LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
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Developing a Research Data Policy 
Core Elements of the Content of a Research Data Management Policy 
 
 
This document may be useful for defining research data, explaining what RDM is, illustrating workflows, 
describing benefits and informing about funder requirements. Additionally, legal and ethical issues are 
considered, as well as the relationship with publishers. 
 
Elements Content Issues that may be addressed 
Definitions of Research Data 
 
 
 
 
 
 
  
Define the scope of the policy and 
the definition of data according to the 
definition of the institution 
Consider the elements of this list: 
 Raw data 
 Primary data 
 Inconclusive results 
 Negative results 
 Positive results 
 Processed data 
 Released data 
 Shared data 
 Published data 
 Open access published data 
Institutional elements / aspects 
Roles 
 
Role of: 
The institution / The 
University  
IT services 
Scientific Library 
Research Support Service 
Legal services 
The researcher / the PI 
Data scientist  
Students in PhD degree  
Funding body 
The policy could define roles, in order to 
foster / promote the scientific process. 
 
Concerning the University, consider 
these issues: 
 
 The University will provide training, 
support, advice, and when 
appropriate, guidelines and 
templates for the research data 
management and research data 
management plans. 
 
 The University will provide 
procedures, mechanisms and 
services for storage, backup, 
registration, deposit and retention of 
research data assets to support 
current and future access, during  
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
                                                                                                                                                       Page 2 of 9 
and after completion of research 
projects. 
 
Consider also these issues: 
 
 Any data stored externally, for  
example in an international data 
service or domain repository should 
be registered with the University. 
 
 Research data management plans 
must ensure that research data are 
available for access and re-use 
where appropriate and under 
appropriate policies and conditions. 
 
 Should the University / the funding 
body maintain ownership of all data 
produced by its “own” researchers 
within its “own frame”? 
 
Concerning the IT-Services, consider 
these issues: 
 
 Maintenance of the system(s) 
 Access to the system(s) 
 Preserving integrity of data 
 Authenticity of data 
 Ensure access according to the 
needs of data provider & rights 
management system 
 Provide appropriate tools  
 
Concerning the Repository 
management, consider these issues: 
 
 Preservation of data 
 Management of data 
 Access to data 
 Extraction of data 
 Reuse of data 
 Visualization of data 
 
Concerning the Libraries, consider 
these issues: 
 
 How can Libraries offer a stable 
environment in order to support 
research data management? 
 
 How can they contribute in 
expanding digital collections and 
developing digital infrastructures for 
research data? 
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
                                                                                                                                                       Page 3 of 9 
 
 How can libraries have a central 
role in coordinating the 
implementation of data 
management policies and 
procedures in institutions? 
 
 
Concerning the funding body, consider 
these issues: 
 
 Requirements of the Funder 
 Costs related to long term 
archiving  (deposit) of research 
data 
 Costs related to further reuse of 
data (setting of interfaces and 
visualization issues) 
 
Concerning the data producer / PI , 
consider these issues: 
 
 Ensure quality of content 
 Organization of data 
 Clear file structure 
 Consistent file naming and 
version control 
 Choice of format 
 Documentation of data & 
metadata 
 
Responsibility for research data 
management through a sound research 
data management plan during any 
research project or program lies 
primarily with the Principal Investigators 
(PIs). 
Research Data Lifecycle  
 Creation of Data 
 Preparation and Preservation 
planning 
 Ingest and Deposit 
 Management 
 Assignment of PI 
 Integrity 
 Storage 
 Backup 
 Registration 
 Retention 
 Future access 
 Sharing 
 Extraction 
 Publication 
 Further reuse 
 
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
                                                                                                                                                       Page 4 of 9 
Appropriate storage systems 
for long term preservation 
Who is providing the mechanisms 
and services for storage, backup, 
registration, deposit and retention of 
research data in support of current 
and future access? 
Checklist of relevant issues: 
 
 Data loss  
 Recovery strategies 
 Training 
Legal compliance  
Legal and Ethical issues 
 
Who is responsible for legal 
compliance and security along all 
phases of research data lifecycle?  
 
This concerns data, metadata, 
related applications as well as 
technical systems. 
 
The legitimate interests of the subjects 
of research data must be protected. 
 
Exclusive rights to reuse or publish 
research data should not be handed 
over to commercial publishers or agents 
without retaining the rights to make the 
data openly available for re-use, unless 
this is a condition of funding. 
 
Checklist of relevant issues: 
 
 Copyright issues 
 Distribution rights 
 Exploitation rights (rights to re-
use, rights to publish, etc.) 
 Privacy issues 
 Employee protection 
 
Consider also these issues: 
 
 Who is in charge of negotiations 
with publishers? 
 
 Who is in charge of negotiations 
with funders? 
 
 Who is providing licensing 
mechanisms and models 
(licenses)? 
Sensitive data Who is responsible?  Consider anonymization issues: 
 
 Disclosure agreements 
 Privacy issues 
 
Focus on: 
 
 The issue of re-use of data 
  The long-term preservation 
issue 
  
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
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Data Management Plans 
 
Who is developing DMPs? 
 
Who is reviewing the DMPs? 
 
Who is in charge of the institutional 
DMPs? 
 
Who is responsible for DMP training? 
 
Who will run the first level support? 
Introduce guidelines for submission of 
project proposals. 
 
Should all new research proposals 
(from date of adoption) include research 
data management plans or protocols 
that explicitly address data capture, 
management, integrity, confidentiality, 
retention, sharing and publication? 
Metadata complex Consider also: 
 
 Contextual information 
 Provenance 
 Relations between datasets 
 Annotations 
 Intellectual property rights of 
metadata 
 Technical issues 
 Terminology issues 
 E-accessibility issues 
Information concerning the metadata 
Standards Who is responsible for the 
standardization of processes? 
 
 
Research data may be managed to the 
highest standards throughout the 
research data lifecycle as part of the 
University’s commitment to research 
excellence. 
 
Checklist of relevant issues: 
 
 Quality assurance 
 Metadata 
 Technical processes 
Visualization models Who is responsible: 
 
 …for the definition of 
visualization models? 
 
 …for the extraction of data? 
 
 …for the implementation of the 
visualization? 
 
Academic based teaching Who is in charge of academic based 
teaching related to online learning 
platforms concerning research data? 
Is this an issue at the institutions 
(concerning research data management 
and the re-use of research data)? 
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
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Traditional knowledge  Who is in charge of safeguarding 
traditional knowledge of the 
institution?  
Consider the integration of the 
organization’s culture and traditions. 
Further issues  Research data of future historical 
interest and all research data that 
represent records of the University, 
including data that substantiate 
research findings, will be offered and 
assessed for storage and retention in 
an appropriate national or international 
data service or domain repository, or a 
University repository. 
Content provider entities 
Quality of content of data Who is responsible? Usually the Principal Investigator (the 
data producer). 
Quality of metadata of 
research data 
Who is responsible? Usually the Principal Investigator (the 
data producer). 
Data access Who is responsible?  Consider various levels:  
 from restricted access to open 
access 
Checklist of relevant issues: 
 Legal and ethical issues 
Data sharing Who is responsible?  Checklist of relevant issues: 
 Legal and ethical aspects 
 Technical aspects 
 Management aspects (e.g. 
metadata) 
Data release Who is responsible?  Checklist of relevant issues: 
 Legal and ethical aspects 
 Technical aspects 
 Management aspects 
Selection of Long Term 
Archiving /Sharing / Release 
Who is responsible? 
 
Who will take care of this data? 
 
Who will hand over this data to 
whom/where? 
 
Who will decide about sharing and 
releasing? 
 
Who will decide about the forms of 
visualization?  
Focus on the issue of re-use and future 
visualization of data. 
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
                                                                                                                                                       Page 7 of 9 
Re-use Who is responsible: 
 
 …for the assignment of correct 
metadata? 
 
 …for clearing licenses of use? 
 
 …for ethical issues? 
 
 …for technical issues? 
Consider the hand over to: 
 
 re-using party  
 
 additional software applications for 
further use (for example: Digital 
Humanities, E-learning systems, 
visualization and representation 
tools) 
Data retention 
Data preservation 
Who will decide about data 
retention? 
 
Who will take care of the 
preservation processes? 
 
Who is responsible for the technical 
support? 
 
General topics concerning technical and non-technical aspects 
Format quality and 
preservation 
Concerning the management of the 
format :  
Who is responsible for it? 
Are there any formats or content 
models recommended by the policy?  
Quality assurance Who is responsible for the digital 
objects / data during the different 
phases of the life cycle of the object 
from the technical and non-technical 
points of view?  
See also the phases of a Digital 
Workflow Model (Image I: Model of 
Workflow) representing the phases and 
describing the different roles 
(production, preparation, pre-ingest, 
ingest, management inside an archiving 
system, extraction, export, visualization) 
 
  
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
                                                                                                                                                       Page 8 of 9 
Image I: Model of Workflow by Raman Ganguly (http://phaidra.univie.ac.at/o:387249) 
This graph shows a digital workflow model starting at the left side with the content producer and ending on the right 
side with the visualization of the content. Data management plans accompany the processes along the entire life 
cycle. 
 
 
 
 
Image II: Data from Research Processes: from raw data to open access published data, by Raman 
Ganguly (http://phaidra.univie.ac.at/o:387241) 
 
 
Phases of the Data Preservation Process 
Pre-Ingest: In this step, the responsibility lies 
on the Content Creator. In this phase will be 
determined which data is to be archived, their 
quality, and under what user license will be 
granted their re-use. 
Ingest: In this step, the Repository Manager 
takes over the responsibility. To be able to 
archive the data, a data conversion process 
needs to take place first. The conversion 
process should not affect the quality of the 
data. 
Management: Ensures security and access to 
the data, in the quality required for their re-
usability. 
Re-Use: Delivery to external applications 
specialized on reusing the data provided. The 
data can be used in e-Learning systems, 
Presentations, or any other additional research 
processes. Here the responsibility goes to the 
Content Provider of the external applications. 
 
Levels and Layers of Data 
 
Raw Data: Primary data derived from the 
research processes  
 
Processed Data: Data derived from 
analytical processes. These processes 
could provide results, including negative or 
inconclusive results.  
 
Shared Data: Data that will be shared with 
others.  
 
Published Data: Publicly available data  
 
Open Access Published Data: 
Publications under any Open Access 
modality. 
LEARN (Leaders Activating Research Networks) | Authors: University of Vienna – WP3           Status: 9 November 2015 | Version 2   
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139. 
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Useful links and further related documents:  
The following list of useful links and further related documents has been created in order to deepen knowledge about 
Research Data Management. It comprehends references to Research Data Management policies, Data Management 
Plans. 
The University of Edinburgh Research Data Management Policy 
The list is work in progress