This document summarizes a presentation about using the LEARN project's research data management policy and guidance. The LEARN project involved 5 partners across Europe working from 2015-2017 to develop best practices for RDM. It conducted workshops, published case studies and a toolkit. The presentation discusses developing an RDM policy, including understanding the progression from taboos to principles to policies to rules. It provides an example outline for a model RDM policy covering aspects like responsibilities and validity. The goal is to produce guidance that research institutions can tailor to their own needs to enhance coordination and alignment of RDM practices.

1. COAR Research Data
Management Session
Paolo Budroni
The LEARN Project
Using the
LEARN RDM Policy
& Guidance
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
COAR Annual Meeting, Venice, May2017

2. The LEARN
Project
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

3. LEaders Activating Research Networks
• 5 partners
• UCL (lead)
• University of Barcelona
• University of Vienna
• LIBER
• ECLAC – UN Commission for
Latin America and the
Caribbean
• June 2015 – May 2017
http://www.learn-rdm.eu

4. At A Glance
1
0Workshops
& Events
421
Twitter followers
500
average monthly website users
65
presentations
31
blog posts

5. Workshops
Audience: Policymakers,
librarians, funders, publishers,
SMEs and more…
Attendance: 70+ people on
average attended each main
workshop vs KPI of 50
• 6 main Workshops
• 3 mini Workshops
• 1 Open Science Café

6. Presentations
65 Presentations to
Policy Makers, Higher
Education
Professionals,
Librarians, Data
Specialists

7. Audiences Reached

8. 23 Best Practice Case Studies in 8
sections
Policy and Leadership
Advocacy
Subject approaches
Open Data
Research Data Infrastructure
Costs
Roles, Responsibilities, Skills
Tool development
LEARN Toolkit
http://www.learn-rdm.eu

9. Case Study 23:
Paul Ayris & Ignasi Labastida: Surveying your level of preparation
for research data management
Take the survey -
http://learn-rdm.eu/en/rdm-
readiness-survey/

10. Toolkit Part 3: LEARN Executive Briefing
http://www.learn-rdm.eu

11. RDM Policies
and Policy
Alignement
1. About Research Data
2. Understanding Policies From
Taboos to Policies
3. Model Policy for Research Data
Management (RDM) at Research
Institutions/Institutes
4. Guideline, Further Developments
and Outreach
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

12. World of data
Raw data (primary data)
World of data
Raw data (primary data)
Processed Data
Negative Results
Processed Data
Negative Results
Processed DataProcessed Data
Processed Data
Inconclusive
Results
Processed Data
Inconclusive
Results
Processed DataProcessed Data
Processed DataProcessed Data
Shared
Data
Shared
Data
Processed DataProcessed Data
Positive resultsPositive results
Positive resultsPositive results
Shared
Data
Shared
Data
Shared
Data
Shared
Data
Pub.
Data
Pub.
Data
OA
Pub.
Data
Pub.
DataReleased
Data
Released
Data
Different levels of processing of data
Model for digital archiving
Ensuring legal and ethical compliance is key issue in this context
 Strata of research data
 Restricted Data
Open data
Published data
Open access published data

13. Mission
• Produce a Policy and a
Guidance which can be
tailored by any University or
Research Institution to meet
their needs
• Enhance Policy Coordination
& Alignment

14. 2
Understanding
Policies:
From Taboos to
Policies

15. Going over to related Principles
Going over to the creation of a Policy
Starting with some Taboos
Looking for an Ontology From Taboos to Policies
11
22
33
Going over to Rules, Legislations and Regulations
(canons, norms, guidelines)
44

16. Taboo
A taboo is something, which is forbidden or
disapproved of, or placed under a social
prohibition.
“Thou shalt not delete scientific data“
“Thou shalt not destroy infrastructures”
Usually a negative assertion.
In society and academic environment taboos are
accepted only if they are just a few.

17. Principle
A principle is a fundamental truth or proposition that
serves as the foundation for a system of belief or behaviour
or for a chain of reasoning.
Research data are to be preserved
Format: positive assertion:
Derivation for an academic institution or an academic
service provider: beliefs governing the organization’s
(body) behaviour.
Research data are to be kept FAIR - Findable, Accessible,
Interoperable, Reusable.
Research data infrastructures are to be kept accessible

18. Policies/ 1
A policy is…
- a course or principle of action adopted or proposed by an
organization (or individual);
“The Institution [name XY] will preserve its research data
infrastructure always accessible and free to its members
according to the FAIR principles”
- a development generated from the bottom (resulting from the
action of individuals);
- a development generated from the top (resulting from the
action of an executive);
N.B.: the original Greek ideal of “the projection of the volition of an
individual” is expressed through the politeia and therefore included in this
principle of action.

19. Policies /2
General assumptions concerning policies:
•A single Policy: the policy is a single entity, it should not be in
competition with other policies
•Policy offers the frame for the generation of Rules
•Policy is usually accepted after a while
•Creators of Policy do not want to modify it
•“Policies lag behind” (usually policies are oriented to the past.
Most Policies are reflections of existing conventions)
•Valid for long periods of time – and there is an end (expiry date)

20. Rules, Regulations/ 1
Rules are prescribing conducts or actions. They are generated
by the founder of “orders”. Characteristics of rules are:
- There may exist “lots of rules”: the number of rules can be
„endless“.
- Rules are not always clear (they often need interpretation
according to the situation).
- Rules are usually accepted, but often imposed procedures.
- It is allowed to modify Rules by definition.
- Rules are only valid during a specified period of time.
- The Law is an expression of rules - Law (usually written order
or direction or legal precept or doctrine)

21. Rules, Regulations /2
Example:
“Our University will maintain accessible our
infrastructure each day from 9:00 a.m. to 12:00
a.m and offer support only on Friday from 7:00
a.m. to 8:00 a.m. The research data, that are
publicly funded are to be kept free and accessible
to all members of our University each Sunday,
from 9:00 to 12:00 a.m.“

22. From Taboos to Policies
Taboos Principles Policies Rules
Negative assertion
few
“You shall not delete
scientific data”
“Youl shall not
destroy
infrastrcutures”
Positive assertion
more than „few“
“Research data are
to be kept FAIR -
Findable,
Accessible,
Interoperable,
Reusable.”
“Research data
infrastructures are
to be kept
accessible”
A course or principle
of action. Policy offers
the frame for the
generation of Rules,
should not be in
competition with
other policies
“The Institution
[name XY] will
preserve its research
data infrastructure
always accessible and
free to its members
according to the FAIR
principles”
Rules prescribe conducts
or actions; define who
what when and where
should be done according
to the Policy
“Our University will
maintain accessible our
infrastructure each day
from 9:00 a.m. to 12:00
a.m and offer support
only on Friday from 7:00
a.m. to 8:00 a.m. “

23. A few words about KPIs
KPIs
Generally KPIs follow
policies and resulting
legislations and regulations,
and may be created
according to the above
mentioned workflow
KPIs offer some metrics
with the aim to create a
structure of reference for
evaluating the effectiveness
existing policies/rules
As stated, KPIs are mainly a response to some
existing requirements resulting from policies or
other kinds of rules:
- They facilitate comparison
- They are expressions of “values”
- They are instruments for creating statistics
- KPIs are element of a policy, because attempt
to steer future developments through their
introduction
- They are important tools for management

24. About KPIs/2
Conclusion: the use and adoption of KPIs is
related to future developments that are
depending on policies. KPIs will then offer a
“screenshot” of existing situations, may also
be used as a regulative instrument for future
developments.

25. Why these differentiations?
• It is important to identify the different
semantic levels
• Understand the differences between
Taboos, Principles, Policies, Rules and
Regulations
• Understanding of the semantic
hierarchy is useful in order to produce
appropriate guidelines

26. 3
The
Model
Policy

27. How we continued
• Creation of first model policy and guidance
• Continuous involvement of LEARN Partners
• Discussion of policy insights and results at 5
Partner Workshops in London, Vienna, Helsinki,
Santiago de Chile and Barcelona
• Co-operation in Mini-Workshops in the Latin
America area to compare and standardise
terminology and to foster policy alignment
• 12/2016 – 02/2017: Peer review process of
Model Policy and Guidance

29. 1. Preamble
2. Jurisdiction
3. Intellectual Property
Rights
4. Handling Research Data
5. Responsibilities, Rights,
Duties
5.1. Researchers are responsible for:...
5.2. The [name of research
institution] is responsible for:…
6. Validity

30. 1. Preamble
The [name of research institution] recognizes the
fundamental importance of research data and
the management of related administrative
records in maintaining quality research and
scientific integrity, and is committed to pursuing
the highest standards. The [name of research
institution] acknowledges that correct and easily
retrievable research data are the foundation of
and integral to every research project. They are
necessary for the verification and defence of
research processes and results. RDM policies
are highly valuable to current and future
researchers. Research data have a long-term
value for research and academia, with the
potential for widespread use in society.

31. 2. Jurisdiction
This policy for the management of research data
applies to all researchers active at the [name
of research institution]. The policy was
approved by the [dean/commission/authority] on
[date]. In cases when research is funded by a
third party, any agreements made with that
party concerning intellectual property rights,
access rights and the storage of research
data take precedence over this policy.

32. 3. Intellectual Property Rights
Intellectual property rights (IPR) are defined in
the work contract between a researcher and his
or her employer. IPRs might also be defined
through further agreements (e.g. grant or
consortial agreements). In cases where the IPR
belong to the institution that employs the
researcher, the institution has the right to choose
how to publish and share the data.

33. 4. Handling research data (1/2)
Research data should be stored and made available for use in a
suitable repository or archiving system, such as [name of
institutional repository/archiving system, if applicable]. Data should be
provided with persistent identifiers.
It is important to preserve the integrity of research data. Research
data must be stored in a correct, complete, unadulterated and
reliable manner. Furthermore, they must be identifiable,
accessible, traceable, interoperable, and whenever possible,
available for subsequent use.
In compliance with intellectual property rights, and if no third-party
rights, legal requirements or property laws prohibit it, research
data should be assigned a licence for open use.

34. 4. Handling research data (2/2)
Adherence to citation norms and requirements regarding publication and future research
should be assured, sources of subsequently-used data explicitly traceable, and
original sources can be acknowledged.
Research data and records are to be stored and made available according to intellectual
property laws or the requirements of third-party funders, within the parameters of
applicable legal or contractual requirements, e.g. EU restrictions on where
identifiable personal data may be stored. Research data of future historical interest
and the administrative records accompanying research projects should also be
archived.
The minimum archive duration for research data and records is 10 years after either the
assignment of a persistent identifier or publication of a related work following
project completion, whichever is later.
In the event that research data and records are to be deleted or destroyed, either after
expiration of the required archive duration or for legal or ethical reasons, such
action will be carried out only after considering all legal and ethical perspectives.
The interests and contractual stipulations of third-party funders and other
stakeholders, employees and partner participants in particular, as well as the
aspects of confidentiality and security, must be taken into consideration when
decisions about retention and destruction are made. Any action taken must be
documented and be accessible for possible future audit.

35. 5. Responsibilities, Rights, Duties
This policy for the management of research data
applies to all researchers active at the [name of
research institution]. The policy was approved by
the [dean/commission/authority] on [date]. In
cases when research is funded by a third
party, any agreements made with that party
concerning intellectual property rights, access
rights and the storage of research data take
precedence over this policy.

36. 5.1. Researchers are responsible for:
a)Management of research data and data sets in adherence with principles and
requirements expressed in this policy;
b)Collection, documentation, archiving, access to and storage or proper destruction of
research data and research-related records. This also includes the definition of protocols
and responsibilities within a joint research project. Such information should be included in
a Data Management Plan (DMP), or in protocols that explicitly define the collection,
administration, integrity, confidentiality, storage, use and publication of data that will be
employed. Researchers will produce a DMP for every research project.
c)Compliance with the general requirements of the funders and the research institution;
special requirements in specific projects should be described in the DMP;
d)Planning to enable, wherever possible, the continued use of data even after project
completion. This includes defining post-project usage rights, with the assignation of
appropriate licences, as well as the clarification of data storage and archiving in the case
of discontinued involvement at the [name of university/research institution];
e)Backup and compliance with all organisational, regulatory, institutional and other
contractual and legal requirements, both with regard to research data, as well as the
administration of research records (for example contextual or provenance information).
f)To ensure appropriate institutional support, it is required that new research projects are
registered at the proposal stage at [name of research institution/central body].

37. 5.2. The [name of research institution] is
responsible for:
a)Empowerment of organisational units, providing appropriate means and
resources for research support operations, the upkeep of services,
organizational units, infrastructures, and employee education;
b)Support of established scientific practices from the beginning. This is possible
through the drafting and provision of DMPs, monitoring, training, education and
support, while in compliance with regulations, third-party contracts for research
grants, university/institutional statutes, codes of conduct, and other relevant
guidelines;
c)Developing and providing mechanisms and services for the storage,
safekeeping, registration and deposition of research data in support of current
and future access to research data during and after the completion of research
projects;
d)Providing access to services and infrastructures for the storage, safekeeping
and archiving of research data and records, enabling researchers to exercise
their responsibilities (as outlined above) and to comply with obligations to third-
party funders or other legal entities.

38. 6. Validity
This policy will be reviewed and updated as
required by the head of/the director of the [name
the research institution] every [two years].

39. Published in LEARN Toolkit in April
2017 http://learn-rdm.eu/wp-
content/uploads/RDMToolki
t.pdf?pdf=RDMToolkit

40. 4
What
else?

41. Guidance Document for
Policy Development Published in LEARN
Toolkit:
http://learn-rdm.eu/wp-
content/uploads/RDMToolkit.pdf?
pdf=RDMToolkit

42. Outreach to Continental
Europe: AUSTRIA
• Merge of LEARN findings and
Use Case in Austria
• Adaptation to needs of five
Austrian Art Universities and
(started) four Medical
Universities
• Validation of Policy for
discipline-specific needs

43. Outreach to Continental
Europe: ITALY
• Expansion of policy
activities to Italy (mainly in
Venice, Padua, Milan and
through CINECA)
• Validation of Policy in Italian
language

44. Outreach to LATIN AMERICA
• ECLAC study on RDM policies in LAC
• Mini-Workshops with ECLAC

45. Policy Evaluation Grid
July 2015-August 2016:
Collection and analysis
of over 40 European
RDM policies with the
use of an analysis grid
with 25 criteria
Results available for download at:
http://phaidra.univie.ac.at/o:459219

46. UNIVIE Team
UNIVIE – WP3 Policy Development and
Alignment
Paolo
Budroni
Katharina
Flicker
Imola Dora
Riehle-Traub
Raman
Ganguly
Barbara
Sánchez Solís
name.surname@univie.ac.at