec
t
(s
ee
ch
ap
te
r
T
he
Pu
bl
ic
K
no
w
le
dg
e
Pr
oj
ec
t:
O
pe
n
So
ur
ce
T
oo
ls
fo
r
O
pe
n
A
cc
es
s
to
Sc
ho
la
rl
y
C
om
m
un
ic
at
io
n)
W
or
dP
re
ss
w
it
h
w
id
e
sp
re
ad
us
e;
ot
he
r
bl
og
gi
ng
so
lu
ti
on
s
M
ed
ia
W
ik
i
w
it
h
w
id
e
sp
re
ad
us
e;
pr
op
ri
et
ar
y
to
ol
s
su
ch
as
G
oo
gl
e
D
oc
s
G
it
H
ub
or
fr
ee
re
po
si
to
ry
pl
at
fo
rm
G
it
;S
ou
rc
ef
or
ge
or
C
V
S
H
is
to
ry
fu
nc
ti
on
T
ra
ce
s
of
cr
ea
ti
on
pr
oc
es
s
us
ua
ll
y
de
le
te
d;
no
hi
st
or
y
fu
nc
ti
on
N
o
tr
ac
es
of
cr
ea
ti
on
pr
oc
es
s;
no
hi
st
or
y
fu
nc
ti
on
C
om
pl
et
e
tr
ac
es
of
cr
ea
ti
on
pr
oc
es
s
an
d
hi
st
or
y
of
ar
ti
cl
es
;
se
am
le
ss
tr
ac
es
of
di
sc
us
si
on
C
om
pl
et
e
tr
ac
es
of
cr
ea
ti
on
pr
oc
es
s
an
d
hi
st
or
y
of
ar
ti
cl
es
;
se
am
le
ss
tr
ac
es
of
di
sc
us
si
on
(c
on
ti
nu
ed
)
Dynamic Publication Formats and Collaborative Authoring 205
T
ab
le
1
(c
on
ti
nu
ed
) S
ch
ol
ar
ly
ar
ti
cl
es
B
lo
gs
/m
ic
ro
bl
og
s
W
ik
is
or
ot
he
r
co
ll
ab
or
at
iv
e
au
th
or
in
g
to
ol
s
So
ci
al
co
di
ng
/G
it
H
ub
O
pe
nn
es
s,
in
ge
ne
ra
l
C
lo
se
d,
af
te
r
pu
bl
ic
at
io
n
af
te
r
pa
yw
al
l/
O
pe
n
A
cc
es
s
C
lo
se
d
un
ti
l
pu
bl
ic
at
io
n
th
an
op
en
Fo
rm
ve
ry
be
gi
nn
in
g
op
en
to
a
w
id
e
co
m
m
un
it
y
Fo
rm
ve
ry
be
gi
nn
in
g
op
en
to
a
w
id
e
co
m
m
un
it
y
R
ea
da
bi
lit
y
in
st
yl
e/
R
ed
un
da
nc
y:
1.
In
th
e
be
st
ca
se
A
bi
li
ty
to
fr
am
e
a
fin
is
he
d
re
se
ar
ch
re
su
lt
,
in
ho
m
og
en
ou
s
w
ri
ti
ng
st
yl
e
by
on
e
(o
r
a
fe
w
)
au
th
or
(s
)
D
ir
ec
t
re
po
rt
in
g
of
re
se
ar
ch
re
su
lt
s
w
it
h
re
ad
ab
il
it
y
of
(g
oo
d)
sc
ie
nc
e
jo
ur
na
li
sm
.
C
ap
ac
it
y
fo
r
co
nt
in
uo
us
re
fin
em
en
t
an
d
co
rr
ec
ti
on
of
te
xt
;
au
th
or
s
al
lo
w
ed
to
re
m
ix
/t
ra
ns
la
te
fr
om
pr
ev
io
us
ly
ex
is
ti
ng
ar
ti
cl
es
C
ap
ac
it
y
fo
r
co
nt
in
uo
us
re
fin
em
en
t
an
d
co
rr
ec
ti
on
of
te
xt
R
ea
da
bi
lit
y
in
st
yl
e/
R
ed
un
da
nc
y:
2.
C
ha
lle
ng
es
an
d
pr
ob
le
m
s
E
xt
er
na
l
pr
es
su
re
s
to
‘p
ub
li
sh
or
pe
ri
sh
’
an
d
th
er
ef
or
e
di
lu
te
d
an
d
di
sp
er
se
d
re
su
lt
s
O
ft
en
‘‘s
er
ia
li
ze
d’
’
re
po
rt
in
g
of
on
go
in
g,
no
t
ye
t
fin
is
he
d
re
se
ar
ch
.
A
s
su
ch
m
os
tl
y
ad
dr
es
si
ng
pe
er
s
in
sa
m
e
re
se
ar
ch
to
pi
c
ar
ea
(e
.g
.
by
no
t
m
ak
in
g
cl
ea
r
re
se
ar
ch
ai
m
s,
pr
es
um
pt
io
ns
,m
et
ho
ds
us
ed
,
et
c.
in
ea
ch
ne
w
bl
og
po
st
)
H
ar
d
to
m
ai
nt
ai
n
co
ns
is
te
nt
en
cy
cl
op
ae
di
a-
le
ve
l
co
nt
en
t
an
d
re
ad
ab
il
it
y
w
it
h
la
rg
e
au
th
or
co
m
m
un
it
y.
In
ad
di
ti
on
,
th
is
of
te
n
le
ad
s
to
w
ik
i-
bu
re
au
cr
ac
y,
e.
g.
m
et
a-
le
ve
l
of
te
m
pl
at
es
,
ru
le
s,
ac
ce
pt
an
ce
of
pr
op
os
ed
ch
an
ge
s,
et
c.
A
t
pr
es
en
t,
fe
w
ex
pe
ri
en
ce
s
w
it
h
ar
ti
cl
e-
st
yl
e
co
ll
ab
or
at
iv
e
kn
ow
le
dg
e
pr
od
uc
ti
on
in
co
de
re
po
si
to
ri
es
;
by
no
w
m
os
tl
y
us
ed
fo
r
pr
od
uc
ti
on
/
co
ll
ec
ti
on
/i
nt
eg
ra
ti
on
of
co
de
an
d
da
ta
C
om
pa
re
d
e.
g.
to
w
ik
i/
so
ci
al
co
di
ng
w
ri
ti
ng
st
yl
e:
un
us
ua
l
to
‘‘r
em
ix
’’
pr
ev
io
us
ly
ex
is
ti
ng
te
xt
an
d/
or
m
ak
e
la
te
r
co
rr
ec
ti
on
s
in
st
yl
e
A
cc
ep
ta
nc
e
w
it
hi
n
sc
ie
nt
ifi
c
co
m
m
un
it
y
T
og
et
he
r
w
it
h
bo
ok
s
in
m
os
t
di
sc
ip
li
ne
s
th
e
es
se
nt
ia
l
pa
rt
of
sc
ie
nt
ifi
c
pu
bl
is
hi
ng
;
of
fic
ia
ll
y
cr
ed
it
ed
So
m
ew
ha
t
ac
ce
pt
ed
,
bu
t
m
os
t
of
te
n
no
t
cr
ed
it
ed
W
id
el
y
us
ed
,
bu
t
co
nt
ri
bu
ti
on
no
t
cr
ed
it
ed
U
se
d,
e.
g.
fo
r
pr
od
uc
ti
on
an
d
m
ai
nt
en
an
ce
of
so
ft
w
ar
e
co
de
,
in
so
m
e
ca
se
s
da
ta
co
ll
ec
ti
on
s/
do
cu
m
en
ta
ti
on
;
M
os
t
of
te
n
no
t
cr
ed
it
ed
206 L. Heller et al.
scientific cultures, it is not expected that something should be published before the
peer-review is concluded. The production process is usually only open to the work
group and authors. This is being changed through the use of Preprint-Repositories
[like arXive.org (since 1991)], or the opening of the peer-review process in some
journals (cf. Pöschl 2012). After peer-review critical comments, extensions, hints,
and reviews have to go to other publications.
Blogs
Blogs are fundamentally different: Only the author decides on the medium, the
structure of publication, and the point in time of the release. Usually blogs are
produced by single persons and even in the case of blogging platforms the
responsibility for the content of one blog usually rests with the author of that explicit
blog. This results in certain expectations with respect to the content of the blog. In
typical scientific blogs, scientists publish reviews and review-like aggregations of
already published scientific publications. Blogs can often be seen as a method of
science journalism. On the other hand, some scientists blog about their ongoing
research, e.g. about the progress of a doctoral thesis (cf. Efimova 2009). In some
cases a blog is a public form of the scientific knowledge creation process—the blog
becomes a diary of the personal knowledge creation process, with novel ideas and
results being constantly published. Even preliminary results and unproven
hypotheses can be presented to a limited audience (limited in terms of real readers—
most blogs are indeed completely open) which, on the other hand, provides useful
feedback in the form of comments or other feedback. In this context, a blog post is
usually based on earlier blog posts and it is not considered to be a ‘full publication’
with sufficient background information. A high level of very specialized knowledge
is assumed (also see the discussion round including Mike Taylor; Taylor 2012). In
conclusion, it could be said that blog postings are no replacement for, but rather a
useful adjunct to old-fashioned, peer-reviewed journal or book publications. Blog
postings seem to already be on their trajectory to become a valuable part of the
publication mix. (Cf. to the development around the project ScienceSeeker.7)
Wikis
While blog postings represent conclusive texts of individual authors, wikis are much
more different from traditional ways of publishing. Wikis were initially introduced
to document practices for developing software,8 however, they became commonly
7 ScienceSeeker: http://scienceseeker.org/
8 Ward Cunningham: http://de.wikipedia.org/wiki/Ward_Cunningham; Wiki Wiki Web: http://
c2.com/cgi/wiki?WikiWikiWeb
Dynamic Publication Formats and Collaborative Authoring 207
known through the open and freely available online encyclopedia Wikipedia
(wikipedia.org, since 2001). Wikis represent websites with content that can be
collaboratively changed by potentially very large groups of users. Despite the fact
that the usage of wikis grew far beyond the remit of software development and
encyclopedias, Wikipedia significantly influenced the wide reception of wikis.
Wikis are hosted as open platforms in the public web. In most cases, a regis-
tration for the wiki is easily possible and new wiki topics can be proposed from
anyone, while the triage of new articles or concurrent changes and their accep-
tation to the public version of wiki is done by the project founders or a special user
group. Usually, these are authors who have become especially trustworthy through
their engagement with, or past contributions to the wiki. Usually the founding
group provides style and content guidelines. Strict adherence to these guidelines is
especially relevant since the content of articles is more or less implicitly approved
of by all authors of the wiki community. To resolve disputes is particularly
challenging since only one version of the articles exists. If disputes are unsolvable
usually all viewpoints are elucidated and the reason for the dispute is mentioned.
The fact that many people can contribute to articles possesses the advantage
that virtually anybody can become a co-author. Errors and biased viewpoints can
be corrected much quicker than in traditional ways of publishing. This is under the
assumption that all contributors adhere to the consensus of the wiki—potentially at
the expense of personal writing styles and viewpoints.
The individual author has to accept that statements might be edited or modified
by the community. Similarly, he has to accept its principles of meritocracy and the
potential consequences that might arise from such principles.
On the other side, contributions to a continuously evolving wiki-project present
the possibility of contributing to a work with an ongoing relevance. Even far into the
future, relevant contributions by an individual author can be traced in detail in the
revision history of the wiki text. This might also be used to scientometrically quantify
the quality or relevance of contributions to wikis—for example current approaches
include algorithmic modeling of the revision history (Kramer et al. 2008).
In contrast to scientific blogging and some dedicated wiki-projects, Wikipedia
itself was never considered to be a platform to exchange originary research results.
Wikipedia keeps an encyclopedic character, however, despite this, there are
multiple examples of scientists who actively contribute to Wikipedia. Ever more
so, some academic organizations propose a lively contribution to Wikipedia
(Farzan & Kraut 2012).
A cointegration of a scientific journal and Wikipedia was started with Topic
Pages by PLoS Computational Biology (Wodak et al. 2012). Topic Pages are a
version of a page to be posted to (the English version of) Wikipedia. In other
words, PLoS Computational Biology publishes a version that is static, includes
author attributions, and is indexed in PubMed, all with the reviews and reviewer
identities of Topic Pages available to the readership. The aim of this project is that
the Wikipedia pages subsequently become living documents which will be updated
and enhanced by the Wikipedia community.
208 L. Heller et al.
Stack Exchange—Message Boards Where Threads
are Initiated by Posting Open Questions
Question centered message boards (‘‘stack exchange’’) like MathOverflow and
BioStar (Parnell et al. 2011) consists of comment threads that are posted under a
known ID. A thread is centered on a question, which is in contrast to blogs which
provide more or less opinions, reviews, comments, overviews, or novel hypotheses.
A reputation (‘Karma’) can be built by earning ‘likes’ or ‘views’ from other users
within the community (initially introduced by Slashdot in the 1990s). The ques-
tioner and the community (Paul et al. 2012) assesses as to whether the answers are
sufficient and whether the thread should be closed, maximizing the potential gain in
Karma. The incentives set by this leads to many useful and comprehensible answers
at the end of a good browseable question thread. Orienting threads around questions
leads to a question-centered discussion and the discussions in turn stay on topic.
SNS for Scientists
Social networking systems (SNS) have found widespread use in the Internet. Early
examples are Friendster, Sixdegrees.com, Myspace, while Facebook made the
concept widely available. In 2012 Facebook hit the first billion of users worldwide.
After signing into a SNS, users can set up a profile page with a multitude of
information about themselves. Besides adopting the information, posting so-called
‘status updates’ has become very popular, ranging from personal feelings to more
or less useful information on one’s current task or whereabouts, often together with
some rich media such as pictures or movies.
Other users can follow the status update of particular members. Members that
are ‘followed’ contribute to their own personal ‘timeline’—a collection of all
status updates of fellow members. Other users can be added to the list of friends.
Friending another user results in a mutual exchange of status updates, media,
pictures, and many more things.
Usually, all friendship and follower connections are visible to a wider audience.
This fact, together with many other aspects of proprietary SNS, resulted in privacy
concerns which the provider tried to counteract by establishing selective privacy
settings. Other SNS such as Twitter incorporated a full and mandatory openness
into their strategy. With the advancement of SNS users are becoming more and
more aware of the chances and dangers of SNS’s.
Most SNS’s create a rich social experience with a mixture of status updates
from friends, personalized news, and the possibility of interacting with the post-
ings of others by ‘liking’ or commenting on them. If the user decides that certain
postings might be of interest to their friends or followers, they can share it with
them—often with the click of a mouse button.
Dynamic Publication Formats and Collaborative Authoring 209
Dedicated SNS’s for scientists were established around 2008 (see chapter
Academia Goes Facebook? The Potential of Social Network Sites in the Scholarly
Realm). Scientists started to share status updates related to their scientific work, in
many cases information about published articles, abstracts, and talks. They provide
a platform for users to post their research articles in compliance with copyright
laws, since a profile page is considered to be a personal homepage (Green road to
Open Access [see chapter Open Access: A State of the Art]). More and more often,
scientists have started to take the opportunity to incorporate media as well as some
interesting, novel findings into their status updates. The providers use information
and connections between users to support scientists, using suggestions about
interesting topics, other publications, and fellow researchers that work in similar
fields. Mendeley.com, Academia.edu, and ResearchGate.net have reached several
millions of users.
Most users of SNS’s for scientists maintain a profile in a non-scientific SNS. At
the dawn of the SNS, it seemed like there was only need for one SNS that could
serve all personal, professional, and scientific (and many more) networking needs.
However, this impression was wrong. It seems to be more suitable for users to
maintain several profiles in several SNS’s, whereof the facets of the personal
profile as well as the shared information depends on the purpose of the SNS
(cf. Nentwich and König 2012). Users do not want to mix holiday pictures with
their scientific publications.
In the past, SNS’s were not considered to be a means of sharing original
scientific results. However, this may undergo profound changes. For example, in
2011, FigShare (a commercial service) was introduced, serving as a free repository
for the archiving and presentation of scientific results. Researchgate as well as
Mendeley allow the publication of preprints; Mendeley allows the finding of
dedicated reviewers for certain publications.
SNS’s carry the potential of becoming a means of publishing scientific results
in accordance with the ongoing decoupling of scientific communication channels
from the journal system (e.g. Priem and Hemminger 2012).
The status updates in SNS’s for scientists could be used to publish ideas,
exciting findings, as well as links to other interesting sources. Commenting, as
well as the ‘like’ functionality could act as a kind of peer-review. In SNS’s for
scientists, status updates, comments, and likes are not anonymously done—
therefore their creator together with their profile are visible to other scientists who
can then assess the ‘credibility’ and the background of the contributing scientists.
The ‘question’ functionality of ResearchGate provides a platform with vivid
scientific discussions that were not possible in such a manner, were the users to
hide behind anonymous acronyms.
Furthermore, SNS’s can analyze the activity of scientists and provide easily
accessible, novel ‘impact’ metrics of scientists—based on their activity and rep-
utation within a SNS or based on established metrics.
A SNS for scientists combined with a text editing and publishing platform
might be the ideal platform to realize a dynamic publication system.
210 L. Heller et al.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Dynamic Publication Formats and Collaborative Authoring 211
Open Research Data: From Vision
to Practice
Heinz Pampel and Sünje Dallmeier-Tiessen
To make progress in science, we need to be open and share.
—Neelie Kroes (2012)
Abstract ‘‘To make progress in science, we need to be open and share.’’ This
quote from Neelie Kroes (2012), vice president of the European Commission
describes the growing public demand for an Open Science. Part of Open Science
is, next to Open Access to peer-reviewed publications, the Open Access to
research data, the basis of scholarly knowledge. The opportunities and challenges
of Data Sharing are discussed widely in the scholarly sector. The cultures of Data
Sharing differ within the scholarly disciplines. Well advanced are for example
disciplines like biomedicine and earth sciences. Today, more and more funding
agencies require a proper Research Data Management and the possibility of data
re-use. Many researchers often see the potential of Data Sharing, but they act
cautiously. This situation shows a clear ambivalence between the demand for Data
Sharing and the current practice of Data Sharing. Starting from a baseline study on
current discussions, practices and developments the article describe the challenges
of Open Research Data. The authors briefly discuss the barriers and drivers to Data
Sharing. Furthermore, the article analyses strategies and approaches to promote
and implement Data Sharing. This comprises an analysis of the current landscape
of data repositories, enhanced publications and data papers. In this context
the authors also shed light on incentive mechanisms, data citation practises and the
interaction between data repositories and journals. In the conclusions the authors
outline requirements of a future Data Sharing culture.
H. Pampel (&)
GFZ German Research Centre for Geosciences, Potsdam, Germany
e-mail: pampel@gfzpotsdam.de
S. Dallmeier-Tiessen
Scientific Information Service, CERN, Geneva, Switzerland
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_14,  The Author(s) 2014
213
The Vision of Open Research Data
Digitization has opened up new possibilities for scientists in their handling of
information and knowledge. The potential of networked research was recorded in
the ‘‘Berlin Declaration on Open Access to Knowledge in the Sciences and
Humanities’’ (2003). This declaration was signed by leading scientific organiza-
tions and is regarded as the central reference for the demands of access and sharing
of scientific results in the digital age. Previous definitions of Open Access were
related to free access to peer-reviewed literature,1 whereas the ‘‘Berlin Declara-
tion’’ considers this in a wider sense. Not only articles, but also ‘‘raw data and
metadata, source materials, digital representations of pictorial and graphical
materials and scholarly multimedia material’’ should be openly accessible and
usable.
This demand is also evident on the political level. An example is the statement
made in a publication of the Organisation for Economic Co-operation and
Development (OECD) 2007, entitled ‘‘Principles and Guidelines for Access to
Research Data from Public Funding’’: ‘‘Sharing and Open Access to publicly
funded research data not only helps to maximise the research potential of new
digital technologies and networks, but provides greater returns from the public
investment in research’’ (OECD 2007). The European Commission also strives for
Open Access to research data. In the ‘‘Commission Recommendation on Access to
and Preservation of Scientific Information’’ which was published in 2012, Euro-
pean member states are requested to ensure that ‘‘research data that result from
publicly funded research become publicly accessible, usable and re-usable through
digital e-infrastructures’’ (European Commission 2012a).
The discussion on the realisation of this aim is present in the scientific com-
munities (Nature 2002, 2005, 2009a, b; Science 2011). The term Open Research
Data can be applied on a cross-disciplinary layer. It covers the heterogeneity of the
data with its diverse characteristics, forms and formats in the scientific disciplines.
Further to this, the term Open Research Data is distinct to Open Data, which is
mainly used in the context of Open Government initiatives and neglects the special
requirements of science.
The two central arguments for Open Access to research data are a) the possi-
bility to re-use data in a new connection and b) the verifiability it guarantees for
ensuring good scientific practice. The OECD (2007) added a further argument:
‘‘Sharing and Open Access to publicly funded research data not only helps to
maximise the research potential of new digital technologies and networks, but
provides greater returns from the public investment in research.’’
The vision of the High Level Expert Group on Scientific Data for the year 2030
is that, scientists in their role as data user, ‘‘are able to find, access and process the
1 Compare: Budapest Open Access Initiative, 2002: http://www.opensocietyfoundations.org/
openaccess/read & Bethesda Statement on Open Access Publishing, 2003: http://www.
earlham.edu/*peters/fos/bethesda.htm
214 H. Pampel and S. Dallmeier-Tiessen
data they need’’, and in their role as data producer ‘‘prefer to deposit their data
with confidence in reliable repositories’’ (High Level Expert Group on Scientific
Data 2010).
The demand for Open Research Data effects individual researchers and their
data handling. In a report of The Royal Society (2012) entitled ‘‘Science as an
open enterprise’’ which is worth reading, the recommendation is given that:
‘‘[s]cientists should communicate the data they collect and the models they create,
to allow free and Open Access, and in ways that are intelligible, assessable and
usable for other specialists in the same or linked fields wherever they are in the
world. Where data justify it, scientists should make them available in an appro-
priate data repository. Where possible, communication with a wider public audi-
ence should be made a priority, and particularly so in areas where openness is in
the public interest.’’ This recommendation makes it clear that diverse basic con-
ditions must be created before Data Sharing can become a standard in scientific
practice. Access and usage conditions must be defined. Murray-Rust et al.2 for
example demands the free accessibility in the public domain in their ‘‘Panton
Principles’’: ‘‘By open data in science we mean that it is freely available on the
public Internet permitting any user to download, copy, analyse, re-process, pass
them to software or use them for any other purpose without financial, legal, or
technical barriers other than those inseparable from gaining access to the Internet
itself.’’ The majority of the disciplines are still far away from the implementation
of these ‘‘[p]rinciples for open data in science’’, however. In addition, there are
many cases in the life sciences and social science disciplines in which, because of
data protection and personal rights, Data Sharing is not possible, or only possible
under narrowly defined conditions.
The Status of Data Sharing Today
In a consultation carried out in 2012, the European Commission determined that
there were massive access barriers to research data. Of the 1,140 of those ques-
tioned, 87 % contradicted the statement that ‘‘there is no access problem to
research data in Europe’’ (European Commission 2012b). In a revealing study
made by Tenopir et al. (2011), 67 % of the more than 1,300 researchers pointed to
a ‘‘lack of access to data generated by other researchers or institutions’’ that is a
hindrance to advances in science. Scientists frequently see the potential offered by
Open Research Data, but most are reticent with regard to the open accessibility of
their own data. Tenopir et al. found, for example, that ‘‘only about a third (36 %)
of the respondents agreed that others can access their data easily’’. This is not in
accordance with the researchers attitudes, as three-quarters state that they ‘‘share
their data with others’’. The study also sheds light on different disciplinary
2 Panton Principles: http://pantonprinciples.org/
Open Research Data: From Vision to Practice 215
practices: Whereas 90 % of scientists working in atmospheric science were willing
to sharing their data, only 58 % of the questioned social sciences scientists were
ready to do this. The authors conclude: ‘‘there is a willingness to share data, but it
is difficult to achieve or is done only on request.’’
Further insights in disciplinary practices are presented by the studies Wicherts
et al. (2006) in psychology and Savage and Vickers (2009) in medicine, for
example. Wicherts et al. approached authors of 141 articles published in 2004 in
journals of the American Psychological Association (APA) and requested access to
data that was the basis of the articles. Within the next six months, they only
received positive replies from one third of the authors, 73 % of the authors were
not prepared to share their data. Savage & Vickers came to a similar result. They
asked authors of ten articles that were published in the PLoS Medicine or PLoS
Clinical Trials journals to allow them access to the underlying data of the articles.
Despite the clear demands for Open Access to data in den editorial policies of each
of the Open Access journals, only one author permitted access to the requested
data. A further insight in the status of Data Sharing is presented by the analysis of
Campbell et al. (2002) in genetics. In this study that involved about 1,800 life
science scientists, they identified two central factors that hinder Data Sharing:
‘‘Lack of resources and issues of scientific priority play an important role in
scientists’ decisions to withhold data, materials, and information from other aca-
demic geneticists.’’
Alongside these very reserved attitudes, however, there are numerous examples
which underline that open exchange of research data can successfully be realized.
The ‘‘Bermuda Principles’’ that were adopted in human genetics in the framework
of the Human Genome Project in 1996, for example. These principles require that
‘‘[a]ll human genomic sequence data generated by centers funded for large-scale
human sequencing should be freely available and in the public domain to
encourage research and development and to maximize the benefit to society’’
(Smith and Carrano 1996). Over time the pre-publication of data comes off as
common practice, i.e. gene sequencies are made openly accessible prior to the
description of them in a peer reviewed article.3 Alongside Data Sharing in large
scientific projects, in which data is made openly available in trustworthy research
data repositories, there are also examples of spontaneous Data Sharing. Research
on a disease-causing strain of the Escherichia coli (O104:H4) bacteria is such a
case. This caused more than 4,0004 people to fall ill in Germany in 2011. The
publication of sequence data under a Creative Commons licence and the use of
the widely popular GitHub5 as an exchange platform enabled scientists all over the
3 See also the Fort Lauderdale Principles (Wellcome Trust 2003) and the Toronto Statement
(Birney et al. 2009).
4 See: http://www.rki.de/DE/Content/Service/Presse/Pressemitteilungen/2011/11_2011.html
(Retrieved 20 August 2012).
5 GitHub is a hosting service for the collaborative development of software. See: https://
github.com/ehec-outbreak-crowdsourced (Retrieved 20 August 2012).
216 H. Pampel and S. Dallmeier-Tiessen
world to make a contribution to a rapid investigation of the bacterium
(Kupferschmidt 2011; Turner 2011; Check Hayden 2012).
A further example of successful Data Sharing is the operation of the World
Data System (WDS) of the International Council of Science (ICSU) which - even
before the coming into being of the Internet - resulted from the International
Geophysical Year (1957–1958). This network of disciplinary data centers ensures
‘‘full, open, timely, non-discriminatory and unrestricted access to metadata, data,
products and services’’.6
Understanding the Barriers
So-called data policies have an increasing effect on scientists and how they handle
research data.7 Recommendations and mandatory requirements by funding agen-
cies and scientific journals stand out here. They request the beneficiary of funds to
ensure the preservation and accessibility of data created in the framework of a
funded project or a publication. The National Institute of Health (NIH) was a
pioneer in this respect. It anchored its ‘‘Data Sharing Policy’’ in 2003: Applicants
for a grant upwards of 500,000 US dollar are requested to make statements on Data
Sharing.8 From 2011 on, the National Science Foundation (NSF) requires receivers
of funds ‘‘to share with other researchers, at no more than incremental cost and
within a reasonable time, the primary data, samples, physical collections and other
supporting materials created or gathered in the course of work under NSF grants’’
(National Science Foundation 2011a). Measures for the implementation of this
guideline must be specified in a ‘‘Data Management Plan’’ (National Science
Foundation 2011b). This request is being increasingly taken up by scientific
journals via editorial policies. Exemplary for these are the requirements of the
Nature journals, in which ‘‘authors are required to make materials, data and
associated protocols promptly available to readers without undue qualifications in
material transfer agreements’’. It is suggested that the data be made accessible ‘‘via
public repositories‘‘.9
It must be noted that implementation of the requirements formulated in the data
policies will not run by itself (Pampel and Bertelmann 2011). To promote Data
Sharing it is necessary to identify the barriers, which influence scientists with
regard to the sharing of their own data. Surveys carried out by Kuipers and Van der
Hoeven (2009) and Tenopir et al. (2011) allow the following barriers to be named:
6 ICSU World Data System: http://icsu-wds.org/images/files/WDS_Certification_
Summary_11_June_2012_pdf
7 For details see: Pampel & Bertelmann (2011).
8 National Institutes of Health: http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-
032.html.
9 Guide to Publication Policies of the Nature Journals: http://www.nature.com/authors/gta.pdf
Open Research Data: From Vision to Practice 217
‘‘legal issues’’, ‘‘misuse of data’’ and ‘‘incompatible data types’’ (Kuipers and Van
der Hoeven 2009), as well as ‘‘insufficient time’’ and ‘‘lack of funding’’ (Tenopir
et al. 2011). These barriers make it clear that a dedicated framework is required for
the publication of research data. The conception and implementation of such a
framework is being increasingly discussed under the Research Data Management
term.10 The aim is to develop organisational and technical measures to ensure a
trustworthy infrastructure for permanent integrity and re-use of data. The centre of
attention hereby is the operation of information infrastructures, such as research
data repositories, in which research data can be permanently stored. To make re-
use of the stored data possible, the Research Data Management framework must
ensure that the data are described via metadata. Documentation of the instruments
and methods used to obtain the data is necessary for reliable re-use of the data, for
example. Such an enhanced documentation of data is often a time-consuming task
that is competing with many other activities on the researchers priority list. Further
to this, in many disciplines there are no standards in which the data can be
described.
Recently, it can be observed that libraries, data centers and other institutions are
increasingly collaborate and begin to build up information infrastructures to
support scientists in the handling of their data and so also to promote Data Sharing
(Pampel et al. 2010; Osswald and Strathmann 2012; Reilly 2012).
Van der Graaf and Waaijers (2011) have formulated four central fields of action
for the realization of a ‘‘collaborative data infrastructure’’ which enables the ‘‘use,
re-use and exploit research data to the maximum benefit of science and society’’.
Incentives must be given to stimulate Data Sharing (1); in addition, the education
and training of scientists and service providers on and around the handling of data
must be intensified (2). Further to these the authors point to the importance on the
structuring and networking of research data infrastructures that serve for a per-
manent and reliable data storage (3) and point out the challenge of the long-term
financing of these infrastructures (4).
Overcoming the Barriers
A central barrier to of the pervasiveness of Data Sharing is the lack of incentives
for the individual scientist to make his data openly accessible. In particular in
projects, in which data management was not already discussed in the preparatory
phase, the individual scientist has good reasons for not making his or her data
openly accessible, as there are no incentive mechanisms for the sharing of research
data in the competitive scientific system (Borgman 2010; Klump 2012).
10 For details see: Büttner et al. (2011) and Pryor (2012).
218 H. Pampel and S. Dallmeier-Tiessen
The slogan ‘‘[c]redit where credit is overdue’’ (Nature Biotechnology 2009)
clearly expresses that: Data Sharing will only be successful when it is worthwhile
for a scientist to make his data openly accessible. Against this background, a
growing number of publication strategies are appearing with a view to the
implementation of Data Sharing on the basis of the established scientific reputation
system. Three of these strategies are as follows11:
1. The publication of research data as an independent information object in a
research data repository.
2. The publication of research data as a textual documentation in the form of a so-
called data paper.
3. The publication of research data as enrichment of an article, a so-called
‘‘enriched publication’’.
Whereas the first named practice has long been established in the life sciences
with the use of data repositories such as GenBank (Benson et al. 2012)12 , the
second named data paper strategy has been gaining more and more attention
recently. Chavan and Penev (2011) define this publication type as follows: ‘‘a
journal publication whose primary purpose is to describe data, rather than to report
a research investigation. As such, it contains facts about data, not hypotheses and
arguments in support of those hypotheses based on data, as found in a conventional
research article.’’ Experience with data papers has been made, among others, in the
geosciences13 and ecology.14 The use of this model has recently been widened to
include so-called data journals. The pioneer of this development is the Open
Access journal Earth System Science Data (ESSD). It has published descriptions
of geosciences data sets since 2008. The data sets themselves are published on a
‘‘reliable repository’’ (Pfeiffenberger and Carlson 2011). The data sets and
descriptive publications described are permanently persistently addressed by
means of a digital object identifier (DOI) which also facilitate data citation. Thanks
to this procedure that was developed within the Publication and Citation of Sci-
entific Primary Data (STD–DOI) project (Klump et al. 2006) and expanded by
DataCite (Brase and Farquhar 2011), it is possible to link publications and the
underlying data. This procedure also supports the visibility of the data. Some
publishing houses, for example, have therefore already integrated freely accessible
research data in their platforms (Reilly et al. 2011). A number of data journals
11 The following categorization is based on Dallmeier-Tiessen (2011).
12 For the GenBank history see: Cravedi (2008).
13 The AGU Journals have published data papers for many years. See: http://www.agu.org/pubs/
authors/policies/data_policy.shtml (Retrieved 20 August 2012).
14 See the Data Papers of the Journals Ecological Archives of the Ecological Society of America
(ESA): http://esapubs.org/archive/archive_D.htm (Retrieved 20 August 2012).
Open Research Data: From Vision to Practice 219
have been brought into being in the meantime.15 It must be noted here that the
establishment of data journals is only feasible when data, metadata and the cor-
responding text publication are freely accessible, as only then can a barrier free re-
use of the data be possible.
The linking of articles and data is also addressed in the third named enriched
publication strategy (Woutersen-Windhouwer et al. 2009). The aim is to build and
sustain a technical environment to relate all relevant information objects around an
article so that a knowledge space is created, in which the research data that are the
basis of the article can be made freely accessible.16
The implementation of the three strategies requires trustworthy repositories on
which the data can be made permanently accessible. A differentiation must be
made here between institutional, disciplinary, multi-disciplinary and project-spe-
cific infrastructure (Pampel et al. 2012). Prominent examples of disciplinary
research data repositories are GenBank in genetics and PANGAEA in geosciences
and Dryad in biodiversity research.17 A look at the access conditions of reposi-
tories highlights some differences: GenBank states that there are ‘‘no restrictions
on the use or distribution of the GenBank data, PANGAEA licences the data under
the ‘‘Creative Commons Licence Attribution’’ and Dryad makes the data acces-
sible under the ‘‘Creative Commons License CC0’’ in the public domain.
A number of studies have been published that show the impact of Data Sharing
on citation rates. Articles for which the underlying data is shared are more fre-
quently cited than articles for which this is not the case. This is substantiated in
studies from genetics (Piwowar et al. 2007; Botstein 2010), astronomy (Henneken
and Accomazzi 2011; Dorch 2012) and paleoceanography Sears (2011). Such
results need to be considered when discussing the lack of incentives for Data
Sharing. The same holds true for data citation and data papers, which could
contribute to the researchers publication profile and thus current research assess-
ments and incentive systems.
15 Examples: Atomic Data and Nuclear Data Tables (Elsevier); Biodiversity Data Journal
(Pensoft Publishers); Dataset Papers in Biology (Hindawi Publishing Corporation); Dataset
Papers in Chemistry (Hindawi Publishing Corporation); Dataset Papers in Ecology (Hindawi
Publishing Corporation); Dataset Papers in Geosciences (Hindawi Publishing Corporation);
Dataset Papers in Materials Science (Hindawi Publishing Corporation); Dataset Papers in
Medicine (Hindawi Publishing Corporation); Dataset Papers in Nanotechnology (Hindawi
Publishing Corporation); Dataset Papers in Neuroscience (Hindawi Publishing Corporation);
Dataset Papers in Pharmacology (Hindawi Publishing Corporation); Dataset Papers in Physics
(Hindawi Publishing Corporation); Earth System Science Data—ESSD (Copernicus Publica-
tions); Geoscience Data Journal (Wiley); GigaScience (BioMed Central); Nuclear Data Sheets
(Elsevier); Open Archaeology Data (Ubiquity Press); Open Network Biology (BioMed Central).
Please note that the majority of the journals are still developing and a narrow definition of the
type of publication is difficult because of this early development stage.
16 Potential offered by this strategy under use of Linked Open Data.
17 An overview of existing data repositories is offered by re3data.org (http://re3data.org).
220 H. Pampel and S. Dallmeier-Tiessen
Translating Vision into Practice
The developments in recent years have shown that numerous initiatives have
emerged in Data Sharing. The hesitation among researchers in many disciplines is
met by new strategies that work on barriers such as the lack of incentives.
A professionalization of the Research Data Management, which supports scientists
in the sharing of their data, is necessary to ensure the permanent accessibility,
however. In this context, priority must be given to the structuring and networking
of the research data repositories and their long-term financing.
A more detailed analysis for the identification and overcoming of barriers to
Data Sharing has been created in the framework of the EU-project Opportunities
for Data Exchange (ODE).18 This project takes the various players involved in
scholarly communication and data management (policy-makers, funders,
researchers, research and education organisations, data centres and infrastructure
service providers and publishers) into consideration, names variables that have an
effect on the sharing and points out strategies for overcoming barriers to Open
Access (Dallmeier-Tiessen et al. 2012). Many of the strategies that are outlined
show that, to counter the diverse challenges, close cooperation is necessary
between the players named above. As an example, the successful implementation
of data policies of supporting organizations requires a Research Data Management
and infrastructures that support scientists and create a regulatory framework. All of
these measures will only lead to success, however, when scholarly societies and
other disciplinary players who support the anchoring in the disciplinary commu-
nities take part. All players in the scientific process are therefore requested to make
their contribution to Open Access of research data.
The publication strategies outlined show that there really is a possibility for the
anchoring of Data Sharing in the scientific reputation system. Further innovation is
desirable, though. The implementation of the increasing demand for Open Science
from society19 and academic policy (Kroes 2012), as is assumed, for example, by
the federation of national academies ALLEA - ALL European Academies (2012),
needs a culture of sharing. The establishment of this culture is a far reaching
challenge. It appears that implementation of it can only then be successful when
changes are made in the scientific reputation system. Scientific performances
should in the future be valued with a ‘‘sharing factor’’ that not only judges the
citation frequency in the scientific community, but also rates the implementation of
sharing of information and knowledge for the good of society.
The demand for openness in science is loud and clear. All players in the
scientific area should direct their practices to this demand. The publication strat-
egies for research data have up to now been important approaches towards Open
Science. The following citation from the ‘‘Berlin Declaration’’ (2003) makes it
18 See: http://ode-project.eu
19 See here, for example, the Vision of the Open Knowledge Foundation (OKF): http://okfn.org/
about/vision/ (Retrieved 20 August 2012).
Open Research Data: From Vision to Practice 221
clear, that further steps are necessary for the realization of Open Science: ‘‘Our
mission of disseminating knowledge is only half complete if the information is not
made widely and readily available to society.’’
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
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224 H. Pampel and S. Dallmeier-Tiessen
Intellectual Property and Computational
Science
Victoria Stodden
Abstract This chapter outlines some of the principal ways United States Intel-
lectual Property Law affects the sharing of digital scholarly objects, particularly
for those who wish to practice reproducible computational science or Open
Science. The sharing of the research manuscript, and the data and code that are
associated with the manuscript, can be subject to copyright and software is also
potentially subject to patenting. Both of these aspects of Intellectual Property must
be confronted by researchers for each of the these digital scholarly objects: the
research article; the data; and the code. Recommendations are made to maximize
the downstream reuse utility of each of these objects. Finally, this chapter proposes
new structures to manage Intellectual Property to accelerate scientific discovery.
Introduction
A deep digitization of the scientific enterprise is taking place across the research
landscape and generating new ways of understanding our surroundings. As a
result, our stock of scientific knowledge is now accumulating in digital form. Our
DNA is encoded as genome sequence data, scans of brain activity exist in func-
tional magnetic resonance image databases, and records of our climate are stored
in myriad time series datasets—to name but a few examples. Equally as impor-
tantly, our reasoning about these data is recorded in software, in the scripts and
code that analyze and make sense of our digitally recorded world. Sharing the code
and data that underlie scientific findings is a necessary step to permit the transfer of
knowledge embodied in the results, so that they can be independently verified,
re-used, re-purposed, understood, and applied in new areas to solve new problems.
V. Stodden (&)
Columbia University, Manhattan, USA
e-mail: victoria@stodden.net
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_15,  The Author(s) 2014
225
The inability to access scientific data and code stands as a barrier to the verification
of scientific findings, and as a barrier to the knowledge transfer needed to both
facilitate scientific advancement and spurn innovation and entrepreneurship
around scientific findings (Stodden 2011).
These computational advances have taken place in parallel with the develop-
ment of the Internet as a pervasive digital communication mechanism, creating an
unprecedented opportunity to broaden access to scientific understanding. In this
chapter I describe Intellectual Property barriers to the open sharing of scientific
knowledge, and motivate solutions that coincide with longstanding scientific
norms. In ‘‘Research Dissemination: The Narrative’’, I frame scientific commu-
nication as a narrative with a twofold purpose: to communicate the importance of
the results within the larger scientific context and to provide sufficient information
such that the findings may be verified by others in the field. With the advent of
digitization, replication typically means supplying the data, software, and scripts,
including all parameter settings and other relevant metadata, that produced
the results (King 1995; Donoho et al. 2009). Included in this discussion is the
importance of access to the primary research narrative, the publication of the
results. ‘‘Research Dissemination: Data and Raw Facts’’ and ‘‘Research Dissem-
ination: Methods/Code/Tools’’ then discuss Intellectual Property barriers and
solutions that enable data and code sharing respectively. Each of these three
research outputs, the research article, the data, and the code, require different legal
analyses in the scientific context.
Research Dissemination: The Narrative
A typical empirical scientific workflow goes something like this: a research
experiment is designed to answer a question; data are collected, filtered, and
readied for analysis; models are fit, hypotheses tested, and results interpreted;
findings are written up in a manuscript which is submitted for publication.
Although highly simplified, this vignette illustrates the integral nature of narrative,
data, and code in modern scientific research. What it does not show is the limited
nature of the research paper in communicating the many details of a computational
experiment and the need for data and code disclosure. This is the subject of the
sections ‘‘Research Dissemination: Data and Raw Facts’’ and ‘‘Research Dis-
semination: Methods/Code/Tools.’’ This section motivates the sharing of the
research paper, and discusses the conflict that has arisen between the need for
scientific diss00emination and modern intellectual property law in the United
States.
A widely accepted scientific norm, labeled by Robert K. Merton, is Communism
or Communalism (Merton 1973). With this Merton described an ideal in scientific
research, that property rights extend only to the naming of scientific discoveries
(Arrow’s Impossibility Theorem for example, named for its originator Kenneth
Arrow), and all other intellectual property rights are given up in exchange for
226 V. Stodden
recognition and esteem. This idea underpins the current system of publication and
citation that forms the basis for academic rewards and promotions. Results are
described in the research manuscript which is then published, typically in estab-
lished academic journals, and authors derive credit through their publications and
other contributions to the research community. They do not receive financial or
other material rewards beyond recognition by peers of the value of their contri-
butions. There are many reasons for the relinquishment of property rights over
discoveries in science, but two stand out. It is of primary importance to the
integrity of our body of scientific knowledge that what is recognized as scientific
knowledge has as little error as possible. Access not just to new discoveries, but
also to the methods and derivations of candidates for new knowledge, is imper-
ative for verification of these results and for determining their potential admission
as a scientific fact. The recognition that the scientific research process is error
prone—error can creep in at any time and in any aspect of research, regardless of
who is doing the work—is central to the scientific method. Wide availability
increases the chances that errors are caught - ‘‘many eyes make all bugs shallow.’’
The second reason Intellectual Property rights have been eschewed in scientific
research is the historical understanding that scientific knowledge about our world,
such as physical laws, mathematical theorems, or the nature of biological func-
tions, is not subject to property rights but something belonging to all of humanity.
The U.S. federal government granted more than $50 billion dollars for scientific
research last year in part because of the vision that fundamental knowledge about
our world isn’t subject to ownership but is a public good to be shared across all
members of society.1 This vision is also reflected both in the widespread under-
standing of scientific facts as ‘‘discoveries’’ and not ‘‘inventions,’’ denoting their
preexisting nature. Further, current intellectual property law does not recognize a
scientific discovery as rising to the level of individual ownership, unlike an
invention or other contribution. Here, we focus on the interaction of intellectual
property law and scientific research article dissemination.
Copyright law in the United States originates in the Constitution, when it states
that ‘‘The Congress shall have Power … To promote the Progress of Science and
useful Arts, by securing for limited Times to Authors and Inventors the exclusive
Right to their respective Writings and Discoveries’’.2 Through a series of laws and
interpretations since then, copyright has come to automatically assign a specific set
of rights to original expressions of ideas. In the context of scientific research, this
means that the written description of a finding is copyright to the author(s) whether
or not they wish it to be, and similarly for code and data (discussed in the fol-
lowing two sections). Copyright secures exclusive rights vested in the author to
both reproduce the work and prepare derivative works based upon the original.
There are exceptions and limitations to this power, such as Fair Use, but none of
these provides an intellectual property framework for scientific knowledge that is
1 The Science Insider: http://news.sciencemag.org/scienceinsider/budget_2012/
2 U.S. Const. art. I, §8, cl. 8.
Intellectual Property and Computational Science 227
concordant with current scientific practice and the scientific norms described
above. In fact far from it.
Intellectual property law, and how this law is interpreted by academic and
research institutions, means that scientific authors generally have copyright over
their research manuscripts. Copyright can be transferred, and in a system estab-
lished many decades ago journals that publish the research manuscripts typically
request that copyright be assigned to the publisher for free as a condition of
publication. With some notable exceptions, this is how academic publication
continues today. Access to the published articles requires asking permission of the
publisher who owns the copyright owner, and usually involves paying a fee.
Typically scientific journal articles are available only to the privileged few affil-
iated with a university library that pays subscription fees, and articles are other-
wise offered for a surcharge of about $30 each.
A transformation is underway that has the potential to make scientific knowl-
edge openly and freely available, to everyone. The debate over access to scientific
publications breaks roughly into two camps. On one side are those who believe
tax-payers should have access to the fruits of the research they’ve funded, and on
the other side are those who believe that journal publishing is a business like any
other, and the free market should therefore be left unfettered.3 The transformation
started in 1991 when Paul Ginsparg, Professor of Physics at Cornell University, set
up an open repository called arXiv.org (pronounced ‘‘archive’’) for physics articles
awaiting journal publication. In the biosciences, a new publishing model was
brought to life in 2000—Open Access publishing—through the establishment of
the Public Library of Science, PLoS.4 PLoS publishes scientific articles by
charging the authors the costs upfront, typically about $1300 per article, and
making the published papers available on the web for free.5 The PLoS model has
been extraordinarily successful, gaining in prestige and publishing more articles
today than any other scientific journal.6
The U.S. government has joined in this movement toward openness in scientific
literature. In 2009 the National Institutes for Health (NIH) began requiring all
published articles arising from research it funds to be placed in the publicly
accessible repository PubMed Central7 within 12 months of publication. In Jan-
uary of 2011, President Obama signed the America COMPETES Reauthorization
Act of 2010.8 This bill included two key sections that step toward the broad
implementation of Open Access mandates for scientific research. The Act both
3 Association of American Publishers Press Release: http://www.publishers.org/press/56/
4 See: http://blogs.plos.org/plos/2011/11/plos-open-access-collection-%E2%80%93-resources-
to-educate-and-advocate/ for a collection of articles on Open Access.
5 See http://www.plos.org/publish/pricing-policy/publication-fees/ for pricing information.
6 See http://scholarlykitchen.sspnet.org/2011/06/28/plos-ones-2010-impact-factor/ for recent
impact factor information.
7 PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/
8 America COMPETES Reauthorization Act of 2010: http://www.gpo.gov/fdsys/pkg/BILLS-
111hr5116enr/html/BILLS-111hr5116enr.htm
228 V. Stodden
required the establishment of an Interagency Public Access Committee to coor-
dinate dissemination of peer-reviewed scholarly publications from research sup-
ported by Federal science agencies, and it directed the Office of Science and
Technology Policy in the Whitehouse to develop policies facilitating online access
to unclassified Federal scientific collections. As a result, on November 3, 2011 the
Whitehouse announced two public requests for information on, ‘‘Public Access to
Peer-Reviewed Scholarly Publications Resulting From Federally Funded
Research’’ and ‘‘Public Access to Digital Data Resulting From Federally Funded
Scientific Research,’’ As this chapter goes to press, the Office of Science and
Technology Policy at the Whitehouse is gathering plans to enable Open Access to
publications and to data from federal funding agencies.9
These events indicate increasing support for the public availability of scientific
publications on both the part of regulators and the scientists who create the con-
tent.10 The paradoxical publishing situation of sustained high charges for content
generated (and subsidized) for the public good came about in part through the
scientific norm of transparency. As mentioned earlier, establishing a scientific fact
is difficult, error-prone work. The researcher must convince skeptics that he or she
has done everything possible to root out error, and as such expose their methods to
community scrutiny in order to flush out any possible mistakes. Scientific publi-
cation is not an exercise in informing others of new findings, it is an active dialog
designed to identify errors and maximize the integrity of the knowledge. Scientific
findings and their methodologies that are communicated as widely as possible have
the best chance of minimizing error.
Scientific knowledge could be spread more widely, more mistakes caught, and
the rate of scientific progress improved. Scientists should be able to share their
published articles freely, rather than remitting ownership to publishers. Many
journals have a second copyright agreement that permits the journals to publish the
article, but leaves copyright in the hands of the authors.11 We are in need of a
streamlined and uniform way of managing copyright over scientific publications,
and also copyright on data and code, as elaborated in the next section.
9 See http://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-
funded-research
10 Unsurprisingly, the journal publishers are not so supportive. Just before the 2011 winter
recess, House representatives Issa and Maloney introduced a bill that would do enormous harm to
the availability of scientific knowledge and to scientific progress itself. Although no longer being
considered by Congress (support was dropped the same day that publishing giant Reed-Elsevier
claimed it no longer supported the bill), the ‘‘Research Works Act’’ would have prohibited federal
agencies and the courts from using their regulatory powers to make scientific articles arising from
federally funded research publicly available.
11 See for example Science Magazine’s alternative license at http://www.sciencemag.org/site/
feature/contribinfo/prep/lic_info.pdf (last accessed January 29, 2013).
Intellectual Property and Computational Science 229
Research Dissemination: Data and ‘‘Raw Facts’’
Computational science today is facing a credibility crisis: without access to the
data and computer code that underlies scientific discoveries, published findings are
all but impossible to verify (Donoho et al. 2009). This chapter discusses how
Intellectual Property Law applies to data in the context of communicating scien-
tific research. Drawing on our vignette introduced in the beginning of ‘‘Research
Dissemination: The Narrative,’’ data is understood as integral in the communi-
cation of scientific findings. Data can refer to an input into scientific analysis, such
as a publicly available dataset like those at Data.gov12 or one gathered by
researchers in the course of the research, or it can refer to the output of compu-
tational research. In short, it is typically inference, and array of numbers or
descriptions, to which analysis interpretation is applied.
In 2004, Gentleman and Temple Lang (Gentleman and Temple Lang 2004).
introduced the concept of the compendium: a novel way of disseminating research
results that expands the notion of the scientific publication to include the data and
software tools required to reproduce the findings. At core, the research compen-
dium envisions computational results not in isolation, but as components in a
description of a meaningful scientific discovery.
Reproducible computational science has attracted attention since Stanford
Professor Jon Claerbout wrote some of the first really reproducible manuscripts in
1992.13 Since then a number of researchers have adopted reproducible methods
(Donoho and Buckheit 1995; Donoho et al. 2007; Stodden et al. 2012) or intro-
duced them in their role as journal editors14 (Trivers 2012). Mature responses to
the ubiquity of error in research have evolved for both branches of the scientific
method: the deductive branch relies on formal logic and mathematical proof while
the empirical branch has standards of statistical hypothesis testing and standard-
ized communication of reproducibility information in the methods section. Uni-
fying the scholarly record to include digital objects such as code and data with the
published article facilitates the new types of information flows necessary to
establish verifiability and reproducibility in computational science.
As we saw in the previous section, copyright attaches to the original expression
of ideas and not to the ideas themselves. In the case of data, U.S. copyright does
not attach to raw facts.15 In 1991 the U.S. held that raw facts are not copyrightable
although the original ‘‘selection and arrangement’’ of these raw facts may be.16
The Supreme Court has not ruled on Intellectual Property in data since and it
12 See https://explore.data.gov/
13 See http://sepwww.stanford.edu/doku.php?id=sep:research:reproducible
14 Journal of Experimental Linguistics: http://elanguage.net/journals/jel
15 Although copyright does attach to raw facts under European Intellectual Property Law. This is
a key distinction between European and U.S. Intellectual Property systems in the context of
scientific research.
16 Feist Publications v. Rural Telephone Service Co., 499 U.S. 360 (1991).
230 V. Stodden
seems plausible that in modern scientific research the original selection and
arrangement of facts may create a residual copyright in a particular dataset, if there
was ‘‘original selection and arrangement’’ of these raw facts. Collecting, cleaning,
and readying data for analysis is often a significant part of scientific research.
The Reproducible Research Standard (Stodden 2009a, b) recommends releas-
ing data under a Creative Commons public domain certification (CC0) in part
because of the possibility of such a residual copyright existing in the dataset.17
Public domain certification means that as the dataset author you will not exercise
any rights you may have in the dataset that drive from copyright (or any other
ownership rights). A public domain certification also means that as the author you
are relying on downstream users’ ethics, rather than legal devices, to cite and
attribute your work appropriately.
Datasets may, of course, have barriers to re-use and sharing that do not stem
from Intellectual Property Law, such as confidentiality of records, privacy con-
cerns, and proprietary interests from industry or other external collaborators that
may assert ownership over the data. Good practice suggests planning for data
release before beginning a research collaboration, whether it might be with
industrial partners who may foresee different uses for the data than really repro-
ducible research, or with scientists subject to a different Intellectual Property
framework for data, such as those in Europe (Stodden 2010, 2011).
Research Dissemination: Methods/Code/Tools
Computational results are often of a complexity that makes communicating the
steps taken to arrive at a finding prohibitive in a typical scientific publication,
giving a key reason for releasing the code that contains the steps and instructions
that generated the published findings. Of the three digital scholarly objects dis-
cussed in this chapter, code has the most complex interactions with Intellectual
Property Law as it is both subject to copyright and patent law.
Software is subject to copyright, as it is an original expression of an underlying
idea. The algorithm or method that the code implements is not subject to copy-
right, but copyright adheres to the actual sequence of letters and numbers that is
the code. Copyright prohibits others from reproducing or modifying the code—for
scientific applications this would prohibit running the code on a different system
(reproducing) or adapting the code to a new problem (re-using). These action are
openly encouraged in scientific research and again, scientific norms are at odds
with Intellectual Property Law. An open license that permits others to re-use
scientific code is essential.
17 Creative Commons was founded in 2001 by Larry Lessig, Hal Abelson, and Eric Eldred to
give creators of digital artistic works the ability to set terms of use on their creation that differ that
those arising from copyright. Creative Commons provides a set of licenses with terms of use for
work that differ from, and are usually more permissive than, the default copyright.
Intellectual Property and Computational Science 231
The Creative Commons licenses discussed in the previous section were created
for digital artistic works and they are not suitable for code. There are, however, a
great number of open licenses written for software. Each of these licenses sets
some specific terms of use for the software (none of them rescind the underlying
copyright). Software can exist in two forms, source and compiled, and for mod-
ification transmission of the compiled form alone is not sufficient. In the context of
scientific research, source code is often in the form of scripts, python or R for
example, that execute in association with an installed package and are not com-
piled. Communication of the source code, whether intended to be compiled or not,
is essential to understanding and re-using scientific code.
There are several open licenses for code that place few restrictions on use
beyond attribution, creating the closest Intellectual Property framework to con-
ventional scientific norms. The (Modified) Berkeley Software Distribution (BSD)
license permits the downstream use, copying, and distribution of either unmodified
or modified source code, as long as the license accompanies any distributed code
and the previous authors’ names are not used to promote any modified downstream
software. The license is brief enough it can be included here:
Copyright (c) \YEAR[, \OWNER[
All rights reserved.
Redistribution and use in source and binary forms, with or without modifica-
tion, are permitted provided that the following conditions are met:
• Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the documentation and/or other
materials provided with the distribution.
• Neither the name of the \ORGANIZATION[ nor the names of its contributors
may be used to endorse or promote products derived from this software without
specific prior written permission.
This template is followed by a disclaimer releasing the author from liability for
use of the code. The above copyright notice and list of conditions, including the
disclaimer, must accompany derivative works. The Modified BSD license is very
similar to the MIT license, with the exception that the MIT license does not
include a clause forbidding endorsement. The Apache 2.0 license is also com-
monly used to specify terms of use on software. Like the Modified BSD and MIT
licenses, the Apache license requires attribution. It differs from the previously
discussed licenses in that it permits users the exercise of patent rights that would
otherwise only extend to the original author, so that a patent license is granted for
any patents needed for use of the code. The license further stipulates that the right
to use the software without patent infringement will be lost if the downstream user
of the code sues the licensor for patent infringement. Attribution under Apache 2.0
requires that any modified code carries a copy of the license, with notice of any
modified files and all copyright, trademark, and patent notices that pertain to the
work must be included. Attribution can also be done in the notice file.
232 V. Stodden
The Reproducible Research Standard (Stodden 2009a, b) recommends using
one of these three licenses, Modified BSD, MIT, or Apache, for scripts and
software released as part of a scientific research compendium, or a similar open
license whose only restriction on reuse is attribution.
Patents are a second form of intellectual property that can be a barrier to the
open sharing of scientific codes. For example, as noted of the University of British
Columbia’s website,
Members of faculty or staff, students and anyone connected with the University are
encouraged to discuss and publish the results of research as soon and as fully as may be
reasonable and possible. However, publication of the details of an invention may make it
impossible to seek patent protection.18
Publication is, of course, the primary way research findings are made available,
and authors who seek patents may be less likely to openly release their software, as
software is a patentable entity (Stodden 2010, 2011). As university technology
transfer offices often encourage startups based about patentable technology and
software, the incentive to release code that permits others to replicate published
findings is reduced. These two systems, technology transfer through patents and
scientific integrity through openly available software, can co-exist. A dual-
licensing system, for example, can be introduced that enables patent revenues for
commercial downstream use, while permitting Open Access for research use such
as verification of findings and re-use of code for research application (Stodden and
Reich 2011).
It should be made clear that the code and scripts alone are not generally suf-
ficient to ensure reproducible research, nor to understand the scientific findings in
question. The accompanying narrative, documentation, and meta-data are an
essential part of understanding the research findings and for their verification and
replication.
Conclusion
The current set of scientific norms evolved to maximize the integrity of our stock
of scientific knowledge. Hence they espouse independent verification and trans-
parency, and historically this has been part of the rationale for the publication of
research findings. The complexity of modern computational science means that in
order to make reproducibility possible new types of scholarly objects, data and
code, must be communicated. In this chapter I have traced how Intellectual
Property Law creates barriers to scholarly communication, through both the
copyright and patent systems and suggested solutions and workarounds.
18 University of British Columbia Policy on Patents and Licensing, March 1993, http://
www.universitycounsel.ubc.ca/files/2010/08/policy88.pdf
Intellectual Property and Computational Science 233
For broad reuse, sharing, and archiving of code to be a possibility, it is
important that open licenses be used that minimize encumbrances to access and
reuse, such as attribution only licenses like the MIT license or the Modified BSD
license. A collection of code with an attribution only licensing structure, or public
domain certification, permits archiving, persistence of the code, and research on
the code base itself. Similarly for collections of research articles. The current
system of distributed permission-based ownership makes archiving, research
extensions, and scholarly research on publications next to impossible. For these
reasons, as well as the integrity of our body of scholarly knowledge, it is imper-
ative to address the barriers created by current Intellectual Property Law in such a
way that access, reuse, and future research are promoted and preserved.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Intellectual Property and Computational Science 235
Research Funding in Open Science
Jörg Eisfeld-Reschke, Ulrich Herb and Karsten Wenzlaff
“The ultimate act of Scholarship and Theater is the art of
selling”
George Lois
Abstract The advent of the Open Science paradigm has led to new interdepen-
dencies between the funding of research and the practice of Open Science. On the
one hand, traditional revenue models in Science Publishing are questioned by
Open Science Methods and new revenue models in and around Open Science need
to be established. This only works if researchers make large parts of their data and
results available under Open Access principles. If research funding wants to have
an impact within this new paradigm, it requires scientists and scientific projects to
make more than just text publications available according to the Open Access
principles. On the other hand, it is still to be discussed how Research Funding itself
could be more open. Is it possible to generate a new understanding of financing
science shaped by transparency, interaction, participation, and stakeholder gov-
ernance—in other words reach the next level as Research Funding 2.0? This article
focuses on both of the aspects: Firstly, how Research Funding is promoting Open
Science. Secondly, how an innovative and open Research Funding might look like.
J. Eisfeld-Reschke 
 K. Wenzlaff
Institut für Kommunikation in sozialen Medien, Berlin, Germany
e-mail: eisfeld-reschke@ikosom.de
K. Wenzlaff
e-mail: wenzlaff@ikosom.de
U. Herb (&)
Saarland University, Saarbrücken, Germany
e-mail: u.herb@sulb.uni-saarland.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_16, 
 The Author(s) 2014
237
Research Funding Policies: Pushing forward Open Science
In the past decades, with new technology allowing interactive communication
between content producers, content consumers, and intermediaries such as the
publishing industry, there has been tremendous pressure on journalists, politicians,
and entrepreneurs to become more accessible. The Web 2.0, a buzzword com-
prising of phenomena such as blogs and social networks, is shaping not just
communication, but also decision-making in communities ranging from Parlia-
mentarians to CEOs, and from TV staff to newspaper editors.
The scientific community has not felt the same amount of pressure. Never-
theless, a few scientists have taken the lead: Science blogs are increasingly popular
and scientists tweet and interact with a larger audience. Universities have opened
pages on Facebook and other social networks, or have created internal networks
for communication. We have even witnessed the rise of social networks that
exclusively address scientists (see Academia Goes Facebook? The Potential of
Social Network Sites in the Scholarly Realm). A silent revolution is on its way
with more and more students entering the ivory tower of academia with a set of
communication expectations based on real-time communication across borders,
cultures, and scientific clubs. The ivory tower opened up some windows which are
now being showcased as Science 2.0.
Meanwhile, a second silent revolution has been taking place. In 2002, the
Soros-founded Budapest Open Access Initiative1 called for more publications
along their principles. Ten years later, Open Access has spread to a plethora of
scientific communities, publishers and journals (Björk et al. 2010; Laakso et al.
2011; Laakso & Björk 2012). The more common Open Access has become, the
more people start to think about what other sorts of scientific information might be
available openly. As is the case with Open Access, for many initiatives striving for
Openness in the context of scientific information it is quite unclear what kind of
Openness they are claiming or how they define Openness. Some Open Access
advocates are satisfied if scientific publications are available online and free of
charge (so called Gratis Open Access) while others want these publications to be
made available according the principles of the Open Definition,2 that applies the
Open Source paradigm to any sort of knowledge or information, including the
rights to access, reuse, and redistribute it.
Considering the Open Science concepts as an umbrella, there are several ini-
tiatives (see Herb 2012) that want to open up nearly every component or single
item within research and scientific workflows, e.g.:
• Open Review, which includes both review of funding proposals and articles that
are submitted for publication, the latter traditionally conducted as a peer review.
Open Review does not so much aim for Openness according to the Open
1 Budapest Open Access Initiative: http://www.soros.org/openaccess
2 Open Definition: http://opendefinition.org/
238 J. Eisfeld-Reschke et al.
Definition or the Open Source Principles, rather it is meant to make the review
processes more transparent, impeding cliquishness between colleagues as sub-
mitting scientists and reviewers (Pöschl 2004).
• Open Metrics as a tool for establishing metrics for the scientific relevance of
publications and data that are independent from proprietary databases like the
Web of Science or the SCOPUS database which do not only charge fees, but also
disallow unrestricted access to their raw data.
• Open Access to scientific data according to the Panton Principles.3
• Open Access to scientific publications.
• Open Bibliography, meaning Open Access to bibliographic data.
As we can see, some of these initiatives focus on free or Open Access to science
related information (in the shape of scientific data, scientific publications, or bib-
liographic data), while others promote amore transparent handling of the assessment
processes of scientific ideas and concepts (such as Open Review and OpenMetrics).
Many prominent funding agencies have already adopted policies that embrace
single elements of an Open Science. Among others, the National Institutes of
Health NIH,4 the Wellcome Trust,5 the European Research Council,6 and the
upcoming European Commission Framework Horizon 20207 also require funded
projects to make project-related research data and publications freely available.
The unfolding science of the future will be characterized not only by seamless
and easy access, but also by interaction, networked and integrated research
information workflows and production cycles, openness, and transparency. Sci-
ence (at least in the Western hemisphere) was an open process in antiquity, having
been debated in agoras in the centre of Greek cities. Even in the Roman Empire,
the sharing of ideas across the Mediterranean Sea had a profound impact on
civilisation—the French, Swedish, English, Italian and German languages attest to
the common linguistic principles that developed in this era. Only with the ideo-
logical dominance of the Catholic doctrines following the collapse of the Roman
Empire did science retreat to monasteries, and scholarly debate to universities and
peer-reviewed science communities. The Enlightenment ensured that the educated
citizenry became involved in science, but only the Internet has pushed the pos-
sibility for a complete citizen science, not unlike how the Greek science com-
munity would have seen the debate.
Even though the online media and the initiatives mentioned above brought
Openness back to scientific communication, one might ask what research funding
which is compliant with the paradigms of Openness and Science 2.0 might look
3 Panton Principles: http://pantonprinciples.org/
4 SHERPA/JULIET. NIH: http://www.sherpa.ac.uk/juliet/index.php?fPersistentID=9
5 SHERPA/JULIET. Wellcome Trust: http://www.sherpa.ac.uk/juliet/index.php?fPersistentID=
12
6 SHERPA/JULIET. European Research Council: http://www.sherpa.ac.uk/juliet/index.php?f
PersistentID=31
7 European Commission: http://ec.europa.eu/research/horizon2020/index_en.cfm
Research Funding in Open Science 239
like. As we have seen, many funding agencies require fundees to make their
project-related research results (as data or text publication) more or less open, or at
least freely available, but until now the processes of research funding are hardly
ever considered to be relevant to Open Science scenarios and appear to be closed,
hidden, and opaque (in contradiction to any idea of Openness).
Research Funding at Present: Limitations and Closed
Discourses
Usually, applications for research funding are reviewed and assessed in closed
procedures similar to the review and assessment of articles submitted for publi-
cation in scientific journals. This also entails that the reviewers are unknown to the
applicant, while, on the other hand, the applicant is known to the reviewers (so-
called single blind review). Horrobin describes the process of evaluating a funding
application as follows:
“A grant application is submitted. The administrators send it to reviewers
(usually two) who are specialists in the field and therefore competitors of the
applicant. A committee (usually between ten and 30 members) assesses
the application and the reviewers’ reports, perhaps with a commentary from the
administration.” (Horrobin 1996, p. 1293). Not only the sequence of events
involved in the funding process, but also the results achieved through the funding
as well as the publication of the results show similarities: in both contexts, the so-
called Matthew Effect (Merton 1968) is evident. This effect describes the fact that
authors with an already high citation quotient are more likely to keep receiving a
high number of citations in the future, and that in the same vein, institutions
already attracting vast amounts of funding can expect to pull in more funds than
other institutions (see The Social Factor of Open Science, Fries: The Social Factor
in Open Science). A current study of the Sunlight Foundation reveals this effect for
example in the funding patterns of the National Science Foundation NSF: “Twenty
percent of top research universities got 61.6 % of the NSF funding going to top
research universities between 2008 and 2011.” (Drutman 2012).
Even the handing-over of the final funding decision from the peers to so-called
Selection Committees, whose verdicts are supposed to incorporate the judgments of
the peers, has led to similar results (v. d. Besselaar 2012). Furthermore, peer
decisions on research funding from the review process do not appear to be
objective: Cole, Cole & Simon presented reviewers with a series of accepted as
well as declined funding applications and examined the consistency of the (sec-
ond) judgment. The result: No significant connection between the first and the
second decision on the eligibility of a funding proposal could be established. The
results indicate “that getting a research grant depends to a significant extent on
chance. The degree of disagreement within the population of eligible reviewers is
such that whether or not a proposal is funded depends in a large proportion of cases
240 J. Eisfeld-Reschke et al.
upon which reviewers happen to be selected for it” (Cole et al. 1981, p. 881). A
study by Mayo et. al. produces similar conclusions, it “found that there is a
considerable amount of chance associated with funding decisions under the tra-
ditional method of assigning the grant to two main reviewers” (Mayo et al. 2006,
p. 842). Horrobin even diagnosed in 2001 “an alarming lack of correlation between
reviewers’ recommendations” (Horrobin 2001, p. 51).
Although the review process for publications as well as for funding proposals is
similar, the consequences of distortions in the reviewing of funding applications
are far more dramatic. Whereas even a mediocre article, after a series of failed
submissions, can hope to be eventually accepted by some lesser journal, an
application for research funding is stymied from the beginning by the paucity of
funding organizations: “There might often be only two or three realistic sources of
funding for a project, and the networks of reviewers for these sources are often
interacting and interlocking. Failure to pass the peer-review process might well
mean that a project is never funded.” (Horrobin 2001, p. 51).
Horobin suggests that the review process for research funding is inherently
conservative as evidenced by the preference for established methods, theories, and
research models, and that reviewers are furthermore “broadly supportive of the
existing organization of scientific enterprise”. He summarizes: “it would not be
surprising if the likelihood of support for truly innovative research was consid-
erably less than that provided by chance.” (2001, p. 51). Consequently, the funding
bodies fund “research that is fundamentally pedestrian, fashionable, uniform, and
second-league—the sort of research which will not stand the test of time but
creates an illusion that we are spending money wisely. The system eliminates the
best as well as the worst and fails to deliver what those providing the funds
expect.” (Horrobin 1996, p. 1293). The preference for mainstream research is thus
an impediment to innovation: “The projects funded will not be risky, brilliant, and
highly innovative since such applications would inevitably arouse broad opposi-
tion from the administrators, the reviewers, or some committee members.”
(Horrobin 1996, p. 1293). In addition, traditional research funding promotes uni-
formity: “Diversity—which is essential, since experts cannot know the source of
the next major discovery—is not encouraged.” (Horrobin 1996, p. 1294). In a
meta-study on the effect of peer reviewing in the funding process, Demicheli and
De Pietrantonj came to the sobering conclusion that: “No studies assessing the
impact of peer review on the quality of funded research are presently available.”
(Demicheli and Di Pietrantonj 2007, p. 2).
Critics of classic research funding are therefore demanding among other alter-
natives the allocation of a part of the available funds by lot through an innovation
lottery (Fröhlich 2003, p. 38) or through the assignment of funds in equal parts to all
researchers: “funds [should be] distributed equally among researchers with aca-
demic (…) positions” (Horrobin 1996, p. 1294). Additionally, the application of the
Open Review described above would ensure greater transparency of the review
Research Funding in Open Science 241
process as well as prevent or counteract distortions; however, in actual fact, Open
Review is hardly practiced in funding processes.8
In the following, the question as to what extent crowdfunding and other
innovative funding procedures and channels may serve as an alternative to tradi-
tional research funding will be examined.
Open Research Funding
Open Science and Open Research Funding share mutual spheres of interest. Both
want to advance science through the involvement of citizens, and both want to
make content available that was previously hidden behind paywalls of traditional
science publishers, informal boundaries of scientific-peer-communities, or formal
boundaries established by private or public funding-bodies. It can be compared as
to how two areas of content creation with similar situation have addressed this
demand for open content: journalism and arts. In both realms, the suppliers of
content vastly outnumber the financiers of content.
There are many more journalists, writers, photographers out there willing to
provide content than there are people willing to invest in large news corporations,
which before the digital era were the only institutions capable of funding large-
scale news publishing. Notwithstanding the bromide that the Internet has allowed
everybody to publish, it has also allowed everyone to find a financier for pub-
lishing—through self-publishing on the eBook market through micropayments to
crowdfunding sites like Spot.us9 or Emphas.is10 we have seen the gradual
development of democratized news funding.
Similarly in the arts. While true skills in the arts still require perseverance,
endurance, and none-the-least talent, the Internet has allowed artists to broaden
their audience and reach out to fans, thus converting them into patrons for the arts.
Therefore artists now enjoy avenues outside of the traditional mechanism in which
content is being produced, sold, and licensed.
Let us examine some cases where this new freedom to find financiers for
content has blended dynamically with Open-Access principles. In 2011, the
Kickstarter11 project “Open Goldberg variations”12 reached US-$ 23.748 by
recording the Bach Goldberg Variations for release into the public domain13:
8 At least some scientists make their funding proposals available after the review is finished
(on their motivation see White 2012).
9 Spot.us: http://spot.us/
10 Emphas.is: http://www.emphas.is
11 Kickstarter: http://www.kickstarter.com/
12 Open Goldberg Variations: http://www.opengoldbergvariations.org/
13 Kickstarter: http://www.kickstarter.com/projects/293573191/open-goldberg-variations-setting-
bach-free
242 J. Eisfeld-Reschke et al.
We are creating a new score and studio recording of J.S. Bach’s Goldberg Variations, and
we’re placing them in the public domain for everyone to own and use without limitations
on licensing. Bach wrote his seminal work over 270 years ago, yet public domain scores
and recordings are hard or impossible to find. Until now! read the introduction at the
Kickstarter project.
The focus of the project is to generate funds for a piece of music that can be
found across the world in music stores, but not in the Public Domain with easy
redistribution, sharing, and mash-up-possibilities.
A similar idea drove the German platform Sellyourrights.org.14 Artists were
encouraged to release their music into the public domain by having the music
crowdfunded through the fans. Unfortunately, that experiment was stopped when
the German GEMA—a collective rights union for artists—prohibited musicians
(not just GEMA members) from share their work on the platform.15
Spot.us—an American platform to crowdfund journalism—was also motivated
by public access to investigative journalism. All crowdfunded articles were
released under a CC-by-license, thus allowing attribution and sharing.16 Again, the
motivation to create content outside of traditional publishing avenues is a key
factor in the success of crowdfunding.
Open Research Funding: Some Considerations
A consideration of the criticism voiced against the traditional models of research
funding suggests that innovative procedures of research funding should exhibit
certain characteristics. For example, the decision-making process leading to a
verdict for or against a funding request should be transparent. Ideally, the decision
would be taken not only by a small circle of (frequently) only two reviewers, but
also involve the entire academic community of the subject in question. Moreover,
the question arises as to whether in the age of electronic communication research
funding still has to be based on bureaucratic models of delegating responsibility, or
whether the direct participation and involvement of researchers as well as citizens
might conceivably be practiced. Since research funding is not only subject to the
inherent conservative tendencies mentioned above, but also thematically restricted
by the available funding programs, it can also be asked as to whether there might
not be more flexible vehicles that are better suited for the funding of niche projects
unrelated to the buzzwords or academic discourses of the day.
14 Sell your rights. The Blog: http://blog.sellyourrights.org/
15 Unreturned Love: http://blog.sellyourrights.org/?p=252
16 Poynter: http://www.poynter.org/latest-news/top-stories/89906/break-the-mold-brainstorming-
how-newspapers-could-make-money/
Research Funding in Open Science 243
Crowdfunding
Obviously, crowdfunding meets several of the aforementioned criteria. Jim Giles,
while commenting also on the public nature of crowdfunding, states in this con-
text: “Crowd-funding—raising money for research directly from the public—looks
set to become increasingly common.” (Giles 2012, p. 252). In principle, crowd-
funding can also be more closely intertwined with the model of Citizen Science
than traditional research funding organizations—for those organizations only a
select and narrowly defined circle of applicants, whether institutions or individu-
als, are eligible. While this pre-selection of fundees may be justified by quality
assurance considerations, it also effectively excludes researchers on the basis of
institutional (and thus non-academic) criteria—a practice that in an age of rising
numbers of Independent Researchers conducting their research outside of the
university framework may well be put into question. Giles also points out the
connection between research and the civic society established through crowd-
funding: “It might help to forge a direct connection between researchers and lay
people, boosting public engagement with science.” (2012, p. 252)
Publicly financed research nevertheless does not appear well-suited to crowd-
funding in the classic sense: research as a process is lengthy, institutionally
anchored, and team-based. It is also expensive. The results of the research process
in many disciplines are not immediately ‘tangible’—rather they are documented in
articles, books, or conference presentations, and more rarely in patents and new
products. Researchers are normally not judged or evaluated according to how well
they can explain their research to the public (not to mention to their students), but
according to how successfully they present themselves to their peers in the aca-
demic journals, conferences, and congresses of their respective fields.
However, the most important impediment is the research process itself. In
ancient times, the ideal of gaining knowledge was the open dialogue in the Agora.
Our notion of science since the advent of modernity has been marked by the image
of the solitary researcher conducting research work in splendid isolation either in a
laboratory or library. Although this myth has long since been destroyed by the
reality of current research, it is still responsible for the fact that a substantial part of
the publicly financed academic establishment feels no pressure to adequately
explain or communicate their research and its results to the public. In particular,
there is no perceived need to involve the public even in the early stages of the
research project, for example in order to discuss the research motivations and
methods of a project.
An alternative model of research is described by Open Science. Open Science
aims to publish research results in such a manner that they can be received and
newly interpreted by the public, ideally in an Open Access journal or on the
Internet. However, Open Science also means that research data are made public
and publicly accessible. Yet this concept of Open Access is incompatible with
current financing models which profit from limited access to research. The concept
is, however, compatible with the basic notion underlying crowdfunding—that of
244 J. Eisfeld-Reschke et al.
the democratization of patronage of the arts, which bears many similarities to the
public patronage of the sciences. A quick glance at the mutually dependent rela-
tionship existing between the ‘free’ arts and company sponsors, wealthy individ-
uals, and the funding agencies of the public sector reveals that the supposedly
‘free’ sciences are tied up in an equally symbiotic relationship with political and
economic interests.
The democratization of research financing does not necessarily lead to a
reduction in dependency but rather increases reciprocity. Nevertheless, in analog
fashion to the creative industries, crowdfunding can also be used as an alternative,
supplementary, or substitute financing instrument in research funding. Just as
crowdfunding does for film, music, literature, or theatre projects, crowdfunding in
research has one primary purpose: To establish an emotional connection between
the public and an object.
If seen in this way, the ideal of the ‘rational scientist’ seems to contrast with
‘emotional science’. Yet the enormous response elicited in our communications
networks by a small NASA robot operating thousands of miles away on another
planet testifies to the emotional potential inherent in science. The example of
CancerResearchUK17 demonstrates that this potential can also be harnessed for
crowdfunding. As part of the CancerResearchUK initiative, attempts were made to
increase donations to scientific projects by means of crowdfunding. The special
draw of the campaign was the chance for supporters to reveal their personal
connection to cancer—be it the death from cancer of a friend or relative, the
recovery, the effect of medicines, medical advances, or research.
What then should a crowdfunding platform for science look like, if it is sup-
posed to be successful? One thing is clear. It will not look like Kickstarter, In-
diegogo,18 or their German equivalents Startnext,19 Inkubato,20 Pling,21 or
Visionbakery.22 The Kickstarter interface we have become accustomed to in the
creative industries which can increasingly be considered as ‘learned’ or ‘acquired’
would make little sense in a scientific research context. Kickstarter is successful
because four key information elements are made so easily graspable and trans-
parent: Who started the crowdfunding project? How much money is needed
overall? How much still has to be accumulated? What do I get for my
contribution?
Presumably, crowdfunding for science and research will have to rely on entirely
different types of information. A film, for example, is inextricably bound up with
the name of a director, or producer, or actors; a team of a different stripe will not
be able to replicate this effect. In science, however, the replicability of methods,
17 Cancer Research UK: http://supportus.cancerresearchuk.org/donate/
18 Indiegogo: http://www.indiegogo.com/
19 Startnext: http://www.startnext.de/
20 Inkubato: http://www.inkubato.com/de/
21 Pling: http://www.pling.de/
22 Visionbakery: http://www.visionbakery.com/
Research Funding in Open Science 245
arguments, and results is key—therefore research and researcher must be inde-
pendent of each other. The knowledge gap a crowdfunding project is supposed to
close is much more salient than the individual researcher. Thus, the big challenge
faced by a crowdfunding platform for research is to visualize this gap.
The target amount is of much lesser concern in research crowdfunding than in
crowdfunding in the creative industries. In the CancerResearchUK project, this
information was visualized—the score of funds received up to the current point
was visually indicated on the site—but for most donors the decisive factor in
joining was not whether the total amount needed was ₤ 5,000 or ₤ 50,00,000, but
the concrete result that a contribution of ₤ 5 or ₤ 50 from crowdfunding would
bring about.
Last, but not not least, the rewards: For many research projects, much thought
and creativity will have to be devoted to the rewards, if a reward-based system of
crowdfunding is to be favored over a donation-based system like Betterplace23.
Reward-based crowdfunding on sites like Kickstarter make it essential to provide
some material or immaterial rewards to incentivize a contribution; donation-based
crowdfunding relies solely on the charitable appeal of a project.
Finding material rewards that are closely connected to the research process for
science projects is not an easy proposition—after all, the results of such a project
are much more complex than in most crowdfunding projects. Of much greater
relevance than the question of what the scientist can give back to the community is
hence the problem of what the community can give to the researcher. For this
reason, the reward actually lies in the access to science and the research process
that participation in the funding initiative allows.
A crowdfunding platform should therefore conceptualize and visualize the
following three elements as effectively as possible: (a) visualization of knowledge
gaps, (b) results achieved by funds, (c) participation options for supporters.
Although crowdfunding is still the exception among the financing instruments
used for research projects, it has nonetheless advanced beyond the merely
experimental stage. Among other projects, scientists were able to raise $ 64,000
through the crowdfunding platform Open Source Science Project24 OSSP for a
study on the water quality of the Mississippi River (Giles 2012, p. 252). The way a
scientific crowdfunding platform works so far is largely identical to the way
platforms devoted to other content categories operate: “Researchers start by
describing and pricing a project, which they submit to the site for approval. If
accepted, the pitch is placed online and donors have a few weeks or months to read
the proposal and make a donation. Some sites operate on a non-profit basis and
channel all proceeds to researchers; others are commercial concerns and take a cut
of the money raised.” (Giles 2012, p. 253).
23 Betterplace: http://www.betterplace.org/de/
24 The Open Source Science Project: http://www.theopensourcescienceproject.com/
246 J. Eisfeld-Reschke et al.
Social Payments in Science
Related to crowdfunding, but not entirely the same, are new tools known as social
payments. Typically, these are micropayments given for content that already exists
on the net. They share with crowdfunding the notion of many small payments
generating large income through accumulation. Flattr25 and Kachingle26 are two
tools commonly associated with social payments. They are a little different from
each other, but share the idea that a content creator embeds a small button on its
webpage, and a content financer in pushing that button ships a small payment to
the content creator.
When the New York Times put their blogs behind a flexible paywall in 2010,
Kachingle rose to the occasion and allowed the readers to “kachingle” the New
York Times blogs, in other words transferring a little bit of money to the writers
behind the blogs every time they visited the site. The New York Times was not
amused and sued Kachingle for using their trademarks—which in the eyes of most
commentators was a reaction to new forms of financing typical of a news monolith.
Flattr, another social payment provider, has deep connections with the Creative
Commons ecosphere. The website Creative Commons employs a Flattr-button to
earn micropayments27 and many bloggers are putting their content both under a
CC license and a Flattr-button. However, there is also one mishap present: Cre-
ative Commons are typically shared under a Non-Commercial Clause, which
would prohibit the use of Flattr on any blog licensing content into the public
domain.28
How can social payments be applied to science? Already Scienceblogs are
using the social payment system—not necessarily for monetary gains but also for
sharing content, engaging in conversation with readers, and measuring
relevance29:
“It is easy to find out how many people access a certain Internet site—but those numbers
can be deceiving. Knowing that X number of people have clicked on my article on Y is no
doubt a good start. But I have no real insight on how many had a genuine interest in
precisely this article and have read my article and appreciated it and how many only found
my site after a Google search and left after 5 s. There may be tools allowing me to find
answers to these questions, but they will most likely require a lot of work and analysis. But
if I have a Flattr-button under each of my articles, I can assume that only people who
really appreciated reading them will click on it—after all, this click costs them real
money.” says Florian Freistetter, author of a blog on Astronomy.
25 Flattr: http://flattr.com/
26 Kachingle: http://www.kachingle.com/
27 FlattrBlog: http://blog.flattr.net/2011/08/great-things-creative-commons/
28 Techdirt: http://www.techdirt.com/articles/20120828/00585920175/should-creative-commons-
drop-its-noncommercial-noderivatives-license-options.shtml
29 Science Blogs: http://scienceblogs.de/astrodicticum-simplex/2010/06/05/warum-ich-flattr-gut-
finde/, translated by the authors.
Research Funding in Open Science 247
The real potential of social payments lies in combination with Open Access
journals, archives, and publications. Imagine, for instance, databases of publicly
available data which allow the users of content to flattr or kachingle the site
whenever they visit it? This would allow the making of money from scientific
content beyond the traditional licensing systems of universities and libraries.
Imagine if a university has a Flattr account filled with 100,000 Euros per year.
Every time a university member accesses a scientific journal, the 100,000 Euro is
divided among the clicks. This could generate a demand-based but fully trans-
parent way of funding science.
Social payments could also be integrated into direct funding: For instance,
through scientists receiving a certain amount of public money or money from
funding institutions which cannot be used for their own projects but must be
donated to other projects in their discipline. Funds as yet unassigned would remain
in a payment pool until the end of the year and then be divided up equally among
all projects.
There seems to be some evidence30 showing that distributions of social pay-
ments follow roughly the sharing and distribution behavior of content. In other
words, content which is often liked, shared, and tweeted is more likely to receive
funds through Flattr.
Social payments are thus likely to generate an uneven distribution of science
funding—controversial, popular articles and data might generate more income
than scientific publications in smaller fields.
Groundbreaking research might profit from such a distribution mechanism,
especially if a new idea applies to a variety of disciplines. The established citation
networks of scholars and the Matthew Effect mentioned above might even be
stabilized.
Social payments in combination with measuring social media impact could
provide an innovative means of measuring relevance in science. Such indices
would not replace traditional impact scores, such as appearances in journals,
invitations to congresses, and third-party funding, but would allow assessment of
the influence of scientific publications within the public sphere.
Virtual Currencies in Science
All of the tools described above relate in one way or another to real cash-flows in
the overall economy. However, these mechanisms might also work with virtual
currencies which may be linked to existing currencies, but not in a 1-to-1-
relationship.
In Flattr, it is customary to be able to use the earned income within the system
to Flattr new content, without having to withdraw cash. The Flattr ecosystem
30 Medien-Ökonomie-Blog: http://stefan-mey.com/2010/05/02/deutsches-flattr-ranking/
248 J. Eisfeld-Reschke et al.
generates its own value of worth. Similarly, on crowdfunding sites such as
Sellaband31 or Sonicangel,32 fans can use some of the rewards they receive to fund
new artists. The money stays inside the ecosystem of the platform. Virtual cur-
rencies are used often in games, whereby gamers can turn real money into virtual
money such as Linden Dollars on Second Life or Farmdollars on Farmville; the
virtual money buys goods and services inside the game, both from other players
and the game provider, and the earned income can be withdrawn at any time. It
might be conceivable that a scientific community creates its own virtual currency.
The currency could be used to trade and evaluate access to data, publications, or
other scientific resources. Let us imagine for instance that a scientist receives a
certain amount of ‘Aristotle-Dollars’ for every publication in the public domain.
Based on the amount of ‘Aristotle-Dollars’ which they earn, they receive earlier
access to public data.
Some Critical Reflections
Quality Assurance and Sustainability
One advantage of the peer review system is seen in the provision of quality
assurance, although the procedure, as stated before, has been criticized. Some of the
crowdfunding platforms hosting scientific project ideas also use peer review (for
further details, see Giles 2012); for instance, SciFlies33 and OSSP only publish
project ideas after an expert review. Moreover, only members of scientific insti-
tutions are allowed to present project ideas via OSSP. In one possible scenario,
researchers could identify themselves in crowdfunding environments by means of
an author identifier such as the Open Researcher and Contributor ID ORCID34 and
document their expert status in this way (see Unique Identifiers for Researchers,
Fenner: Unique Identity for a Researcher). Project proposals from the #SciFund
Challenge,35 on the other hand, were not subject to proper peer review but were
scrutinized only in passing. Since the crowdfunding model, however, demands that
each submission reveals the research idea and the project realization, a mechanism
of internal self-selection can be posited: It can be assumed that scientists will only
go public in crowdfunding environments with projects that are carefully conceived.
The same applies to plagiarism and idea or data theft—these types of academic
misbehavior would almost certainly be revealed through the public nature of the
procedure. The same arguments have also been offered in support of Open Review.
31 Sellaband: https://www.sellaband.de/
32 Sonicangel: http://www.sonicangel.com/
33 SciFlies: http://sciflies.org/
34 ORCID: http://about.orcid.org/
35 #SciFund Challenge: http://scifundchallenge.org/
Research Funding in Open Science 249
Ulrich Pöschl, editor of the journal Atmospheric Chemistry and Physics36 (ACP),
stresses the fact that the transparency of the submissions process in ACP increases
the quality of submissions because authors are discouraged from proposing articles
of mediocre or questionable quality (Pöschl 2004, p. 107): In the interest of self-
protection and self-preservation, scientists can be expected to refrain from
exposing and potentially embarrassing themselves in their community with pre-
mature or ill-conceived publications. Furthermore, crowdfunding relies upon self-
regulation through the expertise of donors who in most cases are able to judge the
merits of a proposal themselves, so that weak proposals, if they are made public at
all, will have very poor prospects. Some crowdfunding platforms also use forums
as additional mechanisms of quality assurance; in FundaGeeks37 “Geek Lounge”,
for instance, potential donors can exchange their thoughts on the weaknesses or
strong points of a project idea. Thanks to an expert discussion in the Kickstarter
forum, a questionable project could be stopped without financial loss for the
donors (Giles 2012, p. 253).
In spite of the positive outlook outlined above, scientific crowdfunding has yet
to prove the advantages claimed for it. To conclude with Jim Giles: “For crowd-
funding to make a real difference, advocates will have to prove that the process—
which sometimes sidesteps conventional peer review — channels money to good
projects, not just marketable ones.” (Giles 2012, p. 252). Also somewhat ques-
tionable is the long-term perspective of the projects: Unlike classic research
funders, crowdfunding donors can hardly require fundees to only develop sus-
tainable service infrastructures, for example. Conversely, crowdfunding, social
payments, and virtual currencies may create new funding avenues facilitating the
funding of specific individual researchers rather than abstract projects with fluc-
tuating staff. Small projects with a funding volume below the funding threshold of
classic funders could also be financed with these instruments.
Plagiarism
As already mentioned, the public character of proposals for crowdfunding is more
likely to expose plagiarism in projects than closed review procedures. For the same
reason, researchers submitting their project ideas for crowdfunding demonstrate
their claim to a scientific idea or method in a manner that can hardly be ignored,
thus discouraging the plagiarizing of ‘their’ project. To put things into perspective,
it must be mentioned that plagiarism or idea theft has also been reported for closed
review procedures (Fröhlich 2003, p. 54).
36 Atmospheric Chemistry and Physics: http://www.atmospheric-chemistry-and-physics.net/
37 FundaGeek: http://www.fundageek.com/
250 J. Eisfeld-Reschke et al.
Pop Science, Self-Marketing, and Verifiability
On account of its proximity to citizens and its public character, crowdfunding also
bears the inherent danger of unduly popularizing research, especially if any
individual may donate to a project. Even though internal platforms for crowd-
funding in which only researchers can donate to a project proposal may develop
faster, it is conceivable—as with the peer review in traditional funding—that
mainstream research is favored. Some also suspect that crowdfunding, but also
social payments, could establish a disproportionate preference of applied research
over basic research (Giles 2012, p. 253). The same could also be suspected for
popular science or science that lends itself easily to media portrayal.
Crowdfunding, social payments, and virtual currencies place new demands on
researchers’ self-marketing (Ledford 2012), but these demands need not be a bad
thing, since a clear, succinct, and understandable presentation of a project proposal
can only enhance and augment the verifiability and testability of scientific con-
cepts by eliminating the dense prose and difficult wording found in many funding
applications (language that is often mandated by funders’ requirements), thus
promoting the intersubjective verifiability of scientific concepts called for by
science theory and philosophy of science.
A more solid grounding in the scientific community might be achieved if
crowdfunding, social payments and virtual currencies were not applied in entirely
open contexts, but rather only within scientific communities (if necessary under
the umbrella of discipline-specific associations or learned societies). In such a
scenario, however, the aspect of involvement of civic society would be lost.
What About Openness?
Although crowdfunding, social payments, and virtual currencies appear as more
transparent than traditional avenues of research financing, the question of the
‘openness’ or accessibility of the research results nevertheless arises. Whereas
traditional financing institutions may require fundees to make project results
publicly available, it is as yet unclear how projects funded through the innovative
procedures detailed above might be mandated to make their project results
accessible for the public. Of equal importance may be the question of who owns
the rights to a project’s results. In the interest of transparency, it might be desirable
to make the names of donors who contributed to a project public, so as to identify
and prevent potential conflicts of interest. Conversely, the risk of sponsorship bias
must not be neglected. The term sponsorship bias refers to the production of
results—consciously or unconsciously—that are consistent with the presumed
expectations or desires of the financiers. To minimize the risks posed by conflicts
of interest as well as sponsorship bias, it may be advisable to keep the identity of
the financiers hidden from the fundees until the project’s end.
Research Funding in Open Science 251
Appendix: A List of Crowdfunding Platforms for Scientific
Projects
• #SciFund Challenge hostet by RocketHub, http://scifundchallenge.org/
• Open Source Science Project OSSP, http://www.theopensourcescienceproject.
com/
• SciFlies, http://sciflies.org/
• PetriDish, http://www.petridish.org/
• iAMscientist, http://www.iamscientist.com/
• Sciencestarter (by now the only German crowdfunding service for scientific
projects), www.sciencestarter.de
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Research Funding in Open Science 253
Open Innovation and Crowdsourcing
in the Sciences
Thomas Schildhauer and Hilger Voss
Abstract The advent of open innovation has intensified communication and
interaction between scientists and corporations. Crowdsourcing added to this trend.
Nowadays research questions can be raised and answered from virtually anywhere on
the globe. This chapter provides an overview of the advancements in open innovation
and the phenomenon of crowdsourcing as its main tool for accelerating the solution-
finding process for a given (not only scientific) problem by incorporating external
knowledge, and specifically by including scientists and researchers in the formerly
closed but now open systems of innovation processes. We present perspectives on
two routes to open innovation and crowdsourcing: either asking for help to find a
solution to a scientific question or contributing not only scientific knowledge but also
other ideas towards the solution-finding process. Besides explaining forms and
platforms for crowdsourcing in the sciences we also point out inherent risks and
provide a future outlook for this aspect of (scientific) collaboration.
What is Open Innovation and What is Crowdsourcing?
Nowadays, companies use online co-creation or crowdsourcing widely as a stra-
tegic tool in their innovation processes (Lichtenthaler and Lichtenthaler 2009;
Howe 2008). This open innovation process and opening up of the creation process
can be transferred to the field of science and research, which is called Open
science.
Here we see Open Science on a par with open innovation: both incorporate
external knowledge in a research process. One way of doing this is ‘‘crowd-
sourcing’’. Companies use open innovation when they cannot afford a research and
T. Schildhauer (&)  H. Voss
Institute of Electronic Business e.V., Affiliate Institute of Berlin University of the Arts,
Berlin, Germany
e-mail: schildhauer@ieb.net
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_17,  The Author(s) 2014
255
development department of their own but still need external or technical knowl-
edge, or when they want to establish an interdisciplinary route to problem-solving
processes. This can be an interesting working milieu for scientists as their
involvement in these open innovation processes may lead to third-party funding.
Firms usually initiate open innovation processes by setting up challenges of
their own or using dedicated platforms; participation is often rewarded by
incentives. The kinds of problems that crop up in the context of open innovation
can be very diverse: Challenges can include anything from a general collection of
ideas to finding specific solutions for highly complex tasks. IBM’s ‘‘Innovation
Jam’’ (www.collaborationjam.com) or Dell’s ‘‘IdeaStorm’’ (www.ideastorm.com)
(Baldwin 2010) quote companies that involve employees of all departments and
partners in open innovation as an example. They also interact with external
experts, customers and even researchers from universities and other scientific
institutions, who do not necessarily belong to the research and development
department, in order to pool and evaluate ideas. This concept was introduced by
Chesbrough (2003).
Firms tend to favor tried-and-tested solutions and technologies when working
on innovations: Lakhani (2006) defined this behavior as local search bias. To his
way of thinking, co-creation can be seen as a tool for overcoming the local search
bias by making valuable knowledge from outside the organization accessible
(Lakhani et al. 2007). Hippel names various studies that have shown some
favorable impacts that user innovation has on the innovation process
(Hippel 2005).
The advantages of open innovation are not only of interest to companies and
firms: it is also becoming increasingly popular with the scientific community in
terms of collaboration, co-creation and the acceleration of the solution-finding
process (Murray and O’Mahony 2007). In the fields of software development
(Gassmann 2006; Hippel and Krogh 2003) and drug discovery (Dougherty and
Dunne 2011) in particular, scientists have discovered the advantages of open
collaboration for their own work: Open Science—which, for the sake of simplicity,
we can define as the inclusion of external experts into a research process.
Most open innovation initiatives do not necessarily address the average Internet
user. Often scientists and other specialists from different disciplines are needed:
Lakhani et al. (2007) find that the individuals who solve a problem often derive
from different fields of interest and thus achieve high quality outcomes.
Open innovation accordingly refers to the inclusion of external experts into a
solution-finding process. This process was hitherto thought to be best conducted
solely by (internal) experts (Chesbrough 2003). Opening up the solution-finding
process is the initial step of using participatory designs to include external
knowledge as well as outsourcing the innovation process (Ehn and Kyng 1987;
Schuler and Namioka 1993). Good ideas can always come from all areas. Special
solutions, however, require specialized knowledge and, of course, not every
member of the crowd possesses such knowledge (‘‘mass mediocrity’’, Tapscott and
Williams 2006, p. 16). The intense research on open innovation confirmed this
conjecture (Enkel et al. 2009; Laursen and Salter 2006).
256 T. Schildhauer and H. Voss
Before the term ‘‘open innovation’’ came into existence it was already common
for companies to integrate new knowledge gained from research institutes and
development departments into their innovation processes (Cooper 1990; Cooper
and Kleinschmidt 1994; Kline and Rosenberg 1986).
Crowdsourcing refers to the outsourcing of tasks to a crowd that consists of a
decentralized, dispersed group of individuals in a knowledge field or area of
interest beyond the confines of the given problem, who then work on this task
(Friesike et al. 2013). Crowdsourcing is used by businesses, non-profit organiza-
tions, government agencies, scientists, artists and individuals: A well-known
example is Wikipedia, where people all over the world contribute to the online
encyclopedia project. However, there are numerous other ways to use crowd-
sourcing: On the one hand, the crowd can be activated to vote on certain topics,
products or questions (‘‘crowd voting’’), or they can also create their own content
(‘‘crowd creation’’). This input can consist of answering more or less simple
questions (such as Yahoo Answers), creating designs or solving highly complex
issues, like the design of proteins (which we will revert to further down).
Figure 1 provides an overview of the different aims and tasks of open inno-
vation and crowdsourcing. The position of the varying fields in the diagram
mirrors a certain (not necessarily representative) tendency: questions and answers
can be fairly complex, a task in design can be easy to solve and human tasks (like
Amazon Mechanical Turk)1 can actually include the entire spectrum from simple
‘‘click-working’’ to solving highly complex assignments.
This section focuses on scientific methods for Open Science via crowdsourcing,
also including possibilities and risks for open innovation and crowdsourcing in the
sciences. Two major aspects of online crowd creation via crowdsourcing are firstly
being part of a crowd by contributing to a question raised on a crowdsourcing
platform and secondly posing a question to be answered by a crowd.
Specialists, scientists in particular, are now the subject of closer inspection:
how can scientists and scientific institutions in particular take part in open inno-
vation and open up science projects, and how can they collaborate with
companies?
How Scientists Employ Crowdsourcing
The use of crowdsourcing not only makes it possible to pool and aggregate data
but also to group and classify data. It would seem, however, that the more specific
a task, the more important it becomes to filter specialists out of the participating
mass.
Scientists can chose between four main forms of crowdsourcing:
1 Amazon Mechanical Turk: https://www.mturk.com/mturk/welcome.
Open Innovation and Crowdsourcing in the Sciences 257
1. Scientists can connect to individuals and to interest communities in order to
collate data or to run through one or a set of easy tasks, such as measurements.
2. Scientists can also use the Internet to communicate with other scientists or
research labs with whom they can conduct research into scientific questions on
equal terms.
3. The second option can also work the other way round: scientists or experts can
contribute to scientific questions.
4. Options 2. and 3. are frequently chosen by firms and companies as a means of
open innovation, often combined with monetary benefits for the participant.
We can conclude that the two major forms of open innovation and crowd-
sourcing in the sciences are: contributing towards a solution (perspective 1) and
requesting a solution (perspective 2).
Perspective 1: Contributing to a Crowdsourcing
Process (Open Science)
One form of open innovation through crowdsourcing in the sciences is to con-
tribute to research questions by setting out ideas—often free of charge. In sciences
Fig. 1 Crowdsourcing and open innovation
258 T. Schildhauer and H. Voss
like mathematics and biology or medicine, in particular, Open Science is a well-
known and much appreciated form of scientific collaboration.
One famous example is the U.S. ‘‘Human Genome Project’’,2 which was
coordinated by the U.S. Department of Energy and the National Institutes of
Health. It was completed in 2003, two years ahead of schedule, due to rapid
technological advances. The ‘‘Human Genome Project’’ was the first large sci-
entific undertaking to address potential ethical, legal and social issues and
implications arising from project data. Another important feature of the project
was the federal government’s long-standing dedication to the transfer of tech-
nology to the private sector. By granting private companies licences for technol-
ogies and awarding grants for innovative research, the project catalyzed the
multibillion dollar U.S. biotechnology industry and fostered the development of
new medical applications.3 This project was so successful that the first, original
project spawned a second one: the ENCODE project.4 The National Human
Genome Research Institute (NHGRI) launched a public research consortium
named ENCODE, the Encyclopedia of DNA elements, in September 2003, to carry
out a project for identifying all the functional elements in the human genome
sequence.5
Another famous project was proposed by Tim Gowers, Royal Society Research
Professor at the Department of Pure Mathematics and Mathematical Statistics at
Cambridge University who set up the ‘‘polymath project’’,6 a blog where he puts
up tricky mathematical questions. Gowers invited his readers to participate in
solving the problems. In the case of one problem, it so happened that, 37 days and
over 800 posted comments later, Gowers was able to declare the problem as
solved.
Collaborating online in the context of Open Science can be achieved using a
multitude of various tools that work for scientists but also for any other
researchers: such as a digital workbench, where users can collaborate by means of
online tools (network.nature.com); They can also build open and closed commu-
nities or workshop groups consisting of worldwide peers by exchanging infor-
mation and insights (DiagnosticSpeak.com, Openwetware.org); researchers can
also establish social networks and pose questions and get them answered by col-
leagues from all over the world (ResearchGate.com).
2 Human Genome Project Information: http://www.ornl.gov/sci/techresources/Human_Genome/
home.shtml.
3 Human Genome Project Information—About the Human Genome Project: http://
www.ornl.gov/sci/techresources/Human_Genome/project/about.shtml.
4 The Encyclopedia of DNA Elements (ENCODE) Consortium: http://genome.ucsc.edu/
ENCODE.
5 National Human Genome Research Institute: http://www.genome.gov/10005107.
6 The polymath blog: http://polymathprojects.org.
Open Innovation and Crowdsourcing in the Sciences 259
Perspective 2: Obtaining Support for One’s Own
Science (Citizen Science)
The other aforementioned aspect of open innovation through crowdsourcing in the
sciences is to ask a community for help. This aspect refers to Luis von Ahn7 and
his approach to ‘‘human computation’’: numerous people carry out numerous small
tasks (which cannot be solved by computers so far). To ask a community for help
works in ‘‘citizen science’’, which, for the sake of simplicity, we can define as the
inclusion of non-experts in a research process, whether it is a matter of data
collection or specific problem-solving. Crowdsourcing is a well-known form of
‘‘citizen science’’. Here, as scientificamerican.com explains, ‘‘research often
involves teams of scientists collaborating across continents. Now, using the power
of the Internet, non-specialists are participating, too’’.8
There are various examples where scientists can operate in this manner and
obtain solutions by asking the crowd. This form of crowdsourcing is also present in
the media: scientific media bodies have established incorporated websites for
citizen science projects like www.scientificamerican.com/citizen-science/ where
projects are listed and readers are invited to take part. In view of the crowd-
sourcing strategy ‘‘Ask the public for help’’, scientists can use these (social) media
websites to appeal to the public for participation and help. A research area or
assignment might be to make observations in the natural world to count species in
the rain forest, underwater or pollinators visiting plants—especially sunflowers, as
in ‘‘The Great Sunflower Project’’9—or to solve puzzles to design proteins, such as
in the ‘‘Fold It’’10 project. Citizens interested in this subject and scientists working
in this field of knowledge can therefore be included as contributors.
A very vivid example is the ‘‘Galaxy Zoo’’11 project. Briefly, the task is to help
classify galaxies. Volunteers are asked to help and it turned out that the classifi-
cations galaxyzoo.org provides are as good as those from professional astrono-
mers. The results are also of use to a large number of researchers.
Another example of how to activate the crowd is ARTigo, a project from art
history. A game has been created and each player helps to provide keywords or
tags for images. The challenge here is to find more suitable tags—preferably
hitherto unnamed ones—than a rivaling player in a given time. The Institute for
Art History at the Ludwig Maximilian University in Munich was accordingly able
to collect more than four million tags for its stock of more than 30,000 images.12
Paying experts to perform this task would have been unaffordable.
7 Luis von Ahn: http://www.cs.cmu.edu/*biglou/.
8 Scientific American: http://www.scientificamerican.com/citizen-science/.
9 The Great Sunflower Project: http://www.greatsunflower.org.
10 Fold it: http://fold.it/portal/.
11 Galaxy Zoo: http://www.galaxyzoo.org.
12 ARTigo: http://www.artigo.org/about.html.
260 T. Schildhauer and H. Voss
The following examples show how different platforms allow scientists and
other experts to engage in open innovation, and how scientists and research
departments can use these platforms for their own purposes.
Innocentive Innoget Solution-
xchange
One
billion
minds
Presans Inpama Marblar
Country USA Spain India Germany France Germany UK
Founded
in
2001 2007 2008 2009 2008 2012 2012
Focus Innovation
challenges
(initiated by
companies/
organisations);
community
Innovation
challenges
and
presentation
of new
solutions
Innovation
challenges;
community
Scientific
and social
projects
Search
for
experts
on
specific
issues
Completed
ideas to
date
Develop
products based
on new and
existing
technologies;
gamification
Innocentive (www.innocentive.com)
Innocentive.com is the best known open innovation platform that allows compa-
nies and experts to interact (Wessel 2007). It was founded by the pharmaceutical
company Eli Lilly. According to their own information, there are now more than
260,000 ‘‘problem solvers’’ from nearly 200 countries registered on Innocen-
tive.com. Thanks to strategic (media) partnerships, however, more than 12 million
‘‘solvers’’ can be reached. Over $ 35 million were paid out to winners, with sums
ranging between $ 500 and over $ 1 million, depending on the complexity of the
task.
Forms of Challenges and Competitions
Challenges can either be addressed to the external community, or they may be
limited to certain groups of people, such as employees. There are also various
forms of competitions: ‘‘Ideation Challenge’’, ‘‘Theoretical Challenge’’, ‘‘RTP
Challenge’’ and ‘‘eRFP Challenge’’.
• ‘‘Ideation Challenges’’ are for the general brainstorming of ideas—relating to
new products, creative solutions to technical problems or marketing ideas, for
example. In this case, the solver grants the seeker the non-exclusive rights to his
ideas.
• ‘‘Theoretical Challenges’’ represent the next level: ideas are worked out in
detail, accompanied by all the information required to decide whether or not the
idea can be turned into a finished product or solution. The period of processing
is longer than for an ‘‘Ideation Challenge’’. The financial incentives are also
Open Innovation and Crowdsourcing in the Sciences 261
much higher. The solver is usually requested to transfer his intellectual property
rights to the seeker.
• ‘‘RTP Challenge’’ calls for a further solution layout on how the solution, once it
has been found, can be applied by the company that invited contributions. The
‘‘solvers’’ are accordingly given more time to elaborate the drafts they have
submitted, while the reward rises simultaneously. Depending on how the
challenge is put, either the intellectual property rights are transferred to the
‘‘seeker’’ or the ‘‘seeker’’ is at least given a licence to use.
• ‘‘eRFP Challenge’’ is the name of the last option to pose a challenge. Scientists
in particular might be interested in this option: Normally, companies have
already invented a new technology and are now looking for an experienced
partner—like external consultants or suppliers—to finalize the developed
technology. In this case, legal and financial stipulations are negotiated directly
between the cooperating parties.
The site’s ‘‘Challenge Center’’ feature provides an opportunity for interaction
between the ‘‘seekers’’ and ‘‘solvers’’. Companies and institutions—the ‘‘seek-
ers’’—post their assignments, and the ‘‘solvers’’ select those that are of interest to
them based on the discipline in which they work.
Registration, Profile and CV
Anybody can register as a ‘‘solver’’, even anonymously, if they chose, but a valid
email address is required. The profile, which is open to the public, can either be
left blank or provide substantial personal information. It is possible to name one’s
own fields of expertise and interest. There is no strict format for the CV; academic
degrees and a list of publications may be mentioned. External links, to one’s own
website, for example, or to a social network profile can be added. The ‘‘solver’’ can
decide whether he or she wants to make his or her former participations in chal-
lenges public. ‘‘Solvers’’ can easily be classified on the basis of this information:
the ‘‘seeker’’ obtains an immediate impression of how many ‘‘solvers’’ might have
the potential to contribute a solution for the given task. It is also possible to recruit
new ‘‘solvers’’. In this case the ‘‘seeker’’ can observe the activities of the ‘‘solvers’’
involved (under ‘‘referrals’’). If a successful solution is submitted, the ‘‘solver’’
who recruited his colleague who actually solved the problem gets a premium. The
recruitment takes place via a direct linkage to the challenge. This link can be
published on an external website or in a social network. The ‘‘InnoCentive Any-
where’’ App creates an opportunity for the ‘‘solver’’ to keep up-to-date with regard
to forthcoming challenges.
262 T. Schildhauer and H. Voss
Innoget (www.innoget.com)
Innoget focuses on linking up organizations that generate innovations with those
that are in search of innovation, both having equal rights. Research institutions in
particular are addressed as potential solution providers. Solution providers can put
forward their proposals for a solution in return for ideas on creating a surplus
which might be valuable to them. The basic difference between this and Inno-
centive.com is that, while Innocentive builds up a community—quite detailed
profile information including expertise, subjects of interest and (scientific) back-
ground are called for—which makes it possible to form teams to find solutions for
a challenge, Innoget.com merely asks for a minimum set of profile questions. It
also has to be mentioned that offering solutions/technologies has to be paid for, so
Innoget.com seems to be less interesting to individuals than to companies. Par-
ticipating in and posing challenges is free of charge, although other options have to
be remunerated: providing a company profile and starting an anonymous chal-
lenge, for instance. Offers and requests are both furnished with tags detailing the
branch and knowledge field. You can also look for partners via the detailed search
provided. You can choose categories like countries, nature of challenge (such as a
collaboration, licensing or a research contract) or organization/institution (major
enterprise, public sector, university etc.).
SolutionXchange (www.solutionxchange.com)
SolutionXchange.com was founded by Genpact, an Indian provider of process and
technology services that operates worldwide. A distinctive feature is that only
Genpact-members and customers can submit challenges to the platform.
As mentioned in the previous examples, registration is easy and free to all
experts and users. After naming the most basic information, such as primary
domain and industry, it is possible to add detailed information on one’s profile:
education, career or social network links etc., but you can opt to dispense with that.
As soon as you have registered, you can upload white papers or your own articles
or write blog entries that you wish to be discussed. You can also join and found
communities. At SolutionXchange.com you can also request to join the network as
a pre-existing group or organization.
Ongoing challenges are initially listed according to your profile information;
after using filter options, different challenges will be displayed on your profile page
as well. Of special interest is the area called ‘‘IdeaXchange’’, where all the different
communities, articles and blogs appear and—independent of the actual chal-
lenges—external experts meet Genpact members for discussion. Submitted articles
are evaluated by Genpact experts or by the companies that set up the challenge. At
the end of the challenge, Genpact has the opportunity to assign individual experts
with the task of evolving an idea that was remunerated, depending on the impor-
tance or size of the project or the time spent on development.
Open Innovation and Crowdsourcing in the Sciences 263
One Billion Minds (www.onebillionminds.com)
This platform calls itself ‘‘Human Innovation Network’’; ‘‘openness’’ according to
the open innovation principle is highlighted in particular.
Registration is simple, detailed information on one’s profile cannot be named,
but you can type in links to your profile located on an external social network.
Inviting contributions towards a given challenge is simple, too: after providing
information on the challenger (whoever set up the challenge: company, non-profit
organization, individual as well as the origin) only the challenge’s title, a further
description of it and a public LinkedIN-profile are required. Afterwards the
challenger has to describe the aim of the challenge, possible forms of collaboration
between initiator/challenger and participant, a deadline and a description of the
rewards (not necessarily monetary rewards). It is also possible to pay for and
charge Onebillionminds.com with moderating the submitted answers. At Onebil-
lionminds.com social and scientific projects have equal rights to those that have an
economical aim. The declared intention of Onebillionminds.com is ‘‘to change the
world’’.13
Presans (www.presans.com)
Presans.com follows a very different approach from those mentioned above: The
company provides a search engine that—according to the platform’s own
description—browses through the whole data base that contains about one million
experts looking for matching partners. Here Presans.com keeps a low profile when
it comes to selection criteria: Nevertheless, it seems that publications and the
assurance of the expert’s availability are of value. This issue is based on the
assumption that the solution-seeking company often does not know what kind of
solution the company is actually looking for.
Inpama (www.inpama.com)
Inpama.com enables inventors and scientists to present their solutions ready for
licensing and accordingly ready to be put on the market. Inpama.com was founded
by InventorHaus, a company that runs several inventors’ businesses and inventor
or patent platforms in the German-speaking countries. Companies that are looking
for solutions can receive information on offers that fit their own business area, so
they can easily get in touch with inventors. Impama.com offers inventors various
possibilities via different media such as image, text, video or web links to describe
13 One Billion Minds: http://www.onebillionminds.com/start/.
264 T. Schildhauer and H. Voss
their products and patents. Tags mark the product’s category. Also, Inpama.com
offers further information and assistance with improving the product or invention
by contacting test consumers or industrial property agents or by marketing the
invention.
Marblar (www.marblar.com)
Marblar.com adopts a very interesting approach. Assuming that a minimum of
95 % of the inventions that have been developed at universities are never realized,
scientists are given a chance to present their inventions to potential financiers and
to the public. The special aspect in this instance is for the presented inventions to
be developed further through gamification: Players can earn points and can receive
monetary rewards up to £10,000. In this way, Marblar.com helps to make
inventions useful that would have been filed away. Marblar.com also helps to open
up a further source of income to the research institutions.
A Landscape of Today’s Platforms: Summary
The crowdsourcing platforms often consist of creatives that participate either by
submitting active content, such as contributions to a challenge, or by judging
contributions to challenges. Referring to the platform Innocentive.com, where
more than 200,000 ‘‘problem solvers’’ are registered, it is obvious that not every
single member submits input for every single challenge. Due to the size of the
group, however, there is a very great likelihood that a lot of valuable ideas will be
put forward. Technically speaking, it is possible that every participant could take
part in every challenge, but this is not true of all the platforms mentioned here.
Nevertheless, Innocentive.com provides an opportunity of thinking ‘‘outside the
box’’ because it allows the participants to name not only their own field of
knowledge but also their field of personal interest. It is accordingly possible to
generate new ways of solving complex problems. It seems obvious that either
communication and interaction within a community or the building of new com-
munities is of interest: the submitted ideas are frequently hidden. It is possible that
lots of companies are not as ‘‘open’’ as open innovation suggests, but perhaps
concealing ideas is not that absurd after all: these open innovation challenges do
not interact with the masses like many other common crowdsourcing projects do.
Many crowdsourcing platforms that work in the field of open innovation provide
an opportunity for organizing challenges for closed groups or pre-existing com-
munities whose members are either invited, belong to the company or other
associated communities that are then sworn to secrecy.
As shown above, scientists can choose between diverse platforms if they want
to participate in open innovation. Usually you can register with detailed profiles. In
Open Innovation and Crowdsourcing in the Sciences 265
most cases you can communicate with different experts within your fields of
knowledge or interest. In this way, there are other advantages apart from the
financial benefits in the event of winning a challenge: insights into a company’s
internal problems are given that might also fit in with a scientist’s own range of
duties.
It is apparent that there are a great many inspiring options and processes to be
gained from the variety of platforms, which may play a substantial role in fostering
the integration of and collaboration between scientists.
It will be exciting to watch and see how Marblar’s gamification principle
develops and to find out if financial incentives are to remain the most important
aspect of open innovation. But how do individuals, companies, scientists and
experts respond to platforms like Inpama.com or Presans.com: will challengers/
inititators prefer to submit challenges to platforms like Innocentive.com or do they
accept the possibility of search engines browsing through the data of submitted
technologies?
The legal aspect remains of considerable importance: Innocentive.com outlines
the point of the challenge fairly clearly when intellectual property changes own-
ership. But still, details of that have to be discussed between the ‘‘solver’’ and the
‘‘seeker’’.
Risks in Crowdsourcing Science
It is clear that different risks have to be taken into account when using or applying
the methods of crowdsourcing or open innovation. The crowd is not invincible,
although often praised for clustered intelligence. A high number of participants
does not guarantee the finding of an ideal solution.
But who actually makes the final decision about the quality of the contributions
in the end? Does it make sense to integrate laymen, customers or individuals and
researchers who work outside the problem field into these kind of creative pro-
cesses in any case?
A detailed and unambiguous briefing by the platform host seems to be called
for. This also applies to participants: ideas need to be short and formulated clearly
in order to survive the evaluation process. It is one thing for a company to ask its
target group about marketing problems but quite another to ask the target audi-
ence—where only a few are professionals in the problem field—to solve difficult
and complex (research) questions. Nevertheless, human tasks of different but less
complexity can be carried out by anonymous carriers, although the limits are still
reached fairly quickly.
Another important factor is dealing with the high number of submitted ideas
and proposed solutions. Who should and might actually be able to evaluate them
all? How does one avoid overlooking the best ideas? Often crowdsourcing also
means that the participants themselves evaluate their proposals—in this way a
form of pre-selection takes place and points out ideas that should attract interest.
266 T. Schildhauer and H. Voss
Nonetheless, manipulation may occur when networks or communities fuse to
support certain ideas because they aim to win the prize, inflict damage on the
initiator or for any other reasons. You can overcome this risk by backing up the
crowdsourcing process with a jury of experts who monitor the process and can
intervene and provide a final evaluation, where required.
There is always the risk of a participant losing his or her idea without getting
paid for it. This might even happen without the initiator’s appearance: it is possible
that other participants or external viewers steal the idea. In this context we have to
mention that it is frequently only the winner who gets rewarded for his or her
input—all the other participants miss out although they might have contributed
several days of dedicated effort for the purpose of the challenge.
For contestants, crowdsourcing and open innovation are a very debatable source
of income. The safeguarding of a contestant’s property is left to his or her own
resources. Researchers should therefore check the agreement drawn up between
them and the platform host to see whether intellectual property is covered prop-
erly. This can be ensured by documenting timestamps and with the help of a good
community management through the platform host.
Of interest to both parties is the delicate question of profit participation: what
happens if the participant receives 1,000 Euros for his or her idea but the company
makes millions out of this idea? The question remains why a researcher or other
collaborator should join in without any certainty that his or her contribution at any
stage of the research/creation process is of value, whether it will be appreciated
and adequately remunerated.
So, the initiator and participant have to take a careful look at legal protection.
Actually, this can prove to be quite easy: The initiator can set up the rules and the
participant can decide, after having studied the rules, whether he or she agrees and
wants to participate or not. A situation under constraint, such as financial
dependence, is often rejected a priori. Assigning intermediaries can often serve to
adjust a mismatch. The more specialized the task, the more weight rests with the
expert or ‘‘solver’’. Internet communication enables the solver to draw attention to
unfair conditions: Both platform host and initiator are interested in retaining a
good reputation; any possible future participants might be discouraged. Much
depends on the question of how the Internet can be used to connect B2C or B2B
companies, scientists or research institutions to target groups and how to receive
desired opinions and ideas; but also how to integrate scientists in particular? How
to ensure and achieve valid results? What legal, communicative, social factors
need to be contemplated? And what further steps are there to mind after a
crowdsourcing process? It would be unflattering if customers, scientists and cre-
atives were motivated to participate but, after that, nothing happened or they
weren’t mentioned in reports on the crowdsourcing process. That would only cause
or aggravate frustration and that is something nobody needs.
Open Innovation and Crowdsourcing in the Sciences 267
Outlook: What the Future Might Bring
A possible future route for development might be hyper-specialization: ‘‘Breaking
work previously done by one person into more specialized pieces done by several
people’’ (Malone et al. 2011). This means that assignments that are normally
carried out by one or only a few people can be divided into small sections by
accessing a huge ‘‘crowd’’ of experts including researchers from all knowledge
fields and task areas.
There is usually an inadequate number of hyper-specialists working for a
company. Provided the transition between different steps of a working process is
smooth, the final result might be of a higher quality. Here the following question
arises: at what point would ‘‘stupid tasks’’ start to replace the highly specialized
work, i.e. where experts are reduced to the level of a mental worker on the
assembly line?
One possible result of open innovation’s success might also be the decline or
disappearance of research and development departments or whole companies: to
begin with, staff members would only be recruited from the crowd and only then
for work on individual projects.
Another outcome might also be that the social media principle of user generated
content could be transferred to the area of media products: normal companies
could be made redundant if the crowd develops products via crowdsourcing and
produces and finances them via crowdfunding.
A further conceivable consequence might be the global spread of the patent
right’s reform: new solutions might not get off the ground if they violated still
existing patents. The more the crowd prospers in creating inventions, develop-
ments and ideas, the more difficult is becomes to keep track and avoid duplicates in
inventions. This raises the question of authorship: what happens to authorship if
the submitted idea was created by modifying ideas put forward by the crowd’s
comments? When does the individual become obsolete so that only the collective
prevails? Do we begin to recognize the emergence of a future model where
individuals are grouped and rearranged according to the ideas being shared and the
resulting benefits? And to what extent will the open source trend increase or find
its own level?
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
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Open Innovation and Crowdsourcing in the Sciences 269
The Social Factor of Open Science
Tobias Fries
Abstract Increasing visibility in the Internet is a key success factor for all
stakeholders in the online world. Sky rocketing online marketing spending of
companies as well as increasing personal resources in systematic ‘‘self-marketing’’
of private people are a consequence of this. Similar holds true for the science and
knowledge creation world—here, visibility is also a key success factor and we are
currently witnessing the systematic exploitation of online marketing channels by
scientists and research institutes. A theoretical base for this novel interest in sci-
ence marketing is herein provided by transferring concepts from the non-science
online marketing world to the special situation of science marketing. The article
gives hints towards most promising, practical approaches. The theoretical base is
derived from considerations in the field of scale-free networks in which quality is
not necessarily a predominant success factor, but the connectivity.
Introduction
New aspects of Web 2.0, together with those that are already familiar, are about to
completely revolutionize the world of academic publishing. The gradual removal
of access barriers to publications—like logins or unavailability in libraries—will
increase the transparency and accordingly the use of Web 2.0 elements. We can
envisage evaluation and suggestion systems for literature based on such factors as
relevance and reputation along similar lines to search engines. Conversely, while it
is conceivable that networking systems and search engine technology will
consequently become increasingly prone to manipulation, this will at the same
time be preventable up to a certain point.
T. Fries (&)
Witten/Herdecke University, Witten, Germany
e-mail: Tobias.Fries@gmx.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_18,  The Author(s) 2014
271
The Transition from Science to Open Science
Admittedly some way behind the consumer sector, the field of science is now
beginning to grow accustomed to the Internet. Certain ingrained dogmas that pre-
viously stood in the way, like a general sense of apprehension on the grounds of it
being ‘‘unscientific’’ are being cast aside—slowly but surely—to reveal the inno-
vative concepts, even though this is proving to be a slightly hesitant process. Just how
hesitant becomes clear when we take a look at the use of Wikipedia, by way of an
example. Wikipedia is admittedly not a primary source of information and calls for
considerable caution when quoting, but this doesn’t make it apply any less to the
seemingly objective conventional science. This is not meant as a disparaging remark
or an insult to the significance of scientific relevance but merely serves to point out
comparable dangers. We cannot judge the objectivity of unofficial editors who
contribute to the Social Internet on a voluntary basis any more than we can assess the
source of funds used to sponsor an academic study. It is in any event wise to exercise
basic level of caution when employing it for any objective purpose.
Anyone—including scientists—who acquainted himself/herself with the new
media early on is already at a great advantage, even now. The earliest pioneers were
able to send the treatises to and from much more frequently via email than with the
traditional postal system, and this in turn considerably shortened editing cycles, and
whoever dared to blow caution to the wind and post his/her text on the Internet,
despite any fears of data theft, was rewarded with tangibly higher citation rates. The
reasons for this are intuitively plausible to anyone who has ever carried out research
work him- or herself: we only quote what we find. No matter how brilliant an
unavailable text may be, if it is unknown, nobody will cite it. This conclusion can be
drawn by taking into account the work of de Solla Price (1976), who analyzed
Cumulative Advantage Processes on the example of paper citations. By showing
that the Science Citation Index only used to consider 1573 sources out of 26000
journals of interest, he calculated a possible total reach of 72 % by having access to
only the first 6 % of journals. He also found out that a longer presence in the archives
increased citation rates as a result of higher potential availability. To transform de
Solla Prices findings into other words, it is not the content alone that leads to its
subsequent utilization in academic circles but also the extent of its reach. This
correlation can also be expressed in the form of an citation conversion equation, by
dividing the number of quotes (k) by the number of times a text is read (n):
kCit= nRead ¼ C ð1Þ
Factor C is used here to denote the citation conversion rate, a coefficient already
familiar from conventional web analysis. The conversion rate refers to the ratio
between the number of orders placed with online shops and the overall amount of
web traffic or the number of baskets/shopping carts filled without proceeding to the
check-out. It serves as an indication of the quality of the website which, by
deduction, can also provide a straightforward analogy to the quality of the aca-
demic publication. Although the concept of the quality of academic publications
272 T. Fries
has been around for decades, a reliable evaluation has yet to be realized, because
no-one so far has managed to track the frequency with which they are read. State-
of-the-art performance assessment of academic articles is largely restricted to the
quantitative number of citations and, very occasionally, the publication location
and the circulation of the journals and books can, with considerable limitations,
provide some clues as to the distribution. Acceptance for publication is, however,
much more subjective than academia would like to admit. Publication frequently
depends on personal contacts, political considerations or the scientific expertise of
small editing committees, who may not necessarily fully recognize the significance
of a treatise that is indeed new from an academic point of view.
By contrast, academics who post their publications on the Internet in an open,
search-friendly format, stand a better chance of being quoted which, given the
specific rationale of de Solla Price findings, consequently creates a self-perpetu-
ating effect with every additional citation. Anyone who can be accessed is abso-
lutely bound to be read more frequently, and even where the quality is inferior, may
well be quoted less often (in relative terms) but more frequently from the point of
view of the absolutely greater number of readers. Since the absolute citation rate in
the current status quo is one of the key indicators of quality, a citation volume
derived from the high statistical figures leads to a real perception of quality, pos-
sibly even when there are better articles on the same topic.
In their own interests, anyone who has grasped this concept is hardly likely to
hide the treatises they have written behind the log-ins of a personal homepage, for
which there may be a charge, or the internal domains of publishing companies’
websites. Academic publications are not a mass product, and anyone who wants to
earn money on the basis of the print run would be better off with books dealing
with sexuality, entertainment or automotive topics, as these subjects regularly
attain a publication run of a million copies or more. In so far as academics are ever
involved in their field of interest for money, this is earned indirectly through
lectures, consultation fees or application products derived from scientific research,
to which the publication itself only contributes the legitimizing reputation.
Browsing the Internet with the help of Google Scholar, Google Books or any other
search engine while restricting one’s search to documents with the ending.pdf will
nowadays turn up a large number of academic publications whose full text is
extremely specific and its perusal accordingly highly productive for one’s own
publications. Some publishers and authors even go as far as placing long passages
of their books verbatim on online book portals like Amazon, specifically for search
purposes, which a good many academics employ for the inclusion of such books
that might not otherwise have been selected for citation. There is undoubtedly a
conflict of goals and it is proving to be a problem for quite a number of researchers
today: the more pages of a book or publication are read in a public domain, the
fewer copies are going to be sold. If we regard the turnover achieved with the
product and the number of book sales as a yardstick for measuring success,
then this viewpoint is justified. If we go one step further, however, to the level of
reach or prominence they gain, the number of sales is of absolutely secondary
importance.
The Social Factor of Open Science 273
Barabási/Albert provide a next level of scientific explanation for this correlation
with their analysis of networks, which can be regarded as the indisputable standard
in network research—at least in terms of the number of recorded citations (cf.
Barabási and Albert 1999). At the time of writing this article, Barabási had been
quoted by more than 12,000 other scientists, according to Google Scholar. The
content of the article deals with the clustering behavior of nodes in scale-free
networks. This designation refers to graphs displaying structures that resemble their
own but on a different scale: in other words, their structures look similar when
enlarged or decreased in size. Another feature of scale-free networks is an expo-
nential function in the number of clusters. Conventional random networks typically
display a bell-shaped curve in their distribution function, according to which most
nodes have a similar number of links and, in the boundary areas of the bell-shaped
curve, tend to be those that deviate from this mean (cf. Erd}os and Rényi 1960).
Examples of conventional random networks include transport or electricity net-
works, as depicted in Figs. 1 and 2 for the European high-speed railway network.
During their investigation, Barabási and Albert (1999) looked at Internet links
and initially assumed a random network of the kind introduced by Erdös and
Rényi (1960). They were, however, surprised to discover an exponential distri-
bution function rather than a bell-shaped curve. This contained a small number of
websites (nodes) which were linked to an extremely large number of other
Fig. 1 The high-speed railway network in Europe. It represents a random network as proposed
by Erd}os and Rényi. (Source and copyright of the diagram Akwa and Bernese media and BIL
under a creative commons licence)
274 T. Fries
websites and an extremely large number of websites to which just a very few other
sites pointed. Taking their research further, they came across a probability mass
function in other scale-free networks, with the help of which it was easier to find
well-attached nodes, so they were linked up more often. They called this phe-
nomenon ‘‘Preferential Attachment’’ and succeeded in proving that clustering
dynamics of this kind give rise to ever-increasing imbalances in the network
system over a longer period of time, so that well-attached nodes accumulate
further links whereas less well-attached nodes attract even fewer new links.
Barabási later coined the very apt designation ‘‘The Rich get Richer Phenomenon’’
to describe this effect (cf. Barabási and Bonabeau 2003, p. 64f).
Applying this investigation to science, it confirms the hypothesis proposed at
the beginning of this treatise that reach, rather than quality, can lead to truth—
particularly in the case of low visibility and accordingly less likelihood of the
better essay being linked. Both de Solla Price and Barabasi/Albert identified
exponential distribution functions in scientific citation, one called the phenomenon
‘‘Cumulative Advantage Processes’’, the other one ‘‘Preferential Attachment’’.
There are nevertheless far-reaching discussions currently in progress in academic
circles regarding free access to publications, intellectual property rights and the
supposed protection of scientific independence. The conflicting goals of scientists
and publishers might be interpreted as the reason behind these discussions. Scientific
advancement does not necessarily have to be the main priority of the publishing
companies, seeing as they are financing an infrastructure and pay its owners divi-
dends. They themselves do not have a share in the indirect income raised through the
reputation of the scientist concerned, so the publishers’ main interest must lie in the
Fig. 2 The frequency distribution of the links between the nodes of the European high-speed
railway network. The density function superimposed on the dots illustrates the underlying
Poisson distribution
The Social Factor of Open Science 275
direct generation of revenue, while intellectual property rights and limited access
safeguard their influence. By contrast, if we take a closer look, the researcher has an
interest in barrier-free accessibility and is therefore trying to break free of the pub-
lishers’ sphere of influence, although they give him the benefit of the Open Science
network. The fact that discussions of this nature are already in progress could be
construed as friction due to a change of paradigms, because science could become
more objective, more transparent and consequently more democratic, offering
impartial benefits to researchers and research outcomes alike, if it were organized
properly and supported by the rigorous use of certain aspects of the social web. To
ensure that publishers don’t come away empty-handed from a turn-around of this
kind, they would be well advised to grasp the mechanisms behind this trend—the
sooner, the better—and help to shape the framework in which science will be
operating in the future through timely investments in the infrastructure for this
Science Web 2.0. The fact that those who get noticed early on benefit from long-term
reach applies to publishers as well, because a publishing company’s reputation—and
accordingly being published within its infrastructure—already attracts the better
scientists; an innovative form of publication will make no difference to the funda-
mental research results achieved by de Solla Price and Barabási/Albert. The early
bird catches the worm, regardless of whether that bird is a publisher or a researcher.
The Importance of Network Science
for Research Relevance
There are only a few research results that can be described as ‘‘objectively good’’.
It is usually those publications about which enough other people speak favorably
in terms of that particular topic that are perceived as being ‘‘good’’. The few
contributions to research that are objectively and indisputably good receive
favorable feedback anyway, due to the excellent quality of their work, provided
they attract a sufficiently wide audience. More often than not, however, work that
is merely mediocre receives positive feedback because its stands out from the rest
or because the author already enjoys a good reputation. The most obvious forms of
favorable feedback in academic circles are citations and references. And the higher
the number, the greater the likelihood of being quoted again in the future, since it
is easier to find a frequently cited article than one that has been cited less often. So
the ‘‘Rich get Richer’’ phenomenon applies to researchers, too. On a practical
level, this explains why it is much easier for a professor who has been publishing
articles for many years to get his next paper published in an acclaimed journal than
a young person, even though he might be the more brilliant of the two, who is
trying to make his debut with an outstanding first work. At the same time it
becomes clear why it may be of an advantage for the brilliant young individual to
ask the professor for his support in publishing his first article, as this would allow
the young author to establish his first links via the science network while the older
research might profit a little from the spillover effect of the excellent debut article.
276 T. Fries
Seen as a whole, this section serves to explain why publishing scientists have an
interest in being quoted as frequently as possible. It is equally conceivable that
there are sufficient reasons for manipulating citations, always with the aim of being
cited more often by others. A number of scientists managed to solve this problem
in the past by means of citation cartels, and in case this is controversial, let it be
said that it definitely applies to some operators of websites at least, because it
constituted a problem for search machines for a long time.
The following section addresses feasible aspects of the Social Internet for Open
Science, always against the backdrop of possible manipulation and the ensuing
consequences in practice. This list, like the whole book, is an incomplete preview
of a topic that is still under development but will revolutionize science.
Aspects of the Social Web in Open Science
Users particularly welcome innovations when they hold the promise of an
advantage of some kind. Individual features embedded in otherwise linear plat-
forms will be just as unsuccessful as those that regularly fail nowadays in con-
sumer applications. The intelligent consolidation of information to create an
outcome that is of broad use to all stakeholders involved will assert itself unless an
inferior, but high-reach solution achieves exclusive prominence in the eyes of the
users. For this reason, the aspects set out below can only realize a fraction of their
collective, self-multiplying effect. They are nevertheless being included individ-
ually for the sake of maintaining the linear structure of the text.
The Basic Principles of Search Engines: Relevance
and Reputation
The purpose of search engines is to present results of relevant Internet searches,
arranged according to their reputation. Relevance is assessed by subjecting the
available contents and the contents of other referring websites to a special quan-
tification process, while the reputation is determined from a wide range of other
aspects, beginning with the number of referring websites and including users’
appraisals and the surfing patterns of people visiting the websites. Due to the
widespread manipulation of search results, search engines have long since moved
away from metrics based solely on the number of inbound links—a practice which,
despite similar manipulation incentives, has not yet caught on in the field of
science, where the number of citations still prevails as the standard benchmark.
The operators of Open Science platforms accordingly have a similar responsi-
bility to that of search engines. In present-day research, scientists already have to
select from the literature pertaining to their specific sphere of research. Purely
determining the topical relevance is the simplest task; assessing the technical rele-
vance is much more difficult. In cases of uncertainty or other causes of hesitation, the
The Social Factor of Open Science 277
researcher will also take the reputation of a scientist into account when contem-
plating who to quote. Each and every piece of research is restricted by the subjective
field of vision of that which the scientist finds. Publications that escape his or her
notice due to a language or access barrier will not be included in the shortlist.
The challenge in setting up Open Science is to achieve as comprehensive a
selection of scientific texts as possible with the lowest possible access barrier, to
enable publishing scientists to obtain the desired level of relevance and reputation
with the help of publication platforms. Merely widening the field of vision for
researchers is the easiest task: increasing objectivity and transparency in the
assessment of reputation will prove to be a challenge. Bearing the evolution of
search engines in mind, we anticipate a highly dynamic advancement and per-
manent alignment of the algorithms employed.
Identification of the Protagonists
The fact that there are sometimes two or more scientists with the same name can
be misleading, but this is not so serious that it renders research impossible,
although common names do make it more difficult to identify which researcher is
meant, especially when they are both active in the same field. With the Open
Science Web approach, every scientist can be allocated an unambiguous profile,
complete with photo, a brief CV, main research focus and, in particular, a specific
ID number. Existing Science Communities like ResearchGate,1 which already
provide a representative picture, seem to be particularly suitable. Profiles with an
open platform identification number enable an integrated use of Open Science
features, as introduced below. The open-platform architecture is of particular
importance. A platform such as ResearchGate, for instance, will gain a strategic
lead as far as reach is concerned, along similar principles to those presented above,
if it makes its own researcher ID available on the Internet for other academic
purposes free of charge and without any barriers. The permanent core reference to
this platform will lead every researcher back to the original community—a long-
term benefit in terms of influence and reputation cannot actually be foreseen at the
moment, due to the growing number of members, but it is highly probable when
we consider the dynamic evolution of scale-free networks.
Ascertaining a Quality Factor from Likes, Dislikes,
Assessments and Comments
One of the most straightforward uses of the Social Web 2.0 for Open Science is
the ability to transfer positive and negative ratings and comments. These features
1 www.researchgate.com.
278 T. Fries
are not new by any means, the most prominent among them being those
employed by Facebook, but even there they were not new, owing to their
simplicity. Blogs, online book marketplaces and bidding platforms recognized
the principle of assessment for boosting one’s reputation at a much earlier stage
and used it to their own advantage. It can basically be divided into simple
expressions of approval or disapproval (like or dislike), an interesting aspect
being that Facebook only allows the affirmative ‘‘like’’ vote which, judging by
the demographic structure of its users, may be of inestimable value in protecting
the psychological development of school-children/minors, seeing as countless
cases of cybermobbing have been heard of even when the voting is limited to
favorable ‘‘thumbs up’’ ratings. Although science ought to be regarded as
objective and rational, it would be wrong to underestimate the interests that lie
behind research results, and which might play a role in influencing the assess-
ment of publications beyond the limits of objectiveness. Only a process of
experimentation and subsequent evaluation can determine whether the accumu-
lation of negative votes leads to an objective improvement in assessment or
encourages the intentional underrating of undesirable research results. In the
interests of transparency, however, it would probably make sense to show fea-
tures of this kind with a clear, publicly visible reference to the originator. In this
way, likes, dislikes, assessments and comments would reflect straight back on the
reputation of the person passing the criticism and would consequently be better
thought-out than anonymous comments. This contrasts starkly with the fear of
uncomfortable, but justified truths which are more easily expressed anony-
mously. It might be possible to experiment with both forms in order to ascertain
a quantified quality factor that would also be taken into consideration in eval-
uating the reputation of an article or researcher.
Crowd Editing
The possibility of crowd editing is a completely new, feasible feature in the
Open Science web. Strictly speaking, it amounts to the steadfast further devel-
opment of joint publications. While the ideal number of academics working on a
treatise is limited to two, three or occasionally four scientists, crowd editing
opens up a publication on a general level. As introduced earlier on in this book
(see chapter Dynamic Publication Formats and Collaborative Authoring), anyone
reading the article can contribute voluntarily to it provided he/she has something
relevant to add. Old versions can remain stored in archives, as is the case with
Wikipedia articles, and subject-related changes can be either approved or
rejected by a group of editors before an official new version of the article is
published. It is conceivable that the relevant subversion could be cited—not
really a new procedure—but the principle of crowd editing might increase the
frequency of amendments.
The Social Factor of Open Science 279
Suggestion Systems for Articles During the Writing Process
The potentiality of suggestion systems is, however, really new. Whereas authors
today actively look for literary sources in conventional and digital libraries,
innovative technologies enable smart suggestion systems. The insertion of context-
based Internet advertising is a long-established practice, whilst its academic
counterpart is still in its infancy. Only Google, in its capacity as trailblazer of
search-engine technology, already proposes search-related topics and authors, thus
paving the way for the intelligent linking of academics and their publications.
It starts to become exciting when suggestions for potentially interesting, sub-
ject-related articles are put forward during the actual writing process. This might to
a certain extent release researchers from the somewhat less intellectual task of
merely compiling information while simultaneously providing them with addi-
tional sources, which they might not have found so easily on their own, since they
are only indirectly linked to the topic in question via another association, for
instance. Special attention should be paid, when developing the relevant tech-
nologies, however, to the selection algorithm, which harbors the risk of tempting
the researcher into a convenience trap. The mental blanking out of other sources
might represent one aspect of a trap of this kind—a phenomenon that is likewise
rooted in the network theory. In this case, the sources that attract most attention are
those that are closest to the interests of the researcher in question and are already
most visible (cf. Barabási and Albert 1999). The predefined ranking of pop-up
results is another hazard. There are countless analyses of the recorded click rate for
search results using the Google search engine. Various analysis in Google Ana-
lytics reports conducted over several years have repeatedly provided a similar
picture—about 80 % of all clicks landed on the first five search results that
appeared on the screen, 18 % on the remaining ones on the first page and only 2 %
on the second page. This data has been retrieved by comparing search statistics
with click statistics. Due to their previous experience and working routines, one
can assume that academics conduct their research more thoroughly than general
consumers. Nevertheless, such attributes as convenience and circumstances like
being in a hurry are only human and also apply to a certain extent to researchers,
which bodes quite well for the first secondary sources in the list, at least.
Against this backdrop it emerges what a high priority status the algorithm will
have with regard to the presentation of suitable secondary literature. Due to the
great resemblance in structure, we assume that this feature will operate along much
the same lines as search engines, so it is likely to face similar challenges and
problems. We will revert to this topic further down, in the section dealing with the
presentation of results.
Once these technical problems have been solved satisfactorily, we can envisage
a completely new form of academic writing, along the lines of the example out-
lined briefly below:
280 T. Fries
Example of Academic Writing
A researcher has an idea for an article and already possesses some previous knowledge of
the subject-matter, which allows him to put his idea into words straight away. So, using a
web application designed specifically for academic writing, he begins to type his idea into
the space provided. Since he is logged in, the platform is not only able to create direct
references to his previous work and topics processed on the platform but can also read his
current input and compare it with texts contributed by other scientists. While he is writing,
the researcher can now view context-related excerpts on the screen next to his own text,
which might be of interest for the passage he is writing. Other, more general articles
dealing with the subject concerned, which might be of relevance to this treatise, appear
elsewhere. Based on the topics and contributions evaluated on the platform, the researcher
in this particular example also receives suggestions as to which other scientists he should
contact for the purpose of exchanging information and views.
This case illustrates a scenario of higher transparency on several levels. Besides
those relating to texts, the researcher also receives suggestions relating to people
who might prove to be an interesting point of contact. This might conceivably be
extended to announcements for specialist conferences, other relevant events or
items that match the theme.
Parallels to Search Engines
Expressed in simplified terms, search engines consist of a crawler and an indexer.
The crawler visits websites, records the contents and proceeds to the next website
via the links provided. There is also a so-called scheduler designed to determine
the order in which the next sites will be visited by arranging the links according to
priority. The indexer orders the recorded contents and allots them priority for
displaying in the lists of results. The precise technical principle is of secondary
importance in this context, but a general outline of the analogical derivation
process is no doubt useful. To sum up, a search engine arranges results according
to their relevance and reputation. It is this principle that will be crucial for a
suggestion system in Open Science, too; other technical features that will also be
essential for a suggestion system include a crawler, an indexer and a scheduler,
thus displaying numerous parallels between search engines and social science in
the Open Web.
The background is that, even where the content is of equal relevance, one or
more additional coefficients are needed to determine which results appear at the
top of the list. These might be such dimensions as frequency of citation, the
number of favorable comments and maybe even comments posted by other highly
rated scientists. These other dimensions may be varied and, in the interests of
maintaining a high standard of output, subject to dynamic change. This is due to
the high probability of leading, and implicitly more relevant, search results being
used more often for quoting, so scientists strive to optimize their own input.
The Social Factor of Open Science 281
Similar Problems to Those of Search Engines
The history of search engines is dotted with attempts to influence this process—
initially through the frequent repetition of keywords taken from the body of the
text. Since this was easy for the author himself to manipulate, the quality of an
assessment based primarily on this factor was fairly meaningless. For this reason,
external criteria such as the number of links from other websites were added, but
they were also easily influenced by means of self-developed networks. We have
observed a kind of cat and mouse game between search engines and so-called
search engine optimizers over the past 15 years. These SEOs began by inserting a
large number of keywords on their websites, which led to the search engines
introducing a kind of maximum quota. Everything over and above that quota was
classified as spam and greater importance was ascribed to the number of incoming
links. So the SEOs began devising their own website structures that pointed to the
target sites to be optimized. Search engines consequently began to evaluate the
number of different IP numbers as well, so the SEOs retaliated by setting up
different servers, whose sites highlighted the target sites in the shape of a star or a
circle. And we could add many more examples to his list. Similar developments
are to be expected in the scientific sphere, particularly as the setting up of citation
networks is nothing unusual even in traditional academia. What does need to be
solved is the problem of avoiding cartels of this kind and it is essential that we
learn as much as possible from past experience with search engine optimization.
Similar Solutions to Those of Search Engines
Solutions in the field of search engine technology are increasingly permeating the
domain of network science. Analyzing typical and atypical linkages has now
advanced so far that it can determine with reasonable probability whether a more
or less naturally evolved linking network is behind a certain website or whether
there are numerous links bred on search engine optimizers’ own farms. The
solution is not yet complete but the number of very crude manipulations has
receded noticeably during the past few years, as Google and other search engines
were evaluating search engine positions for those detected. Similar occurrences are
to be anticipated in the academic sphere of Open Science. In such areas where
network references are unmistakably concentrated in denser clusters than the
extent of the subject-matter would normally justify, an algorithm will be employed
to reduce the reputation factor to a natural size. Search engines meanwhile go one
step further and remove excessively optimized sites completely from the index, a
move that can only be reversed by dismantling the linkage cartel or stopping the
manipulations. Whilst the hitherto anonymously functioning search engines are
only just beginning to identify users in the registered domains and to incorporate
their search and surf patterns in the reputation assessment process, this has been
282 T. Fries
common practice in the publication of scientific treatises on the social web right
from the start due to the clear authentication system described above. This has the
added advantage of being able to include commenting and rating behavior, and
possibly even the amount of time spent on a page of a treatise, in the reputation
assessment of an article. It is not possible to forecast the entire range of potential
manipulations as yet, and a certain amount of reciprocal technological upgrading is
also to be anticipated in academic circles—in the interests of unbiased, relevant
results on the one hand and motivated by a desire for upfront placements, which
hold the promise of additional citations, on the other.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
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286(5439), 509–512.
Barabási, A.-L., & Bonabeau, E. (2003). Scale free networks. Scientific American 60–69.
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de Solla Price, D. (1976). A general theory of bibliometric and other cumulative advantage
processes. Journal of the American Society for Information Science, 27(5–6), 292–306
The Social Factor of Open Science 283
Part IV
Cases, Recipes and How-Tos
Creative Commons Licences
Sascha Friesike
Abstract Licences are a topic many researchers shy away from. And it is com-
mon behavior that property rights are unknowingly signed away. In this little
section we would like to present the different creative commons licences one is
oftentimes confronted with. This book for instance is published under a creative
commons license. They are widely used and especially popular online and it is
helpful to any researcher to understand what they mean.
Attribution CC BY Attribution-NoDerivs CC BY-ND
This license lets others distribute, remix, tweak,
and build upon your work, even commercially,
as long as they credit you for the original
creation. This is the most accommodating of
licenses offered. Recommended for maximum
dissemination and use of licensed materials
This license allows for redistribution,
commercial and non-commercial, as long as it
is passed along unchanged and in whole, with
credit to you
(continued)
S. Friesike (&)
Alexander von Humboldt Institute for Internet and Society, Berlin, Germany
e-mail: friesike@hiig.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_19,  The Author(s) 2014
287
(continued)
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This license lets others remix, tweak, and build
upon your work even for commercial purposes,
as long as they credit you and license their new
creations under the identical terms. This license
is often compared to ‘‘copyleft’’ free and open
source software licenses. All new works based
on yours will carry the same license, so any
derivatives will also allow commercial use.
This is the license used by Wikipedia, and it is
recommended for materials that would benefit
from incorporating content from Wikipedia and
similarly licensed projects
This license lets others remix, tweak, and build
upon your work non-commercially, and
although their new works must also
acknowledge you and be non-commercial, they
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BY-NC-SA
Attribution-NonCommercial-NoDerivs CC
BY-NC-ND
This license lets others remix, tweak, and build
upon your work non-commercially, as long as
they credit you and license their new creations
under the identical terms
This license is the most restrictive of our six
main licenses, only allowing others to
download your works and share them with
others as long as they credit you, but they can’t
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No Copyright: Public Domain CC0
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You can find more information on the well curated website of
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Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
288 S. Friesike
Organizing Collaboration on Scientific
Publications: From Email Lists
to Cloud Services
Sönke Bartling
Scientific publications—ranging from full papers, abstracts, and presentations to
posters, including grant applications and blog posts—are usually written by one or
a few authors and then corrected and reviewed by many authors. Collaboration on
scientific publications is a cornerstone of science. Many novel tools exist that can
be integrated and facilitate this process. However, novel disadvantages come with
the changes of the workflow—the future will tell as to which way to work on
documents will be the most efficient one.
Here we will discuss several ways of organizing collaboration and discuss the
advantages and disadvantages.
The 1.0 way of organizing collaboration:
• Sending out emails with texts, presentations and manual tracking of the version
number
• Using the track change or compare documents functionality of word processing
tools
• Using data transfer protocols, such as shared local networks or FTP to transfer
larger files
• Manual tracking of versions, backups, etc.
Advantage:
• Current state of the art
• Full data control—the process can be organized, so that only known and trusted
parties gain access to the content
• Politics—authors and co-authors can decide who sees which version and when
S. Bartling (&)
German Cancer Research Center, Heidelberg, Germany
e-mail: soenkebartling@gmx.de
S. Bartling
Institute for Clinical Radiology and Nuclear Medicine, Mannheim University Medical
Center, Heidelberg University, Mannheim, Germany
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_20,  The Author(s) 2014
289
• Free and open source solutions available
Disadvantage:
• Can be hard to keep track of edits and to integrate changes into a summarized
version
• Many ‘‘local cultures of keeping track of versions’’
• Simultaneous work needs central organization (leading author?) and might
create high workload to keep versions in sync
Solutions:
• Standard Email and document processor software packages, FTP, network file
systems
• MS Office solutions
• Open Office
• Text editors, LaTex
The cloud way of organizing collaboration:
• Using cloud tools that synchronize folders within the work group and create a
version history/backup of old files
• Using collaborative authoring tools to work simultaneously on documents
Advantage:
• Simultaneous work
• No out-of-sync version of documents
• Complete history
• No work load overhead for version synchronization and file transfer
management
Disadvantages:
• Only 97 % compatibility with current standard word or presentation software
solutions
• Third party companies gain access to data and texts, therefore have to be trusted
• Most free solutions are high-maintenance and need high amount of training,
local cloud solutions may lack features
• Scepticism regarding novel concepts with both good and bad arguments (the old
fashioned way is usually perceived as secure and well established, while dis-
advantages of novel concepts are over-perceived)
• Collaborative authoring tools do not relieve authors from recursively checking
the internal integrity of the documents—low threshold to submit changes may
decrease the diligence of contributors
290 S. Bartling
Solutions (examples):
• Dropbox
• Google documents/drive
• Zotero
• other Concurrent version systems (social coding!) with front ends to focus on
collaborative text editing
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
Organizing Collaboration on Scientific Publications 291
Unique Identifiers for Researchers
Martin Fenner and Laure Haak
Abstract Two large challenges that researchers face today are discovery and
evaluation. We are overwhelmed by the volume of new research works, and
traditional discovery tools are no longer sufficient. We are spending considerable
amounts of time optimizing the impact—and discoverability—of our research
work so as to support grant applications and promotions, and the traditional
measures for this are not enough.
The Problem
Two large challenges that researchers face today are discovery and evaluation. We
are overwhelmed by the volume of new research works, and traditional discovery
tools are no longer sufficient. We are spending considerable amounts of time opti-
mizing the impact—and discoverability—of our research work so as to support grant
applications and promotions, and the traditional measures for this are not enough.
Research is increasingly global and many interactions do not happen at a
personal level anymore, but rather through online tools, from email to videocon-
ferencing and online databases. Researchers have traditionally been identified by
their names, but this has never worked reliably because of confusions between
popular names (John Smith or Kim Lee), errors in transliteration (e.g. Müller
becomes Mueller or Muller), and name changes through marriage. These name
issues present an even greater challenge when we try to find out more about
M. Fenner (&)
Public Library of Science, San Francisco, CA, USA
e-mail: mfenner@plos.org
L. Haak
ORCID, Bethesda, MD, USA
e-mail: l.haak@orcid.org
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_21,  The Author(s) 2014
293
researchers who we do not know personally, for example an author of a paper we
find interesting or an interesting speaker at a conference, or about the research
going on in an institution where we have applied for a job.
Unique Identifiers as a Solution
The only way to uniquely identify a researcher is through an identifier rather than a
name. We, of course, use unique identifiers already with usernames for email,
social media accounts, institutional accounts, and more. What is missing is a
standard unique researcher identifier that is widely used by academic institutions,
funders, publishers, and online tools and services for researchers that is embedded
in research workflows and that automates the process of connecting researchers
and their research. The existing researcher identifier services and social networks
for scientists do not fill that gap. Many of the existing solutions are limited to a
geographic region or discipline, many researchers and institutions are reluctant to
use a commercial service, and some of the open solutions do not have the wide
support from the community needed to reach critical mass.
Open Researcher & Contributor ID (ORCID)1 is an international, interdisci-
plinary, open and not-for-profit organization created to solve the researcher name
ambiguity problem for the benefit of all stakeholders. ORCID was built with the
goal of becoming the universally accepted unique identifier for researchers:
1. ORCID is a community-driven organization
2. ORCID is not limited by discipline, institution, or geography
3. ORCID is an inclusive and transparently governed not-for profit organization
4. ORCID data and source code are available under recognized open licenses
5. the ORCID iD is part of institutional, publisher, and funding agency
infrastructures.
Furthermore, ORCID recognizes that existing researcher and identifier schemes
serve specific communities, and is working to link with, rather than replace,
existing infrastructures.
ORCID Registry
The ORCID Registry launched in October 2012, and as of July 2013 more than
200,000 researchers have registered. Use of the Registry is free: individuals may
create, edit, and share their ORCID record. ORCID staff, infrastructure, and
software development is supported by member fees for organizations embedding
the iD into systems.
1 ORCID: http://orcid.org/.
294 M. Fenner and L. Haak
Many organizations have started to integrate ORCID identifiers into their
infrastructure. In early 2013, this includes manuscript submission systems from
several publishers (Nature Publishing Group, Copernicus, Hindawi, and others),
linkage with other identifier schemes (Scopus, ResearcherID, Faculty of 1,000),
and integration with research works databases such as CrossRef and Figshare. The
first services building on top of the Registry have also emerged, including alt-
metrics (see chapter Altmetrics and Other Novel Measures for Scientific Impact)
tools to track the impact of all research outputs linked to a particular ORCID
identifier.
Outlook
One of the stated goals of the ORCID initiative is to facilitate linkage with all
research outputs: papers, monographs, books, datasets, software, peer review,
clinical trials, patents, grants, etc. By providing a switchboard for this information,
ORCID can help raise awareness of—and credit for—important research and
scholarly activities and help the research community develop tools and metrics to
better understand and evaluate impact. By embedding the ORCID iD in research
workflows, ORCID can also help to reduce the time researchers spend on
administrative and reporting activities, including publication lists for institutions
and funders, submissions to the institutional repository, and more. Unique iden-
tifiers for researchers and research outputs can automate much of this reporting,
Unique Identifiers for Researchers 295
giving researchers more time to do actual research. Widespread adoption of unique
researcher identifiers will foster the development of exciting new tools and ser-
vices for researchers which will make science more collaborative, productive, and
interesting.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
296 M. Fenner and L. Haak
Challenges of Open Data in Medical
Research
Ralf Floca
Abstract The success of modern, evidence based and personalized medical
research is highly dependent on the availability of a sufficient data basis in terms of
quantity and quality. This often also implies topics like exchange and consolida-
tion of data. In the area of conflict between data privacy, institutional structures
and research interests, several technical, organizational and legal challenges
emerge. Coping with these challenges is one of the main tasks of information
management in medical research. Using the example of cancer research, this case
study points out the marginal conditions, requirements and peculiarities of han-
dling research data in the context of medical research.
Introduction
First the general importance of data exchange and consolidation will be discussed.
In the second section, the important role of the patient in medical research will be
addressed and how it affects the handling of data. The third section focuses on the
question what the role of open data could be in this context. Finally, the fourth
section tackles the topic of challenges of open data in the context of medical
(research) data. It tries to illustrate why it is a problem and what the obstacles are.
R. Floca (&)
German Cancer Research Center, Heidelberg, Germany
e-mail: r.floca@dkfz.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_22,  The Author(s) 2014
297
Importance of Data Exchange and Consolidation
With oncology striving after personalized medicine and individualized therapy,
stratification becomes a major topic in cancer research. The stratification of tumor
biology and patients is important to provide individual therapies with maximum
tumor control (optimally total remission) and minimal side effects and risks for the
patient. Therefore, the search for diagnostic markers, e.g. diagnostic imaging,
antibody tests or genome analysis, as well as for adequate treatments with respect
to specific markers is constantly intensified.
Looking at research results, it becomes obvious that cancer diseases (e.g.
prostate cancer or breast cancer) are more like disease families with a multitude of
sub-types and that the anatomical classification of tumors might be misleading and
a classification according to the pathological change of signaling pathways on the
cellular level is more adequate. This differentiation is very relevant because for
one patient a certain treatment may be effective and absolutely relevant while it
has no positive impact on tumor control for other patients with the ‘‘same’’ cancer
and only bears side effects.
In order to have an evidence-based medicine with a sound statistical basis, the
amount and quality of available data becomes very important. The required
amount of data increases with the number of relevant factors. Looking at the
current cancer research, one has a vast array of factors and information—and it is
still increasing. One has for example the patient and tumor biology (e.g. a mul-
titude of diagnostic images; analysis of the genome, proteome etc.; lab results; cell
pathologies; …); way of living before and after the diagnose/therapy; environ-
mental factors and chosen individual therapy.
The current situation can therefore be characterized as too few cases for too
many factors. The size of sample sets even large institutions can collect is too
small for evidence-based and highly stratified medicine. John Wilbanks, the chief
commons officer of Sage Bionetworks,1 put this fact more bluntly:
[…] neither Google nor Facebook would make a change to an advertising algorithm with a
sample set as small as that used in a Phase III clinical trial.
John Wilbanks, Sage Bionetworks
Kotz, J.; SciBX 5(25); 2012
One strategy to tackle these shortcomings is to build up networks and initiatives
and pool the data to acquire sufficient sample sets2. This is not a trivial task
because of the heterogeneity of the public health sector that has to be managed.
1 Sage Bionetworks is the name of a research institute which promotes biotechnology by
practicing and encouraging Open Science. It is founded with a donation of the pharmaceutical
services company Quinitles. cf. http://en.wikipedia.org/wiki/Sage_Bionetworks.
2 An Example is the German Consortium for Translational Cancer Research (Deutsches
Konsortium für Translationale Krebsforschung, DKTK; http://www.dkfz.de/de/dktk/index.html).
One objective in the DKTK is the establishement of a clinical communication platform. This
platform aims amongst others to better coordinate and standardize multi centric studies.
298 R. Floca
You have got several stakeholders, heterogeneous documentation of information
(different in style, recorded data, formats, storage media) and different operational
procedures (time and context of data acquisition).
Thus, it is inevitable to cope with this heterogeneity and to build large study
bases by sharing and pooling medical research data in order to realize evidence-
based personalized medicine. One way to achieve this goal could be the adaption
of ideas and concepts of open research data (see below).
Role of the Patient and its Data
As described in the previous section, data is of high importance. This data cannot
be collected without patients and their cooperation is crucial on several levels. This
leads to a very central role for the patient and, in addition, to a special nature of
medical data and its acquisition compared to other research data.
1. Medical data is personal data
By default medical data is always personal data. The implications that derive
from this fact may vary according to the legal framework of a country (e.g. USA:
Health Insurance Portability and Accountability Act (HIPAA); Germany: right to
informational self-determination/personal rights), but it has almost always an
impact on how medical data may be acquired, stored and used. In Germany, for
instance, an individual (in this context a patient) must always be able to query
which personal information is stored, where the information is stored and for
which purpose this information is used. The information may only be altered,
transferred, used, stored or deleted with according permission and sufficient
traceability guaranteed.
2. Ethics
Having an experimental setup that allows the acquisition of data suitable for
verifying or falsifying the scientific hypothesis goes without saying. But in the
context of human research it is also mandatory to ensure that ethical principles are
regarded. These principles are often derived from the Declaration of Helsinki3 and
implemented by national regulations (e.g. USA: institutional review boards;
Germany: Ethikkommision). Thus every study design is reviewed and needs ethic
approval. This may lead to situations where experimental setups are optimal from
a technocratic research perspective but cannot be approved ethically and therefore
must be altered or not conducted.
3 The Declaration was originally adopted in June 1964 in Helsinki, Finland. The Declaration is
an important document in the history of research ethics as the first significant effort of the medical
community to regulate research itself, and forms the basis of most subsequent documents.
Challenges of Open Data in Medical Research 299
3. Lack of predictability and limits of measurements
Most research-relevant incidents (e.g. (re)occurrence of an illness, adverse
reactions) are not predictable and not projectable (fortunately; see ‘‘Ethics’’).
Therefore, you have to wait until enough natural incidents have happened and are
monitored. The latter can be complicated, because not every measurement tech-
nique can be used arbitrarily frequent due to technical, ethical4 or compliance5
reasons. Without the possibilities to repeat6 ‘‘measurements’’ and in conjunction
with the heterogeneity explained in the previous section, getting a sufficient
number of cases is a nontrivial task.
4. Long ‘‘field’’ observation periods
In order to derive conclusions that really matter for patients, like ‘‘improved
survival’’or ‘‘improved quality of life’’ you need observation periods of 10 and
more years. In this time, the individuals will move around in the distributed public
health system (e.g. by changing their place of residence, choosing new general
practitioners). Data will be accumulated, but is not centrally available because of
the heterogeneous nature of the system. Therefore, keeping track on a study
participant and assembling a non-biased, non-filtered view on study relevant data7
can be very complicated.
5. Compliance
Besides all the explained technical and organizational problems, the key
stakeholder is the study participant/patient and its compliance to the study and the
therapy. If the participant is not compliant to the study, he drops out, which results
in missing data. This missing data can lead to a selection bias and must be handled
with expertise in order to make a reliable assessment of the trial’s result. The
dropout rates vary and depend on the study; rates around 20 % are not unusual,
also rates up to 50 % have been reported.
Participants that are not therapy compliant alter the therapy or skip it totally
(e.g. changing medication; skipping exercises; taking additional drugs). According
to a report (WHO 2003) of the World Health Organization up to 50 % of the
patients are not therapy compliant. An unnoticed lack of therapy compliance may
introduce a bias towards the trial results.
4 e.g.: you cannot repeat an x-ray based imaging arbitrarily often, due to radiation exposition;
you cannot expect a person suffering from cancer to daily lie in an MRI scanner for an hour.
5 e.g.: The payload for an imaging study can easily double the duration of an examination. This
may lead to more stress for the participant and decreasing compliance.
6 Single measurements can be repeated (but this implies stress and leads to decreasing
compliance; or is not ethically not compliant). But the complete course of treatment cannot be
repeated; if a treatment event is missed, it is missed.
7 This could be a lot of (different) data. See for example the relevant factors from
section Importance of Data Exchange and Consolidation.
300 R. Floca
6. Consent
The patient has to consent8 on three levels before he can be part of a medical
trial. First, he must consent to a therapy that is relevant for the trial. Second, if all
inclusion criteria and no exclusion criteria for the trial are met, the patient must
consent to be part of the trial. Third, the patient must consent to the usage of the
data. The third consent exists in different types, namely: specific, extended,
unspecific/broad. The specific consent limits the usage to the very trial it was made
for. In the context of open data this type of consent is not useful and is considered
as limiting by many researchers (see challenges). The extended consent often
allows the usage for other questions in the same field as the original trial (e.g.
usage for cancer research). If it is extended to a level where any research is
allowed, it is an unspecific consent. An example for this type of consent is the
Portable Legal Consent devised by the project ‘‘Consent to Research’’.9
You may find each aspect in other types of research data, but the combination
of all six aspects is very distinctive for medical research data and makes special
handling necessary.
Role of Open Research Data
The chapter ‘‘Open Research Data: From Vision to Practice’’ in this book gives an
overview over the benefits open data is supposed to bring. Websites like ‘‘Open
Access success stories’’10 try to document these benefits arising from Open
Access/Open Science. Also in the broad field of medical research, many groups
advocate a different handling of data (often in terms of open data).
One main reason is the requirement of transparency and validation of results
and methods. For example in the domain of medical image processing the research
data (test data, references and clinical meta data) is often not published. This
renders the independent testing and verification of published results, as well as the
translation into practice very difficult. Thus initiatives like the concept of Deserno
8 The necessity for an informed consent of the patient can be derived from legal (see point 1) and
ethical (see point 2) requirements. It is explained in detail here to characterize the different types
of consent.
9 ‘‘Consent to Research’’/WeConsent.us, is an initiative by John Wilbanks/Sage Bionetwirks
with the goal to create an open, massive, mine-able database of data about health and genomics.
One step is the Portable Legal Consent as a broad consent for the usage of data in research.
Another step is the We the People petition lead by Wilbanks and signed by 65,000 people.
February 2013 the US Government replied and announced a plan to open up taxpayer-funded
research data and make it available for free.
10 http://www.oastories.org: The site is provided by the initiative knowledge-exchange.info
which is supported by Denmark’s Electronic Research Library (DEFF, Denmark), the German
Research Foundation (DFG, Germany), the Joint Information Systems Committee (JISC; UK)
und SURF (Netherlands).
Challenges of Open Data in Medical Research 301
et al. (2012) try to build up open data repositories. Another example would be the
article of Begley and Ellis (2012), which discusses current problems in preclinical
cancer research. Amongst others, it recommends a publishing of positive and
negative result data in order to achieve more transparency and reliability of
research.
Besides this, several groups (e.g. the Genetic Alliance11 or the former men-
tioned project Consent to Research) see Open Access to data as the only sensible
alternative to the ongoing privatization of Science data and results. For instance
the company 23 and Me offers genome sequencing for $99.12 In addition to
the offered service the company builds up a private database for research and the
customers consent that this data may be used by the company to develop intel-
lectual property and commercialize products.13
Another topic where the research community could benefit from the imple-
mentation of open data publishing is the heterogeneity of data (see next section:
challenges). Making data available means, that it is:
• open (in terms of at least one public proceeding to get access)
• normed (content of data and semantics are well defined)
• machine readable
• in standardized format.
Having this quality of data would be beneficial, for instance, for radiology,
whose ‘‘[…] images contain a wealth of information, such as anatomy and
pathology, which is often not explicit and computationally accessible […]’’, as
stated by Rubin et al. (2008). Thus, implementing open data could be an oppor-
tunity to tackle this problem as well.
Challenges
The previous sections have discussed the need for data consolidation, the pecu-
liarities of medical research data and how medical research is or could be (posi-
tively) affected by concepts of open research data. It is irrelevant which approach
is taken in order to exchange and consolidate data, you will always face challenges
and barriers on different levels: regulatory, organizational and technical.
The general issues and barriers are discussed in detail by Pampel and Dall-
meier-Tiessen (see chapter Open Research Data: From Vision to Practice). This
section adds some aspects to this topic from the perspective of medical research
data.
11 http://www.geneticalliance.org.
12 https://www.23andme.com/about/press/12_11_2012/.
13 The article of Hayden (2012a) discusses the topic of commercial usage on the occasion of the
first patent (a patented gen sequence) of the company 23 and me.
302 R. Floca
Regulatory constraints for medical (research) data derive from the necessity of
ethic approval and legal compliance when handling personal data (see sec-
tion Role of the Patient and Its Data, point 1 and 2). There are still open discus-
sions and work for the legislative bodies to provide an adequate frame. The article
of Hayden (2012a) depicts the informed consent as a broken contract and illus-
trates how today on one hand participants feel confused by the need of ‘‘reading
between the lines’’, on the other hand researchers cannot pool data due to specific
consents and regulatory issues.
Although there are open issues on the regulatory level, ultimately it will be the
obstacles on the organizational and technical level—which may derive from
regulatory decisions—which determine if and how open data may improve med-
ical research. Therefore, two of these issues will be discussed in more detail.
Pooling the Data
Given that the requirements are met and you are allowed to pool the data of
different sources for your medical research, you have to deal with two obstacles:
mapping the patient and data heterogeneity.
As previously noted, patients move within the public health system and
therefore medical records are created in various locations. In order to pool the data
correctly, you must ensure that all records originated with an individual are
mapped towards it but no other records. Errors in this pooling process lead either
to ‘‘patients’’ consisting of data from several individuals or the splitting of one
individual in several ‘‘patients’’. Preventing these errors from happening can be
hard to implement because prevention strategies are somehow competing (e.g. if
you have very strict mapping criteria, you minimize the occurrence of multi-
individual-patients but have a higher change of split individuals due to typing
errors in the patient name).
In the case that you have successfully pooled the data and handled the mapping
of patients, the issue of heterogeneity remains. This difference of data coverage,
structure and semantics between institutions (which data they store, how the data is
stored and interpreted) makes it difficult to guarantee comparability of pooled data
and to avoid any kind of selection bias (e.g.: Is an event really absent or just not
classified appropriately by a pooled study protocol).
Anonymization, Pseudonymization and Reidentification
Individuals must be protected from (re)identification via their personal data used for
research. German privacy laws, for instance, define anonymization and pseudon-
ymization as sufficient, if they prohibit reidentification or reidentification is only
possible with a disproportional large expenditure of time, money and workforce.14
14 see § 3 (6) Federal Data Protection Act or corresponding federal state law.
Challenges of Open Data in Medical Research 303
Ensuring this requirement becomes increasingly harder due to technical progress,
growing computational power and—ironically—more open data.
Reidentification can be done via data-mining of accessible data and so-called
quasi-identifiers, a set of (common) properties that are—in their combination—so
specific that they can be used to identify. A modern everyday life example would
be Panopticlick.15 It is a website of the Electronic Frontier Foundation that
demonstrates the uniqueness of a browser (Eckersley 2010) which serves as a
quasi-identifier. Therefore, a set of ‘‘harmless’’ properties is used, like screen
resolution, time zone or installed system fonts.
The following examples illustrate possibilities and incidents of reidentification:
a. Simple demographics: The publications of Sweeney (2000) and Golle (2006)
indicate that for 63–87 % of the U.S. citizens the set of birth date, sex and
postal code is unique and a quasi-identifier.
b. ICD codes: Loukides et al. (2010) assume that 96.5 % of the patients can be
identified by their set of ICD916 diagnoses codes. For their research the Van-
derbilt Native Electrical Conduction (VNEC) dataset was used. The data set
was compiled and published for an NIH17 funded genome-wide association
study.
c. AOL search data: AOL put anonymized Internet search data (including health-
related searches) on its web site. New York Times reporters (Barbaro et al.
2006) were able to re-identify an individual from her search records within a
few days.
d. Chicago homicide database: Students (Ochoa et al. 2001) were able to
re-identify a 35 % of individuals in the Chicago homicide database by linking it
with the social security death index.
e. Netflix movie recommendations18: Individuals in an anonymized publicly
available database of customer movie recommendations from Netflix are
re-identified by linking their ratings with ratings in a publicly available Internet
movie rating web site.
f. Re-identification of the medical record of the governor of Massachusetts: Data
from the Group Insurance Commission, which purchases health insurance for
state employees, was matched against the voter list for Cambridge, re-identi-
fying the governor’s health insurance records (Sweeney 2002).
15 see https://panopticlick.eff.org/
16 ICD: International Classification of Diseases. It is a health care classification system that
provides codes to classify diseases as well as a symptoms, abnormal findings, social
circumstances and external causes for injury or disease. It is published by the World Health
Organization and is used worldwide; amongst others for morbidity statistics and reimbursement
systems.
17 National Institutes of Health; USA.
18 See http://www.wired.com/threatlevel/2009/12/netflix-privacy-lawsuit and http://www.
wired.com/science/discoveries/news/2007/03/72963.
304 R. Floca
The examples illustrate the increasing risk of reidentification and the boundary
is constantly pushed further. If you look for example at the development of
miniaturised DNA sequenzing systems19 (planned costs of US$1,000 per device),
sequencing DNA (and using it as data) will presumably not stay limited to insti-
tutions and organisations who can afford currently expensive sequencing
technologies.
Thus proceedings that are compliant to current privacy laws and the common
understanding of privacy are only feasible if data is dropped or generalized (e.g.
age bands instead of birth date or only the first two digits of postal codes). This
could be done for example by not granting direct access to the research data but
offering a view tailored for the specific research aims. Each view ponders the
necessity and usefulness of each data element (or possible generalizations) against
the risk of reidentification.
Even if an infrastructure is provided that enables the filtering of data described
above, you will always have medical data that is easily reidentifiable and at least
hard to be pseudonymized. Good examples are radiological head or whole body
Fig. 1 Example for a magnetic resonance head image (MRI). The upper MRI shows an original
layer of data set of an study participant (axial view, parallel to the feet). The MRIs below are
reconstructions of the original data in sagittal view (left) and coronal view (right). The sagittal
view is similar to a head silhouette and therefore more familiar
19 e.g. the MinIONTM device from Oxford Nanopore Technologies (http://www.
nanoporetech.com). See also (Hayden 2012b).
Challenges of Open Data in Medical Research 305
images. Figure 1 shows head images from a study participant.20 The original
perspective of the image (axial view) and the other medical perspectives (sagittal
and coronal view) may not be suitable for reidentification by everyman. But a
simple volume rendering of the data (Fig. 2) allows easy reidentification. Starting
from this point with modern technologies several scenarios are not too far-fetched.
An artificial picture, for instance, could be reconstructed and used with available
face recognition APIs21 or you could take the volume data convert it into a 3D
model and print it via a 3D-printer.22
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
20 The data shown in Figs. 1 and 2 are provided by courtesy of Markus Graf (German Cancer
Research Center).
21 One example would be web API offered by face.com (http://en.wikipedia.org/wiki/Face.com).
22 In order to print 3D-Models you can use services like www.shapeways.com or http://
i.materialise.com.
Fig. 2 Volumetric rendering
of the data set shown in
Fig. 1. The possibility to
reidentify is now strikingly
obvious. Volumetric
rendering can easily be done
with software tools publically
available
306 R. Floca
References
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research. Nature, 483(7391), 531–533. doi:10.1038/483531a.
Deserno, T. M., Welter, P., & Horsch, A. (2012). Towards a repository for standardized medical
image and signal case data annotated with ground truth. Journal of Digital Imaging, 25(2),
213–226. doi:10.1007/s10278-011-9428-4.
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77–80). New York: ACM.
Hayden, E. C. (2012a). Informed consent: A broken contract. Nature, 486(7403), 312–314.
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Hayden, E. C. (2012b). Nanopore genome sequencer makes its debut. Nature,. doi:10.1038/
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Loukides, G., Denny, J. C., & Malin, B. (2010). The disclosure of diagnosis codes can breach
research participants’ privacy. Journal of the American Medical Informatics Association, 17,
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Ochoa, S., et al. (2001). Reidentification of individuals in Chicago’s homicide database: A
technical and legal study. Massachusetts: Massachusetts Institute of Technology.
Rubin, D. L., et al. (2008). iPad: Semantic annotation and markup of radiological images. In
Proceedings of AMIA Annual Symposium (pp. 626–630).
Sweeney, L. (2000). Uniqueness of simple demographics in the U.S. poopulation, LIDAPWP4. In
Pittsburgh: Carnegie Mellon University, Laboratory for International Data Privacy.
Sweeney, L. (2002). k-Anonymity: A model for protecting privacy. International Journal of
Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557–570. doi:10.1142/
S0218488502001648.
WHO. 2003. Report. Adherence to long-term therapies: evidence for action, Available at: http://
www.who.int/chp/knowledge/publications/adherence_report/en/.
Challenges of Open Data in Medical Research 307
On the Sociology of Science 2.0
Vladimir B. Teif
The difference between technology and slavery is that slaves
are fully aware that they are not free
—Nassim Nicholas Taleb
Abstract While the previous chapters of this book reveal some technical prin-
ciples of Science 2.0, here we look at the psychological and sociological motives
of researchers using these novel tools. In this chapter we will see how and why the
main drivers of scientists in the Internet are different from usual ‘‘offline’’ scien-
tists. We consider here an Internet-geek (driven by the psychological principles
described below), assuming that he/she is also a scientist (the potential audience of
Science 2.0). So how would such a person behave?
While the previous chapters of this book reveal some technical principles of
Science 2.0, here we look at the psychological and sociological motives of
researchers using these novel tools. In this chapter we will see how and why the
main drivers of scientists in the Internet are different from usual ‘‘offline’’ scien-
tists. We consider here an Internet-geek (driven by the psychological principles
described below), assuming that he/she is also a scientist (the potential audience of
Science 2.0). So how would such a person behave?
Let us first outline the classical understanding of the usual ‘‘offline’’ scientist.
About 70 years ago Merton (1942) summarized some of the basic sociological
principles that drive scientists, the Mertonian norms of science, often referred to by
the acronym ‘‘CUDOS’’. These include communalism—the common ownership of
scientific discoveries, according to which scientists give up intellectual property in
exchange for recognition and esteem; universalism—according to which claims to
truth are evaluated in terms of universal or impersonal criteria, and not on the basis
of race, class, gender, religion, or nationality; disinterestedness—according to
which scientists are rewarded for acting in ways that outwardly appear to be
selfless; organized skepticism—all ideas must be tested and are subject to rigorous,
structured community scrutiny.
V. B. Teif (&)
German Cancer Research Center (DKFZ), Heidelberg, Germany
e-mail: v.teif@dkfz-heidelberg.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_23,  The Author(s) 2014
309
In addition to the Mertonian principles, western scientists are also governed by
the economic principles outlined in the ‘‘Republic of Science’’ by Polanyi (1962)
about half a century ago. These economic principles have become extremely
important now, when the era of ‘‘scientific Eldorado’’ has finished and scientists
have to make efforts just to remain in science. Economic pressure dictates that
scientists are rewarded for being productive, competitive, and successful. Scien-
tists need to publish as much as possible, as often as possible, and do their best to
advertise their work through all types of media so as to attract citations, funding,
and recognition. Sociologically, all of the forces mentioned above, whether they
are egoistic or altruistic, are job-centric. This is true for conventional ‘‘offline’’
scientists. Is it still true for Science 2.0?
To address this question, the author has conducted a survey online. 50 most
active users of one of the leading scientific online communities, professional life
scientists with Ph.D. degree, were asked the following question: ‘‘What are you
doing here?’’ The respondents were given several choices and were asked to
choose only one answer, most closely resembling their feelings. Below is a
summary of received answers:
I am having here a nice time and it is useful for my work (*50/50) 40 %
I am having here a nice time, relaxing after work 19 %
I am polishing my scientific arguments in online discussions 12 %
I am addicted to Internet. I would like to leave this resource but can not 10 %
I am getting here some useful information for my work 5 %
I am popularizing my scientific ideas/publications 5 %
I am advertising my products/services 5 %
I am helping other members, and I like it 2 %
I am maintaining contacts with my colleagues here 2 %
I am here mainly to exchange PDF articles free of charge 0 %
This survey presents quite surprising results for the advocates of Science 2.0.
Firstly, we see that the overstated need for free access to scientific publications is
not the driving force at all (none of the 50 respondents was using social tools to
exchange PDFs of articles behind subscriptions). Secondly, the ‘‘facebook-type’’
activity of maintaining contacts with ‘‘friends’’ is negligible (just 2 % of
respondents use Science 2.0 tools to maintain contacts with colleagues). As
expected, few people use social media to sell/buy/advertise something scientific
(5 % for each of these categories). Now we come to the largest shares. 10 % of
scientists openly say in this anonymous survey that they are simply addicted to the
Internet (in the negative sense). 12 % use scientific tools online to polish their
arguments (probably before publication). 19 % enjoy Science 2.0 tools just for fun.
Finally, 40 % of scientists combine fun and usefulness for work. (Compare with
just 5 % of scientists who answered that they are using Science 2.0 tools primarily
to get something useful to work). Taken together, these data explain why scientific
310 V. B. Teif
social media has failed to attract the majority of usual ‘‘offline’’ scientists. Just the
basic motivation behind most Science 2.0 systems offering services other than the
top three lines of this survey is wrong. Nothing is wrong with scientists. Something
is wrong with Science 2.0, which needs to be more flexible. Acknowledging the
huge progress reached by Science 2.0, we have to admit that it still requires large
changes, and the next wave of science, Science 3.0, is yet to come (Teif 2013,
2009).
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
Merton, R. K. (1942). The sociology of science: Theoretical and empirical investigations.
Chicago: University of Chicago Press.
Polanyi, M. (1962). The republic of science: Its political and economic theory. Minerva, 1,
54–74.
Teif, V. B. (2009). Science 3.0: The future of science in the Internet. Available at: http://
generegulation.info/index.php/science-30.
Teif, V. B. (2013). Science 3.0: Corrections to the Science 2.0 paradigm. Available at:
ArXiv:1301.2522.
On the Sociology of Science 2.0 311
How This Book was Created Using
Collaborative Authoring and Cloud Tools
Sönke Bartling
Abstract This book about novel publishing and collaboration methods of schol-
arly knowledge was itself created using novel and collaborative authoring tools.
Google Docs as a collaborative authoring and text editing tool and Dropbox as a
cloud storage solution were used. Our experience was a positive one and we think
that it saved us a lot of organisational emails and hundreds of work hours. Here we
describe the workflow process in detail so that the interested author might benefit
from what we learnt.
How this Book was Created
The creation process can be divided in several phases in regard to the online tools
which were used.
Phase I: Potential content was collected and authors were invited to participate.
Shortly afterwards, a table in Google Docs was collaboratively maintained by both
editors. For each chapter, the title and possible authors were discussed, emails to
authors were sent, and feedback was added. Chapters were divided among both
editors, so that one contact person was responsible for each chapter. In jour-fixe
Skype sessions the status of the work in progress was discussed.
Phase II: A table of contents was created as a text document in Google Docs.
The returning abstracts were uploaded to Google Docs and the links were created
to the abstracts. The table of contents file served as the central document (Fig. 1).
S. Bartling (&)
German Cancer Research Center, Heidelberg, Germany
e-mail: soenkebartling@gmx.de
S. Bartling
Institute for Clinical Radiology and Nuclear Medicine, Mannheim University Medical
Center, Heidelberg University, Mannheim, Germany
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_24,  The Author(s) 2014
313
Phase III: Returning articles were uploaded to Google Docs and the authors
were invited to participate with ‘editing’ privileges (Fig. 2). Articles were also
linked to the TOC. Authors and editors worked iteratively on the Google docu-
ments. Commenting functionality was used to discuss points of controversy.
Images were designed in Apple Keynote and the image files and other files were
shared using Dropbox.
Phase IV: An internal review started once almost final versions of the chapters
existed. All authors received a TOC with links to every chapter—all authors
possessed commenting privileges for all other chapters. Only the chapter authors
and editors had the right to change text. The internal references within the book
were set in this phase and consistency among the chapters was assured. Citations
were added using the Harvard author-date style, omitting the necessity of changing
in-text references if novel references were added. Since Google Docs lacks inte-
gration with a reference management system, Zotero was used to import the ref-
erences from databases. The bibliographies for each chapter were generated from
Fig. 1 The table of contents was the central workplace for the editors in the early phase of this
book project
314 S. Bartling
shared Zotero databases (one for each chapter) and manually inserted into the
documents. URLs were included as footnotes.
Phase V: After reviewing changes and undertaking final proofreading, a
finalized table of contents with embedded links was sent to the publisher.
Phase VI (now): The book is now available as Open Access printed book and
its content can be downloaded from www.openingscience.org. Here the content
can also be edited.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
Fig. 2 During the editing process of the chapters, the authors and editors changed the chapters
with editing privileges, while all others authors were invited to comment—serving as a form of
internal peer-review
How This Book was Created 315
History II.O
Luka Oreškovic´
Abstract Science 2.0 is a concept of immense potential for the historical discipline.
Throughout the world, researchers are undertaking different projects that attempt to
harness the benefits of research efforts employing a wider community, be it fellow
historians or the general public, and have reached different conclusions and results.
Yet, most of these projects point to a clear direction in historical research of
increasingly relying on the tremendous benefits that digital, and at times, Open
Access to both scholarly work and primary sources has given them. While the idea of
using Science 2.0 and crowd sourcing for historical research has produced a number
of projects of great potential, Open Science and ideas of open publishing remain
largely underutilized and avoided by the academic community of historians.
Introduction: Issues and Opportunities
of Open History and 2.0
Science 2.0 is a concept of immense potential for the historical discipline.
Throughout the world, researchers are undertaking different projects that attempt to
harness the benefits of research efforts employing a wider community, be it fellow
historians or the general public, and have reached different conclusions and results.
Yet, most of these projects point to a clear direction in historical research of
increasingly relying on the tremendous benefits that digital, and at times, Open
Access to both scholarly work and primary sources has given them. While the idea
of using Science 2.0 and crowd sourcing for historical research has produced a
number of projects of great potential, Open Science and ideas of open publishing
remain largely underutilized and avoided by the academic community of historians.
Many issues arise between using Science 2.0 in historical research and becoming an
L. Orešković (&)
Harvard University, Cambridge, USA
e-mail: luka.oreskovic@gmail.com
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_25,  The Author(s) 2014
317
‘‘Open Science’’ for history as an academic discipline, as opening academic history
and research to Open Access, digital publication is both challenging and alarming
for historians. It is challenging in terms of opening the field and competition in
publishing historical work to a series of authors who might previously be dis-
qualified due to lack of academic status such as scholarly success and track record
as well as institutionally recognized intellectual credibility derived from a Ph.D. It
is alarming in terms of economic constraints professional historians face today as
institutions and fellow academics are unlikely to recognize scholarship published as
Open Science, thus limiting career advancement possibilities in academia. While
academic history increasingly comes to rely on open research platforms and Sci-
ence 2.0 methods of historical research, it is likely that historical publishing, even
though numerous new open publication platforms are developed, will remain lar-
gely limited to print or access-limiting journals and books.
History 2.0: Developments, Directions and Conclusions
A number of noteworthy projects either offering Open Access to historical sources
or relying on crowd sourcing and online research contribution platforms that are
open to a wider professional community or even the general public have come into
being over the past years. While numerous platforms offer the general public Open
Access to historical databases, projects that also involve contributors in the research
effort are fewer.1 Still, several projects that pool research from a wide array of
contributors, ranging from professional historians to the interested general public,
have gained traction and achieved noteworthiness. Projects like University College
London’s Transcribe Bentham,2 National Geographic’s Field Expedition:
Mongolia,3 Founders and Survivors4 on Tasmanian convicts and University of
Oxford’s Ancient Lives and The project Woruldhord5 have achieved very successful
collaboration among its members, and offer insight into successful practices for
crowd sourcing historical research.6 Each of these projects in particular holds a
valuable lesson on how crowd sourcing in historical research should be approached
and raises both conceptual and practical questions about the nature of the practice.
1 Diaspora: http://www.diaspora.illinois.edu/newsletter.html; Old Biley Online: http://
www.oldbaileyonline.org/
2 see Causer 2013
3 National Geographic: http://exploration.nationalgeographic.com/mongolia
4 Founders and Survivors: http://foundersandsurvivors.org
5 Woruldhord: http://projects.oucs.ox.ac.uk/woruldhord/
6 Ancient Lives: http://www.ancientlives.org
318 L. Orešković
Types of Collaboration in Research Crowd
Sourcing Projects
Transcribing platforms—The University College London project Transcribe
Bentham presents the most elementary of models for employing crowd sourcing in
historical research.7 The project, as its name aptly describes, uses its members who
join the platform from the wider public for transcribing original and unstudied
manuscripts and documents written by the philosopher Jeremy Bentham with the
aim of assembling an online database of all of Bentham’s writing in unedited form.
The elementary nature of this project is in the type of contribution it seeks from its
members, using them as a free resource for manuscript transcription. Any content-
level contribution is not possible in Transcribe Bentham yet its very nature and
goal of completing an online database of Bentham’s writings limits the level and
type of members’ contribution. The project attracted great attention and won the
Prix Ars Electronica prize for Distinction in the Digital Communities (Fig. 1).
A very similar model is also employed by University of Oxford’s Ancient Lives
platform that allows users to transcribe the Oxyrhynchus Papyri through an online
tool with specific symbols and scanned papyri. The tool allows anyone interested
to contribute in the physical work of transcribing ancient manuscripts while the
contributions are then analyzed, evaluated for accuracy by historians curating the
project and and translated into English by professional translators. The historians,
papyrologists and researchers providing curatorial oversight of contributions by
Fig. 1 Transcribe Bentham, University College London, Screenshot, February 26, 2013
7 http://www.transcribe-bentham.da.ulcc.ac.uk/td/Transcribe_Bentham
History II.O 319
the participating public in the crowd sourcing project allows for academic accu-
racy and credibility, thus enabling more reliable usage of the material transcribed
in the project (Fig. 2).
Projects that employ the crowd for transcribing and information pooling from
the already existing resources, such as the Ancient Lives, Transcribe Bentham and
others also implemented possibilities for crowd contributions such as forms for
additional information on transcribed documents, indexing tools for transcribed
documents or applications that provide a space for interaction and commentary
among various contributors to the project about their work. While the first two
additional contribution modes are of questionable applicability as they often
require some degree of professional knowledge, the possibility of exchanging
comments and experiences, coupled with the possibility of moderating such dis-
cussions for the purpose of establishing and sharing best transcription practices
could be beneficial.
Content platforms—More demanding platform for participant collaboration in
historical research was implemented by National Geographic’s Field Expedition:
Mongolia, The Founders and Survivors Project and University of Oxford’s The
project Woruldhord.
National Geographic’s project might be the least beneficial one in terms of
actual contributions to historical research, yet it represents an idea in the right
Fig. 2 Greek papyrus in the web interface for online transcription. Courtesy of Ancient Lives
Project, Oxford
320 L. Orešković
direction that opens existing sources such as transcripts, documents or maps, for
analysis by the general public, the results of which are then reviewed, curated and
applied by professionals. The Field Expedition: Mongolia platform is based on an
actual historical and archaeological project of search for Genghis Khan’s tomb.
The platform enables users, of whom there are an astounding 31,591 registered at
the moment of writing this review, to review satellite imagery and tag it for objects
and landmarks that researchers on the ground can then potentially utilize for their
professional work. While the platform’s aim is primarily to increase awareness of
Mongolia’s rich historical heritage than actually contributing to research on the
ground, the idea of using crowd sourcing for more advanced research tasks that
still demand substantial manpower and thus limit the number of professionals
doing the work shows great promise.
Founders and Survivors project and University of Oxford’s The project Wo-
ruldhord elevate collaboration of the participating public to a new level. Rather
than relying on non-expert users for working or reviewing already existing sour-
ces, these two projects aim to build and assemble databases of historical primary
sources submitted by users, thus relying on users for actual content building.
Founders and Survivors isa study of the 73,000convicts transported to Tasmania
between 1803and 1853, aiming toassemble a record systemof these convicts and
buildupon this with data suchas health records,demographics andpersonal infor-
mation. The project, in the words of its founders, aims to ‘‘combine professional
expertise with the enthusiasm of volunteers.’’ Some types of documents submitted
by registered users in the project include conduct records, surgeons’ journals,
newspaper reports, births, deaths and marriages, parish records, family histories,
memories and legends as well as formal sources like records from the convict
system, trial and conviction documents and tickets of leave. Volunteers included
genealogists and family historians, librarians, members of the wider public whose
personal or family histories relate to the Tasmanian convicts and other researchers
interested in the field. The submitted data is reviewed and organized by IT spe-
cialists and professional historians and published in the database (Fig. 3).
The applications of such a database are best exhibited in the number of research
projects that arose from the Founders and Survivors project—these include a study
of Morbidity and mortality on the voyage to Australia, Crime and convicts in
Tasmania, 1853–1900, Fertility decline in late C19 Tasmania, Prostitution and
female convicts and Tracing convicts’ descendants who served in WWI. The
success of the projectcan largely be attributedto relying on a largenumber of
public users forresearch while navigatingand moderating theirresearch through
bothprepopulated forms thatlimit the type of data andinformation users can
submit as well asprofessional curation byhistorians and otherspecialists (Fig. 4).
A very similar model is used by University of Oxford’s The project Woruldhord
that collects a database of photographs, documents and presentations relating to
Anglo-Saxon centered English History and Old English literature and language.
The materials in The project Woruldhord were collected from both members of the
public, free to submit any documents related to the period and field of Woruldhord
as well as museums, libraries, academics and scientific societies and resulted in a
History II.O 321
Fig. 3 Prepopulated Form, Crowdsourced project structure, instructional process and workflow
designed by Professor Janet McCalman, Centre for Health & Society, University of Melbourne,
with technical assistance from Sandra Silcot and Claudine Chionh. Funded by the Australian
Research Council
Fig. 4 Sample Entry for an Individual Convict, Crowd sourced project structure, instructional
process and workflow designed by Professor Janet McCalman, Centre for Health & Society,
University of Melbourne, with technical assistance from Sandra Silcot and Claudine Chionh.
Funded by the Australian Research Council
322 L. Orešković
collection of approximately 4,500 various digital objects. Woruldhord’s own
description appropriately terms the project a community collection open to access
of everyone interested in the field (Fig. 5).
Projects like Founders and Survivors and Project Woruldhord exemplify the
frontier of crowd sourcing practices for historical research that employ the general
public for content contribution and aggregate databases of primary sources that are
broader in scope than many projects have achieved in spite of having a larger
professional staff and more funding.
Conceptual Questions and Challenges
Edward L. Ayers, currently the President of University of Richmond, argued in
1999 in his seminal essay on ‘‘The Pasts and Futures of Digital History’’ that
history as an academic discipline underwent ‘‘democratization’’ while meaningful
‘‘democratization of the audience’’ was lacking at that time (Ayers 1999). His hope
was that the ‘‘digital world’’ might be able to, once again, spur innovation in
academic history and it is possible that this time has arrived. History as a 2.0
Scienceand eventually, an OpenScience, has the potentialto involve professional
historians globally moreclosely than ever before,creating platforms for collabo-
rations across disciplines of academichistory. Although historians are increasingly
relying on collaborative and crowd sourcing platforms for their research, the
methods also give rise to questions of the role of historians in the research process.
Shawn Graham, Assistant Professor of Digital Humanities in the Department of
History at Carleton University, together with two students at Carlton, wrote a Case
Fig. 5 Project Woruldhord, University of Oxford, February 27, 2013
History II.O 323
Study on a crowd sourcing project they administered (Graham et al. 2012). In this
case, they raise questions of what the role of historians in projects relying on
crowd sourcing for historical research is as well as claims over authorship of the
work of history that results from crowd sourcing research. Namely, is the research
produced this way primarily historian’s or the crowd’s? In answering this and
similar questions, it is important to keep in mind that all the aforementioned
projects were curated and led by professional historians. The established processes
of source review and criticism in history demand professional expertise and thus,
any crowd research efforts or community contributed content (CCC) projects still
closely rely on guidance and review processes by professional historians (cf.
Howell and Prevenier 2001).
Conclusions
Crowd sourcing and Science 2.0 in academic history holds the potential of
unparalleled access to research resources for historians focusing on a range of
fields, from ‘‘big history’’ to ‘‘microhistory’’ as well as everything in between.
Access to the plethora of perspectives, personal data and documents from indi-
vidual repositories of the past, family histories and communal archives is already
attracting numerous historians, with future trends set to continue in this direction.
Yet there is still a big gap between utilizing Science 2.0 (online resources) for
research and making history an Open Science in terms of publishing practices.
Economic constraints such as a shrinking market for academic appointments in
history are among the most often mentioned reasons for historians’ shying away
from open publishing as academic institutions that recognize such publications are
rare. Hesitance to recognize ‘‘Open Science’’ publishing as reference for academic
track record is understandable, and the hesitance is not only on the part of aca-
demic institutions. The Open Source knowledge and publishing processes already
have considerable drafting and review procedures in place, but there is space for
improvement. While the transparency of review processes that are noticeable in
the ‘‘Open Science’’ are laudable, the critical review of submissions can still be
improved and in accordance with the principles of Open Science, it will likely
keep improving (forever?). As Open Science publishing practices reach the level
of high-quality peer review and editorial processes that traditional academic
publications exhibit, it will be upon academic institutions to begin recognizing
open source knowledge and publications as contributing to the scholarly success
and track record of its faculty. The widely covered ‘‘Memorandum on Journal
Pricing’’ published by Harvard Library’s Faculty Advisory Council in April of
2012 calls on all Harvard Faculty Members in all Schools, Faculties, and Units to,
among other things, make sure their papers are accessible by submitting them to
Harvard’s DASH Open-Access repository as well as consider ‘‘submitting articles
to Open-Access journals, or to ones that have reasonable, sustainable subscription
costs; move prestige to Open Access’’. The Council also asked scientists to sway
324 L. Orešković
journals that restrict access to their content to become more open.8 If impossible to
make these journals Open Access, the members of Harvard Library’s Faculty
Advisory Council recommend to their Harvard peers to consider resigning such
journals’ advisory boards. While calls from Harvard’s Library have been wel-
comed in numerous publications worldwide, the memorandum also raises ques-
tions on the issue of peer and institutional recognition of open source publishing as
relevant to scholarly track record. Many headlines cited ‘‘Memorandum on Journal
Pricing’’ as ultimate proof that current publishing pricing practices are economi-
cally impossible since Harvard, the wealthiest of academic institutions globally,
could not afford them. As Open Science platforms continue to grow in promi-
nence, Harvard should take the first step in encouraging research published as
Open Science to be weighted more equally compared with more traditional pub-
lishing platforms.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
References
Ayers, E.L. (1999). The pasts and futures of digital history. Available at: http://
www.vcdh.virginia.edu/PastsFutures.html.
Causer, T. (2013). Welcome to transcribe bentham. Transcribe Bentham. Available at: http://
blogs.ucl.ac.uk/transcribe-bentham/.
Graham, S., Massie, G., & Feurherm, N. (2012). The heritage crowd project: A case study in
crowd sourcing public history (Spring 2012 version). Available at: http://
writinghistory.trincoll.edu/.
Howell, M. C., & Prevenier, W. (2001). From reliable sources: an introduction to historical
methods. Ithaca, New York: Cornell University Press.
8 Faculty Advisory Council Memorandum on Journal Pricing: http://isites.harvard.edu/icb
History II.O 325
Making Data Citeable: DataCite
Jan Brase
Abstract In 2005 the German National Library of Science and Technology
started assigning DOI names to datasets to allow stabile linking between articles
and data. In 2009 this work lead to the funding of DataCite, a global consortium of
libraries and information institutions with the aim to enable scientists to use
datasets as independently published records that can be shared, referenced and
cited.
Data integration with text is an important aspect of scientific collaboration. It
allows verification of scientific results and joint research activities on various
aspects of the same problem. Only a very small proportion of the original data is
published in conventional scientific journals. Existing policies on data archiving
notwithstanding, in today’s practice data are primarily stored in private files, not in
secure institutional repositories, and effectively are lost to the public. This lack
of access to scientific data is an obstacle to international research. It causes
unnecessary duplication of research efforts, and the verification of results becomes
difficult, if not impossible. Large amounts of research funds are spent every year to
recreate already existing data.
Handling datasets as persistently identified, independently published items is a
key element for allowing citation and long term integration of datasets into text as
well as supporting a variety of data management activities. It would be an
incentive to the author if a data publication had the rank of a citeable publication,
adding to their reputation and ranking among their peers.
The German National Library of Science and Technology (TIB) developed and
promotes the use of Digital Object Identifiers (DOI) for datasets. A DOI name is
used to cite and link to electronic resources (text as well as research data and other
types of content). The DOI System differs from other reference systems commonly
used on the Internet, such as the URL, since it is permanently linked to the object
J. Brase (&)
National Library of Science and Technology (TIB), Hanover, Germany
e-mail: Jan.Brase@tib.uni-hannover.de
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8_26,  The Author(s) 2014
327
itself, not just to the place in which the object is located. As a major advantage, the
use of the DOI system for registration permits the scientists and the publishers to
use the same syntax and technical infrastructure for the referencing of datasets that
are already established for the referencing of articles. The DOI system offers
persistent links as stable references to scientific content and an easy way to
connect the article with the underlying data. For example:
The dataset:
G.Yancheva, N. R. Nowaczyk et al. (2007)
Rock magnetism and X-ray flourescence spectrometry analyses on sediment
cores of the Lake Huguang Maar, Southeast China, PANGAEA
doi:10.1594/PANGAEA.587840 (http://dx.doi.org/10.1594/PANGAEA.587840)
Is a supplement to the article:
G. Ycheva, N. R. Nowaczyk et al. (2007)
Influence of the intertropical convergence zone on the East Asian monsoon
Nature 445, 74-77
doi:10.1038/nature05431 (http://dx.doi.org/10.1038/nature05431)
Since 2005, TIB has been an official DOI Registration Agency with a focus
on the registration of research data. The role of TIB is that of the actual DOI
registration and the storage of the relevant metadata of the dataset. The research
data themselves are not stored at TIB. The registration always takes place in
cooperation with data centers or other trustworthy institutions that are responsible
for quality assurance, storage and accessibility of the research data and the creation
of metadata.
Access to research data is nowadays defined as part of the national responsi-
bilities and in recent years most national science organisations have addressed the
need to increase the awareness of, and the accessibility to, research data.
Nevertheless science itself is international; scientists are involved in global
unions and projects, they share their scientific information with colleagues all over
the world, they use national as well as foreign information providers.
When facing the challenge of increasing access to research data, a possible
approach should be global cooperation for data access via national representatives.
– a global cooperation, because scientist work globally, scientific data are created
and accessed globally.
– with national representatives, because most scientists are embedded in their
national funding structures and research organisations .
The key point of this approach is the establishment of a Global DOI
Registration agency for scientific content that will offer to all researchers dataset
registration and cataloguing services. DataCite was officially launched on
December 1st 2009 in London to offer worldwide DOI-registration of scientific
328 J. Brase
data to actively offer scientists the possibility to publish their data as an inde-
pendent citable object. Currently DataCite has 17 members from 12 countries:
The German National Library of Science and Technology (TIB), the German
National Library of Medicine (ZB MED), the German National Library of
Economics (ZBW) and the German GESIS—Leibniz Institute for the Social
Sciences. Additional European members are: The Library of the ETH Zürich in
Switzerland, the Library of TU Delft, from the Netherlands, the L’Institut de
l’Information Scientifique et Technique (INIST) from France, The technical
Information Center of Denmark, The British Library, the Sedish National Data
Service (SND), the Conferenza dei Rettori delle Università Italiane (CRUI) from
Italy. North America is represented through: the California Digital Library, the
Office of Scientific and Technical Information (OSTI), the Purdue University and
the Canada Institute for Scientific and Technical Information (CISTI). Furthermore
the Australian National Data Service (ANDS) and the National Research Council
of Thailand (NRCT) are members.
DataCite offers through its members DOI registration for data centers, currently
over 1.7 million objects have been registered with a DOI name and are available
through a central search portal at DataCite.1
Based on the DOI registration DataCite offers a variety of services such as a
detailed statistic portal of the number of DOI names registered and resolved.2
In cooperation with CrossRef, the major DOI registration agency for scholarly
articles a content negotiation service has been established that allows persistent
resolution of all DOI names directly to their metadata in XML or RDF format.3
In June 2012 DataCite and the STM association4 signed a joint statement to
encourage publishers and data centers to link articles and underlying data.5
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
1 DataCite Metadata Search: http://search.datacite.org/ui
2 DataCite Statistics: http://stats.datacite.org
3 DOI Content Negotiation: http://www.crosscite.org/cn
4 STM Association: http://www.stm-assoc.org
5 Joint statement from STM and DataCite: http://www.datacite.org/node/65
Making Data Citeable: DataCite 329
About the Authors
Sönke Bartling
PD Dr. med. Sönke Bartling is a researcher in medical imaging sciences (CT/X-
ray) at the German Cancer Research Center in Heidelberg and a board certified
radiologist at the University Medical Center in Mannheim, both in Germany. His
interest in Open Science grew over the last few years, when he realized that several
things aren’t the way they could be. The transition towards More Open Science is
an opportunity for profound changes within the world of research, if driven by
knowledgeable and opinionated researchers. email: soenkebartling@gmx.de.
Roland Bertelmann
Roland Bertelmann is head of the Library ‘‘Wissenschaftspark Albert Einstein’’,
Potsdam, a joint library of several research institutions (GFZ German Research
Centre for Geosciences, PIK Potsdam Institute for Climate Impact Research,
Potsdam branch of Alfred Wegener Institute and IASS Institute for Advanced
Sustainability Studies). He is responsible for Helmholtz Association’s Open
Access Coordination Office and is a member of the working group ‘Open Access’
in the Priority Initiative ‘‘Digital Information’’ of the German Alliance of Science
Organisations. email: roland.bertelmann@gfz-potsdam.de
Peter Binfield
Peter Binfield is Co-Founder and Publisher of PeerJ, a recently launched open
access publisher of PeerJ (a peer-reviewed journal for the biological and medical
sciences) and PeerJ PrePrints (a preprint server). PeerJ aims to make high quality
publication open and affordable to all, and it incorporates several innovations in
regard to open peer review, cutting edge functionality, and new business models.
Prior to founding PeerJ, Pete ran PLOS ONE for 4 years and before that worked at
IoPP, Kluwer Academic, Springer, and SAGE. Pete has a PhD in underwater
holography, which sounds much more interesting than it actually is. email:
pete@peerj.com
S. Bartling and S. Friesike (eds.), Opening Science,
DOI: 10.1007/978-3-319-00026-8,  The Author(s) 2014
331
Mathias Binswanger
Mathias Binswanger is professor for economics at the University of Applied
Sciences Nordwestschweiz in Olten and lecturer at the University of St.Gallen,
Switzerland. He was also visiting professor at the Technical University Freiberg in
Germany, at the Qingdao Technological University in China, and the Banking
University in Saigon (Vietnam). Mathias Binswanger is author of numerous books
and articles in professional journals as well as in the press. His research foci are in
the areas of macroeconomics, financial market theories, environmental economics,
and also in exploring the relation between happiness and income. Mathias
Binswanger is also the author of the book ‘‘Die Tretmühlen des Glücks’’
(Treadmills of Luck), published in 2006, which became a bestseller in Switzerland.
In 2010, his most recent book ‘‘Sinnlose Wettbewerbe - Warum wir immer mehr
Unsinn produzieren’’ (Pointless Competitions – Why we keep on producing more
and more Nonsense) was published.
Jan Brase
Jan has a degree in Mathematics and a PhD in Computer science. His research
background is in metadata, ontologies, and digital libraries. From 2005 to 2012 he
was head of the DOI-registration agency for research data at the German National
Library of Science and Technology (TIB). Since 2009 he has furthermore been
Managing Agent of DataCite, an international consortium with 17 members from
12 countries. DataCite was founded in December 2009 and has set itself the goal of
making the online access to research data for scientists easier by promoting the
acceptance of research data as individual, citable scientific objects. Jan is Chair of
the International DOI foundation (IDF), Vice-President of the International
Council for Scientific and Technical Information (ICSTI), and Co-Chair of the
recently established CODATA Data Citation task group. He is the author of
several articles and conference papers on the citation of data sets and the new
challenges for libraries in dealing with such non-textual information objects.
Sünje Dallmeier-Tiessen
Sünje is currently working as a postdoctoral fellow in the Scientific Information
Service at CERN (www.cern.ch). She came to CERN the end of 2009. Her
research focuses on innovations in digital scholarly communication, i.e. the
integration of research data. She is also particularly interested in the engagement
of research communities in Open Science and possible support solutions.
Beforehand, she worked for the Helmholtz Association’s Open Access
Coordination Office. email: sunje.dallmeier-tiessen@cern.ch
Jörg Eisfeld-Reschke
Jörg Eisfeld-Reschke is a founder of ikosom - ‘‘Institut für Kommunikation in
sozialen Medien’’. ikosom has been researching trends like Crowdfunding,
Crowdinvesting, and Crowdsourcing through several publications and studies. The
332 About the Authors
first Crowdsourcing Report for Germany and several studies on Crowdfunding in
the creative industries has been published by ikosom. Jörg Eisfeld-Reschke is head
of the working group ‘‘Digital Fundraising’’ of the German Fundraising
Association and an alumnus of the Humboldt-Viadrina School of Governance.
Benedikt Fecher
Benedikt Fecher is a doctoral researcher at the Humboldt Institute for Internet and
Society in Berlin, Germany. The focus of his dissertation is on commons-based
peer production in science and motivational aspects of participation in Open
Science.
Martin Fenner
Martin Fenner is a software developer for the publisher Public Library of Science
(PLOS). Before joining PLOS in 2012, he worked as medical doctor and clinical
cancer researcher at Hannover Medical School. He regularly writes about Science
2.0 in his blog ‘‘Gobbledygook’’.
Ralf Floca
Dr. Ralf Floca is a researcher in the fields of medical informatics and image
processing, as well as group leader at the German Cancer Research Center in
Heidelberg, Germany. His group, ‘‘Software Development for integrated
Diagnostic and Therapy’’, facilitates translation within the research program
‘‘Imaging and Radiooncology’’ of the German Cancer Research Center. Ultimately
the goal is to build bridges in order to overcome the gaps between state-of-the-art
research and clinical application, therefore supporting a more personalized, more
effective treatment of cancer. One important step towards this goal is the
correlation and analysis of different data sources in terms of data intensive science.
This is one of many connection points with topics within Open Science that
motivated his contribution to this book. email: r.floca@dkfz.de
Tobias Fries
While studying business and economics and writing his doctoral dissertation at
Witten/Herdecke University in Germany, Tobias Fries was already building up
various different internet companies. Living many years abroad in places like
Buenos Aires and Geneva and working in the internet industry, he became
interested in the field of scale free networks. His current startup companies all
include some elements of scale free network thinking, from when his contribution
is derived.
Sascha Friesike
Dr. Sascha Friesike works as a postdoctoral researcher at the Alexander von
Humboldt Institute for Internet and Society in Berlin, Germany. There he is in
About the Authors 333
charge of the research group ‘‘Open Science’’. He holds an engineering degree
from the Technical University in Berlin and a Ph.D. from the University of
St.Gallen. Prior to his engagement in Berlin, he worked as a researcher at the
Center for Design Research at Stanford University.
Alexander Gerber
Managing Director of the German Research Center for Science and Innovation
Communication, Alexander Gerber teaches science marketing (Technical
University Berlin), science communication (Rhine-Waal University), and
science policy (Rhine Sieg University). He is an elected member of the
Governing Board of Euroscience, and the ESOF Supervisory Board. He chairs
the Editorial Board of Euroscientist and the Stakeholders Assembly of the EU
science communication network PLACES (FP7). Mr. Gerber is also Secretary
General of the German Society for Science & Technical Publishing (TELI).
As an information scientist, he primarily focuses his research and consulting on
interactive media, citizen involvement, communication impact measurements, and
market insight in science and innovation. Before that, he was head of Marketing &
Communications at Fraunhofer ICT Group for 7 years, and editor of InnoVisions
Magazine for 5 years.
Laure Haak
Laurel L. Haak (http://orcid.org/0000-0001-5109-3700) is the Executive Director
of ORCID. She has experience in research evaluation, science policy, editing, and
IT systems development, with positions at Science Magazine, The US National
Academies, Discovery Logic, and Thomson Reuters. Dr. Haak was awarded a PhD
in Neuroscience by Stanford University Medical School, and performed
postdoctoral work at the US National Institutes of Health.
Lambert Heller
Lambert Heller is a librarian (LIS master degree from Humboldt University Berlin)
and social scientist. He heads the ‘‘Open Science Lab’’ team at TIB Hannover, the
German National Library of Science and Technology. Before that he worked for
DFG funded projects on information management and libraries. He then worked as
a subject librarian in Hannover, introducing reference management, publishing, and
social media related services at the TIB. He publishes and teaches about open
knowledge production, (scholarly) communication on the Internet, and library 2.0.
Ulrich Herb
Ulrich Herb holds a diploma in Sociology and is a PhD candidate in Information
Science at Saarland University. He is the Open Access expert at Saarland
University and the project manager at Saarland University and State Library for
projects in the realm of Electronic Publishing and Open Access. He also acts as a
freelance science consultant and science journalist. In 2012 he edited the
334 About the Authors
anthology ‘‘Open Initiatives: Offenheit in der digitalen Welt und Wissenschaft’’
which describes and analyzes claims and initiatives for Openness in scientific and
non-scientific contexts as in, e.g. Open Access, Open Metrics, Open Data or Open
Research Data.
Michael Nentwich
The lawyer and science and technology scholar Michael Nentwich has worked and
studied in Vienna, Bruges, Cologne, Warwick, and Colchester. Since 2006 he has
been the director of the Institute of Technology Assessment (ITA) of the Austrian
Academy of Sciences. In 2003, he published the volume ‘‘Cyberscience. Research
in the Age of the Internet‘‘, and, in 2012, together with René König, ‘‘Cyberscience
2.0. Research in the Age of Digital Social Networks’’.
René König
René König has studied sociology in Bielefeld (Germany) and Linköping
(Sweden). He is currently working on his PhD thesis at Karlsruhe Institute of
Technology0s Institute for Technology Assessment and Systems Analysis (ITAS),
focusing on online search behavior in the context of scientific controversies.
Before this, he was a researcher at the Austrian Academy of Sciences in Vienna
and published the book ‘‘Cyberscience 2.0: Research in the Age of Digital Social
Networks’’ (2012) together with Michael Nentwich.
James MacGregor
Formerly of the Electronic Text Centre at the University of New Brunswick, James
has been a PKP system developer and community coordinator since 2007. He is
involved in various components of the Project, including translation, testing,
documentation, teaching, research, and even some code development occasionally.
Ongoing areas of interest include alternative research metrics, community
outreach and organization, and the ongoing and worldwide push for open access
to scholarly research.
Luka Oreskovic
Luka is a historian, publicist, and political analyst. At Harvard, he studies modern
European political and economic history and is currently working on research of
diplomatic and political history between the US and Yugoslavia for a biography of
Henry Kissinger. He is a columnist for The Moscow Times and his writing has
appeared in the Financial Times, LSE’s EUROPP, UN Chronicle, Acque&Terre,
and others. He advises leading geopolitical risk research and consulting companies
on economic reform and policy.
About the Authors 335
Heinz Pampel
Heinz Pampel studied library and information management at Stuttgart Media
University (HdM). Since 2007 he has worked for the Helmholtz Association’s
Open Access Coordination Office at GFZ German Research Centre for
Geosciences, and the Alfred Wegener Institute for Polar and Marine Research
(AWI). He is a member of several work groups on the permanent access to
scientific information. He is currently working on a doctoral dissertation at the
Berlin School of Library and Information Science at Humboldt-Universität zu
Berlin.
Cornelius Puschmann
Dr. Cornelius Puschmann is a postdoctoral researcher at Humboldt University
Berlin’s School of Library and Information Science, a research associate at the
Alexander von Humboldt Institute for Internet and Society, and a visiting fellow at
the Oxford Internet Institute. Cornelius studies computer-mediated communication
and the Internet’s impact upon society, especially upon science and scholarship.
He is also interested in the role of digital data for various different stakeholders
(platform providers, data scientists, end users).
Kaja Scheliga
Kaja Scheliga studied English and Drama (BA) at Royal Holloway, University of
London, and Computer Science (MSc) at University College London. At the
Humboldt Institute for Internet and Society, she is currently a doctoral researcher
in the field of Open Science.
Thomas Schildhauer
Since April 1999, Prof. Schildhauer has been the founder and director of the
Institute of Electronic Business – the first affiliated institute of the University of
Arts Berlin. In May 2007, he was appointed executive director of the Berlin Career
College at the University of Arts, Berlin.
Since 2012 Prof. Schildhauer has been one of the executive directors of the
Alexander von Humboldt Institute for Internet and Society gGmbH, where he is
responsible for the research topic ‘‘Internet based innovation’’.
Since October 2012, he has served as the scientific director of the digital
consultancy iDeers Consulting GmbH, founded by IEB and Hirschen Group
GmbH.
Michelle Sidler
Michelle Sidler is an Associate Professor of writing studies at Auburn University,
Alabama, USA where she teaches classes in the rhetoric of science and technology,
professional and technical communication, and English composition. Her research
interests include writing, technology, science, and medicine. She has published
336 About the Authors
multiple journal articles and chapters in edited collections, and her co-edited
anthology, Computers in the Composition Classroom, won the 2008 Distinguished
Book Award from the Computers and Composition community.
Dagmar Sitek
Dagmar is head of the library at the German Cancer Research Center in
Heidelberg, Germany. She is a member of the working group ‘‘Research Data’’ in
the Priority Initiative ‘‘Digital Information’’ from the Alliance of German Science
Organisations. email: d.sitek@dkfz.de
Victoria Stodden
Victoria is an assistant professor of Statistics at Columbia University, and affiliated
with the Columbia University Institute for Data Sciences and Engineering. She
completed both her PhD in statistics and her law degree at Stanford University.
Her research centers on the multifaceted problem of enabling reproducibility in
computational science. This includes studying adequacy and robustness in
replicated results, designing and implementing validation systems, developing
standards of openness for data and code sharing, and resolving legal and policy
barriers in disseminating reproducible research. She is the developer of the award
winning ‘‘Reproducible Research Standard’’, a suite of open licensing
recommendations for the dissemination of computational results. She is a co-
founder of http://www.RunMyCode.org, an open platform for disseminating the
code and data associated with published results, and enabling independent and
public cloud-based verification of methods and findings. She is the creator and
curator of SparseLab, a collaborative platform for reproducible computational
research in underdetermined systems.
She was awarded the NSF EAGER grant ‘‘Policy Design for Reproducibility
and Data Sharing in Computational Science.’’ She serves as a member of the
National Science Foundation’s Advisory Committee on Cyberinfrastructure
(ACCI), the Mathematics and Physical Sciences Directorate Subcommittee on
‘‘Support for the Statistical Sciences at NSF’’,and the National Academies of
Science Committee on ‘‘Responsible Science: Ensuring the Integrity of the
Research Process.’’ She is also on several committees in the American Statistical
Association: The Committee on Privacy and Confidentiality, the Committee on
Data Sharing and Reproducibility, and the Presidential Strategic Initiative
‘‘Developing a Prototype Statistics Portal’’. She also serves on the Columbia
University’s Senate Information Technologies Committee.
email: victoria@stodden.net
Kevin Stranack
Kevin is a Learning & Development Consultant with the Public Knowledge
Project at the Simon Fraser University Library. Kevin received his Master of
Library and Information Studies from the University of British Columbia in 2002,
About the Authors 337
and his Master of Adult Education from the University of Regina in 2013.
Vladimir Teif
Dr. Vladimir B. Teif works at the German Cancer Research Center (DKFZ). His
current professional interests include quantitative modeling of gene regulation
processes in chromatin. His research in this field was reported in ‘‘classical-style’’
peer-reviewed publications and highlighted by prestigious young scientist awards
and fellowships. As a hobby, he is also an administrator or moderator for several
scientific internet projects. In his manuscript, ‘‘Science 3.0: The future of science
in the Internet’’, he has critically evaluated current business models behind
Science 2.0 and proposed alternatives that aim to make what he calls ‘‘Science
3.0’’ a more democratic and more effective solution, both for individual scientists
and society.
Ronald The
Ronald The is a designer, information architect, and concept developer. He holds a
Graphic Design Diploma from the Karlsruhe University of Arts and Design and a
Master of Arts in Communication, Planning, and Design from the University of
Design, Schwäbisch Gmünd. Ronald founded the user experience consultancy
company infotectures, based in Heidelberg, Germany. He specializes in user
interface design, web-design/mobile design, and presentations. His involvement in
research and Open Science was stimulated by his position as a guest lecturer at the
Popakademie Mannheim and the University of Design Schwäbisch Gmünd.
Hilger Voss
Consultant at iDeers Consulting, a joint venture between the Hirschen Group and
the Institute of Electronic Business (affiliated institute of the University of Arts
Berlin), where he used to be a member of the research staff. He studied Media
Consulting at TU Berlin.
Karsten Wenzlaff
Karsten Wenzlaff is the CEO and a founder of ikosom - ‘‘Institut für
Kommunikation in sozialen Medien’’. ikosom has been researching trends like
Crowdfunding, Crowdinvesting, and Crowdsourcing through several publications
and studies. The first Crowdsourcing Report for Germany and several studies on
Crowdfunding in the creative industries has been published by ikosom. Karsten
Wenzlaff is an alumnus of the University of Cambridge and the University of
Bayreuth.
John Willinsky
John Willinsky is the Khosla Family Professor of Education at Stanford University
and Professor (Limited Term) of Publishing Studies at Simon Fraser University,
338 About the Authors
where he directs the Public Knowledge Project, which conducts research and
develops scholarly publishing software intended to extend the reach and
effectiveness of scholarly communication. His books include the ‘‘Empire of
Words: The Reign of the OED’’ (Princeton, 1994); ‘‘Learning to Divide the World:
Education at Empire’s End’’ (Minnesota, 1998); ‘‘Technologies of Knowing’’
(Beacon 2000); and ‘‘The Access Principle: The Case for Open Access to Research
and Scholarship’’ (MIT Press, 2006).
About the Authors 339