LEARN Webinar

The LEARN Tookit:
an armoury of best practice
for all research performing
organisations
The Webinar will start at 1500 CET
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

SPEAKERS
Martin Moyle
University College London
Wouter Schallier
UN ECLAC
Paolo Budroni
University of Vienna

Leaders Activating
Research Networks
Martin Moyle – University College London
WP1
Stakeholder Engagement

LEARN – LEaders Activating
Research Networks
• To develop the LERU Roadmap
for Research Data
• 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

•Identifies how policy development and leadership are undertaken
Policy and Leadership
•Who undertakes advocacy and what is the message?
Advocacy
•Technical issues around collection and curation
Selection, Collection, Curation, Description, Citation, Legal Issues
•Where is it stored and by whom?
Research Data Infrastructure
•How much does it cost?
Costs
•What skills are required by which communities?
Roles, Responsibilities, Skills
•Who does what?
Recommendations to different stakeholder groups
http://www.leru.org/files/publications/AP14_LERU_Roadmap_for_Research_data_final.pdf
LERU Roadmap for
Research Data

LERU Roadmap:
Key Messages
• Research-performing organisations are not ready for the
challenges of research data management
• Universities should have Research Data Management Policies
• Researchers should have Research Data Management Plans
• Benefits of Open Data for sharing and re-use should be
advocated
http://www.leru.org/files/publications/AP14_LERU_Roadmap_for_Research_data_final.pdf

Deliverables
• Model Research Data Management Policy
• Fed by a study of RDM policies and input from Workshop
attenders
• Toolkit to support implementation
• Issues identified in Workshops and in literature
• Surveys and self assessment tools
• Executive Briefing (in several languages)

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

Case Study 10:
James Bulley and Andrew Gray: RDM in the Performing Arts

Case Study 11:
Professor Geoffrey Boulton: Why Open Data?

Case Study 17:
Robin Rice and David Fergusson: RDM at the University of
Edinburgh: How is it done, what does it cost?

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/

Toolkit Part 3: LEARN Executive Briefing

Leaders Activating
Research Networks
Paolo Budroni – University of Vienna
WP3
Policy Development and
Alignment

Outline
1. LEARN - The Mission
2. Understanding Policies From
Taboos to Policies
3. Model Policy for Research Data
Management (RDM) at Research
Institutions/Institutes
4. Further Developments and
Outreach

Mission
• Produce an exemplar Research
Data Management Policy (agreed
by all Partners)
• Produce a Policy and a Guidance
which can be tailored by any
University or Research Institution to
meet their needs
• Enhance Policy Coordination &
Alignment

2
Understanding
Policies:
From Taboos
to Policies

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
1
2
3
Going over to Rules, Legislations and
Regulations (canons, norms, guidelines)4

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.

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

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.

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)

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)

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.“

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. “

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

How we started
1. Identification of Funders
in the German speaking
countries (DACH)
2. Collection of European
RDM policies and first
analysis
3. Production of 2 grid
documents for policies:
• Formal Elements
• Content Elements
of policies

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

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

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.

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.

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.

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.

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.

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.

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].

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.

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].

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

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

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

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

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

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

UNIVIE Team
Paolo
Budroni
Katharina
Flicker
Imola Dora
Riehle-Traub
Raman
Ganguly
Barbara
Sánchez Solís
name.surname@univie.ac.at

Paolo Budroni
Paolo.budroni@univie.ac.at
THANK YOU!

Questions?
• Type your questions in the chat box
• Wouter Schallier (moderator) will select and
pose questions to the speakers
More Information?
• Please see our website, www.learn-rdm.eu
• Download a free copy of the Toolkit
• Test your RDM Readiness with our survey
We’ll share a recording of the webinar shortly!

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