Open science, open data
Lidia Stępińska-Ustasiak
 
Open Science Platform, ICM, University of Warsaw
“Open science is the idea that scientific knowledge of all kinds should 
be openly shared as early as is practical in the discovery process.”
Michael Nielsen
Outline 
• Openess in science
• Open research data
• Definitions
• Formats
• Levels of openess 
• Depositing data 
• Open Access and open research data pilot in Horizon2020
• CC licences in science
• Research Data Managment 
4th Paradigm
• Empirical  - describing natural 
phenomena (last millenia)
• Theoretical - building models and 
generalisations (last centuries)
• Computational - simulating 
complex phenomena (last 
decades)
• Data Exploration “data-intensive” 
scientific discovery (last years)
Scholarly communication is changing
Open 
science
Open access
Open data
Open source
Open educational 
resources
Open peer review
Open notebook 
science
Citizen science
What does the EC understand by the OA?
• Online access at no charge to the user
• To peer reviewed scientific publications
• To scientific data
• Two main publishing business models 
• Self archiving – deposit manuscripts & 
immediate/delayed OA provided by 
autho (green OA)
• OA publishing – costs covered & 
immediate OA provided by publisher 
(gold model) e.g. „author pay” model 
(APC)
Objective
• The EC goal is to optimize the impact of research in Europe. 
Expected benefits:
• Better and more efficient science (Science 2.0)
• Economic growth
• Broader, faster, more transparent and equal access for the benefit of 
researchers, industry and citizens. (Responsible Research and 
Innovations)
European Commission (2013):
„Open access can be defined as the practice of providing on-line access to 
scientific information that is free of charge to the end-user and that is re-usable.
In the context of research and innovation, 'scientific information' can refer to
(i) peer-reviewed scientific research articles (published in scholarly journals)
or
(ii) research data (data underlying publications, curated data and/or raw data).”
Guidelines on Open Access to Scientific Publications and Research Data 
in Horizon 2020. Version 16 December 2013.
Intellectual Property Rights in H2020
Scientific information
Articles and books
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Research data
„the recorded factual material commonly accepted in the 
scientific community as necessary to validate research findings”
„Research data is data that is collected, observed, or created, 
for purposes of analysis to produce original research results.”
„Data is anything that has been produced or created during research.”
Other definitions of research data
„…the recorded factual material commonly accepted in the 
scientific community as necessary to validate research findings.”
„Anything & everything 
produced in the course of 
research”
Digital Curation Center 
• Numerical data
• Text documents, lab notes
• Questionnaires, responses, transcripts
• Audiotapes, videotapes
• Photographs, films 
• Artefacts, specimens, samples
• Models, algorithms, scripts
• Simulation results
• Methodologies and workfows
Examples of research data:
Numerical data
Text documents, lab notes
Questionnaires, responses, transcripts
Audiotapes, videotapes
Photographs, films 
Artifacts, specimens, samples
Models, algorithms, scripts
Simulation results
Methodologies and workfows
Examples of research data
The focus [in the context of open access] is on research data 
that is available in digital form.
“Open data and content can be freely used, 
modified, and shared by anyone for any purpose.”
Open Knowledge Foundation
The Open Definition:
What is open data?
What is open data?
 make your stuff available on the Web (whatever format) under an open licence
 make it available as structured data (e.g. Excel instead of a scan of a table)
 use non-proprietary formats (e.g. CSV instead of Excel)
 use URIs to denote things, so that people can point at your stuff
 link your data to other data to provide context
Tim Berners-Lee, 5-star Open Data, 5stardata.info
This model is concerned with removing technical barriers to data re-use.
Formats
Type of data Reccomended Avoid for data sharing
Tabular CSV, TSV, SPSS portable Excel 
Text Plain text, HTML, RTF PDF/A only if layout matters Word
Media Container: MP4, Ogg Codec: Theora, Dirac, FLAC Qiucktime
Images TIFF, JPEG2000, PNG Gif, JPG
Structured data XML, RDF RBDMS
Major sources of open data
Statistical data
Financial
Cultural
Climate
Environment
Transport
…
Public data Research data
Specialized data repositories
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Protein Data Bank 
– since 1971
Oxford Text Archive 
– since 1976
GenBank 
– since 1982
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What about data for which no 
specialized repositories exist?
➞ Broad or general data repositories
➞ Data journals


ZENODO
• Zenodo is a free-to-use data archive, run by CERN 
•  It accepts any kind of data, from any academic discipline 
•  It is generally preferable to store data in a disciplinary data centre, 
but not all scholarly subjects are equally well served with data 
centres, so this may make for a useful fallback option 
• See http://zenodo.org/ for more details
Should all data be open?
Should all data be open? No.
But data existence should always be open:
• Allows discovery & negotiation on use
• Avoids pointless replication
Slide adapted from Kevin Ashley, DCC, CC-BY
Privacy protection (human subjects!)
National security issues
Protection of endangered species, of archaeological sites, etc.
Interference with commercialization plans
https://www.youtube.com/watch?v=RGtPVIBmFBI&feature=youtu.be
Why data sharing is worth your attention?
• Digital technology now used very widely in research, and is enabling 
new research and scientific paradigms 
• Research funders and publishers know that digital research data can 
be expensive to produce but inexpensive to share, making reuse more 
feasible and desirable 
• The challenge is to ensure digital research findings can be reproduced 
and cited
The long tail of research data
Size of 
the data
Number of 
datasets
Long-tail of data:
all the data produced by small research 
groups and individual researchers
Big Data
„To me, the really difficult challenge is (…) the variety. The heterogeneity, as you put it. 
And we see this particularly in what they call the long tail of data (…)”
Mark Parsons, Research Data Alliance
Excercise 
Objections to data sharing 
How to answer to the most 
commonly heard 
objections to data 
sharing?
1. My data in not of 
interest or use to anyone 
else.
Replies (1)
• It is! Researchers want to access data from all kinds of studies, 
methodologies and disciplines. It is very difficult to predict which 
data may be important for future research. Your data! May also be 
essential for teaching purposes. Sharing is not just about archiving 
your data but about sharing them amongst colleagues. 
2. I want to publish my 
work before anyone else 
sees my data.
Replies (2)
• Data sharing will not stand in the way of you first using your data 
for your publications. Most research funders allow you some period 
of sole use, but also want timely sharing. Also remember that you 
have already been working with your data for some time so you 
undoubtedly know the  data better than anyone coming to use 
them afresh. If you are still concerned you can embargo your data 
for a specific period of time.
3. If I ask my respondents for 
consent to share their data, then 
they will not agree to participate in 
the study.
Replies (3)
• Don’t assume, that participants will not participate because data 
sharing is discussed. Talk to them, they may be less reluctant than you 
might think or less concerned over data sharing. Make it clear that is 
entirely their decision. Explain that data sharing means and why it 
might be important. 
• If you not have asked for permission during research you can return to 
gain retrospective permission from participants. 
4. I’m doing quantitative research 
and the combination of my 
variables discloses my participants’ 
identities. 
Replies (4)
• Quantitative data can by anonymised trough processes of 
aggregation, top coding, removal of variables or controlled access 
to certain variables. 
5. I have collected audio-visual 
data and I cannot anonymise them, 
therefore I cannot share these 
data.
Replies (5)
• Visual data can be anonymised trough blurring faces or distorting 
voices but it can be time consuming. It can mean losing much of 
the value of the data. It is better to ask for consent to share data 
from participants to share data in unanonymised form or / and 
control access to the data. 
6. I’m doing highly sensitive 
research. I cannot possibly make 
my data available for others to see.
Replies (6)
• Ask respondents and see if you can get consent for sharing in the 
first instance. Anonymisation procedures can help to protect 
identifying information. If this two tactics are not apropriate. Than 
consider controlling access to tha data or embargoing for a period 
of time. 
7. It is impossible to 
anonymise my transcripts 
as too much information is 
lost. 
Replies (7)
• Sometimes access control on the data may be a better solution 
than anonymisation if too much useful information would be lost. 
8. My data collection 
contains the data which I 
have purchesed and it 
cannot be made public. 
Replies (8)
• It is important to know who holds the copyright to the data you are 
using and to obtain relevant permissions. You need to be aware of 
the licence conditions of the data you are using and what you can 
and cannot do with the data. 
9. Other researchers 
would not understand my 
data at all or may use 
them for a wrong purpose. 
Replies (9)
• Producing good documentation and providing contextual 
information for your research project should enable other 
researchers to corretly use and understand your data. 
10. There is IPR in the 
data.
Replies (10)
• This should not be a problem if you seek copyright permission from 
the owner of the intellectual property rights.  This is best done 
early on in the research project but also may be done 
retrospectively. 
Role playing exercise derived from the UKDA’s “Potential barriers to data sharing – 
with suggested solutions” (CC-BY-NC-SA) The original is available from http://data-
archive.ac.uk/create-manage/training- resources

Open Access in Horizon 2020
Mandate on open access to publications:
„Under Horizon 2020, each beneficiary must ensure open access 
to all peer-reviewed scientific publications relating to its results.”
Open Access in Horizon 2020
In order to comply with this requirement, beneficiaries must, at the very least, 
ensure that their publications, if any, can be read online, downloaded and printed. 
However, as any additional rights such as the right to copy, distribute, search, link, 
crawl, and mine increase the utility of the accessible publication, beneficiaries 
should make every effort to provide for as many of them as possible. 
Open Access in Horizon 2020
Open research data pilot:
„The Open Research Data Pilot applies to two types of data:
1) the data (…) needed to validate the results presented in scientific 
publications as soon as possible;
2) other data (…) as specified and within the deadlines laid down in the 
data management plan.”
„Participating projects are required to deposit the research data 
described above, preferably into a research data repository.”
Open Access in Horizon 2020
Open research data pilot:
„The Open Research Data Pilot applies to two types of data:
1) the data (…) needed to validate the results presented in scientific 
publications as soon as possible;
2) other data (…) as specified and within the deadlines laid down in the 
data management plan.”
„Participating projects are required to deposit the research data 
described above, preferably into a research data repository.”
• Only for projects from 7 s lected areas.
• You can opt-in, and you can also opt-out.
Open Access in Horizon 2020
Participating projects are required to deposit the research data described 
above, preferably into a research data repository.
As far as possible, projects must then take measures to enable for third 
parties to access, mine, exploit, reproduce and disseminate (free of charge 
for any user) this research data. 
One straightforward and effective way of doing this is to attach a Creative 
Commons Licence (CC-BY or CC0 tool) to the data deposited.
H2020 - areas participating in the data pilot 
• Future and Emerging Technologies
• Research infrastructures – part e-Infrastructures
• Leadership in enabling and industrial technologies – Information and Communication 
Technologies
• Societal Challenge: 'Secure, Clean and Efficient Energy' – part Smart cities and 
communities 
• Societal Challenge: 'Climate Action, Environment, Resource Efficiency and Raw 
materials' – except raw materials
• Societal Challenge: 'Europe in a changing world – inclusive, innovative and refective 
Societies' 
• Science with and for Society Projects in other areas can participate on a voluntary basis 
Reasons for opting out
•If results are expected to be commercially or industrially exploited 
•If participation is incompatible with the need for confidentiality in 
connection with security issues
•If incompatible with existing rules on the protection of personal data
•Would jeopardise the achievement of the main aim of the action
•If the project will not generate / collect any research data 
•If there are other legitimate reasons to not take part in the Pilot
Can opt out at proposal stage OR during lifetime of project.
Should describe issues in the project Data Management Plan. 
Slide by Sarah Jones, adapted by Kevin Ashley, DCC, 
CC-BY
Legal aspects
CC licences 
What are Creative Commons Licenses?
What are Creative Commons Licenses?
BY – Attribution
SA – Share Alike
NC – Non-commercial
ND – No derivatives
Public Domain
Public Domain Mark Public Domain Dedication
Gratis
open access
Libre
open access
the right to read the right to read 
and re-use
CC0 is easy to use
You don’t need to know what rights actually apply to your dataset 
(what is protected?)
 you should know this for CC-BY (and other CC licenses)
Why CC0 for research data?
BY: Datasets are particularly prone to attribution stacking, where a derivative work 
must acknowledge all contributors to each work from which it is derived, no matter 
how distantly. 
SA:  The problem with copyleft licences is they prevent the licensed data being 
combined with data released under a different copyleft licence: the derived dataset 
would not be able to satisfy both sets of licence terms simultaneously.
NC: Non-commercial licences may have wider implications than intended due to 
the ambiguity of what constitutes a commercial use.
From:
Ball, A. (2014). ‘How to License Research Data’. DCC How-to Guides. Edinburgh: Digital Curation Centre. 
Available online: http://www.dcc.ac.uk/resources/how-guides/license-research-data#x1-4000
Open Access in Horizon 2020
Open research data pilot:
„The use of a detailed data management plan covering individual 
datasets is required for funded projects participating in the Open 
Research Data Pilot.”
Research data 
management
…an active approach towards handling data 
throughout all stages of the research data lifecycle.
What is Research Data Management?Create data
Document
Analyze, 
process, use
Preserve
Share
Re-use
Research data 
lifecycle
Active data management 
• Data management planning
• Creating data
• Documenting data
• Accessing & using data
• Storage and backup
• Selecting what to keep
• Sharing data
• Data licencing and citation
• Preserving data
• …
Digital Curation Center
1. Legal requirements to retain the data beyond its immediate use.
2. Scientific or Historical Value: this involves inferring anticipated future use.
3. Uniqueness: does it duplicate existing datasets?
4. Non-Replicability: would it be feasible to replicate the data? (high costs, one-time events)
5. Potential for Redistribution: the reliability, integrity, and usability of the data files (do 
formats meet technical criteria? are IPRs addressed?)
6. Economic Case: costs for managing and preserving the data are justifiable when assessed 
against evidence of potential future benefits.
7. Full documentation: documentation is comprehensive and correct.
Data Selection – guidelines
Based on:
Whyte, A. & Wilson, A. (2010). "How to Appraise and Select Research Data for Curation". 
DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: 
http://www.dcc.ac.uk/resources/how-guides/appraise-select-data
File formats - tactic
If you want your data to be re-used and sustainable in the long-term, 
you typically want to opt for open, non-proprietary formats. 
• Do you have a choice or do the instruments you use only export in 
certain formats? 
•  What is common in your field? Try to use something that is accepted 
and widespread 
• Does your data centre recommend formats? If so it’s best to use 
these.
Data selection…
…depends on what researchers want to do with their data;
what they are allowed to do with the data;
and what the institution can afford to do with the data.
Slide adapted from Kevin Ashley, DCC, CC-BY
A brief plan that outlines 
• what data will be created and how
• how it will be managed (storage, back-up, access…)
• plans for data sharing and preservation
What is a DMP?
Slide from Kevin Ashley, DCC, CC-BY
Lots of research funders require DMP
Why develop a DMP?
DMPs are useful whenever researchers are creating data to:
• Make informed decisions to anticipate and avoid problems 
• Avoid duplication, data loss and security breaches 
• Develop procedures early on for consistency
• Ensure data are accurate, complete, reliable and secure
• Save time and effort
Slide adapted from Kevin Ashley, DCC, CC-BY
Five common themes
1. Description of data to be collected / created
  (i.e. how will it be collected, content, type, format, volume...)
2. Documentation & metadata
(standards and formats, structure of file naming, etc.)
3. Ethics and Intellectual Property
 (highlight any restrictions on data sharing e.g. privacy, confidentiality)
4. Plans for data sharing and access
  (i.e. how, when, to whom)
5. Strategy for long-term preservation
www.dcc.ac.uk/resources/data-management-plans/checklist 
Slide adapted from Kevin Ashley, DCC, CC-BY
Advice on writing DMPs
• Keep it short and simple, but be specific
• Seek advice - consult and collaborate
• Base plans on available skills and support 
• Make sure implementation is feasible
• Remember: plans change and should evolve
For better understanding of your data
• Think about what is needed in order to find, evaluate, understand, 
and reuse the data. 
• Have you documented what you did and how? 
• Did you develop code to run analyses? If so, this should be kept and 
shared too. 
• Is it clear what each bit of your dataset means? Make sure the units 
are labelled and abbreviations explained. 
•  Record metadata so others can find your work e.g. title, date, 
creator(s), subject, format, rights…,
Which data need to be kept
• Could this data be re-used 
• Must it be kept as evidence or for legal reasons 
• Should it be kept for its potential value 
• Consider costs – do benefits outweigh cost? 
• Evaluate criteria to decide what to keep 
• 5 steps to decide what data to keep www.dcc.ac.uk/resources/how-
guides/five-steps-decide-what-data-keep
Where to deposit?
• Does your publisher or funder suggest a repository? 
•  Are there data centres or community databases for your discipline?
• Does your university offer support for long-term preservation?

Excercise
Define and select your data
Choose one specific research project and for this project:
1. Define what data will be generated (all of it!)
2. What would you select for preservation?
3. How would you share your data?
There is no such thing as ideal data.
Thank you for your attention
Contact: 
l.stepinska-ustasiak@icm.edu.pl