LEARN Final Conference: Tutorial Group | Implementing the LEARN RDM Toolkit

UCL LIBRARY SERVICES
How to implement the LEARN
Toolkit and Executive Briefing
Dr Paul Ayris
Pro-Vice-Provost (UCL Library Services)
Co-Chair of the INFO LERU community
Adviser to the LIBER Board
Chair Jisc Content Strategy Group
e-mail: p.ayris@ucl.ac.uk

Content
 LEARN
 Project Objectives
 LEARN Toolkit
 LEARN Executive Briefing
and Recommendations
 Conclusions
2
Plaster Relief by John Flaxman,
Flaxman Gallery, UCL

LEARN
 5 partners
 UCL (University College
London) – lead partner
 University of Barcelona
 University of Vienna
 LIBER
 ECLAC – UN Commission
for Latin America and the
Caribbean
 Started in June 2015; runs
for 24 months
 €497,000 budget
 100% funded
3
Wilkins Building, UCL, 1826

LEARN 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 six
languages) 4
Wilkins Building, UCL, 1826

What is the problem LEARN seeks to
address?
How prepared
are you and
your institution
for RDM?
5

UCL survey by Research Data Advocacy
Officer
 130 research departments, institutes, centres and units
represented in survey
 Response rate – 306 completed surveys out of 619
 Respondents
 18% early career researchers
 39% experienced researchers
 30% research students
6

 45% of respondents used a personal computer for storage
 Choices included Cloud services; others used paper...
 Central UCL facility used by only 5% 7
17
196
130 116
145
112
64 60
22
178
112
109 122 79
36
21 21
19
105
68
50
94
45
50
22 22
29
0
50
100
150
200
250
300
350
400
External hard
drive or USB
stick
Hard drive of
personal PC
or laptop
Cloud service
(e.g.
Dropbox,
Google Docs,
iCloud)
Shared drive /
UCL server
Hard drive of
UCL PC or
laptop
On paper, on
UCL
premises
On paper,
off-site
Hard drive of
instrument /
sensor
Institutional
repository
(e.g.
Research
Data Storage;
Discovery)
for long-term archiving
to back them up
while working on them
Qu.34 In your most recent project, where did you keep your data? (433 respondents; multiple choice)

8
20
29
30
33
49
52
57
74
84
89
94
111
143
175
0 50 100 150 200
Other
Commercial questions related to your data
No help needed
Citing data
Ethical questions related to your data
Finding data and publications to re-use
Creating metadata
Legal questions related to your data
Sharing data
Open Access to your publications
Project budget and costing data management
Storage & preservation of data (MOSTLY personal
and sensitive)
Data Management Plans
Storage & preservation of data (NOT personal or
sensitive)
Qu.64 Would you be interested in some help with data management? Please tick up to FIVE
preferred elements from the list below. (308 respondents; multiple choice)

9
Beginning of the
project ("very early on",
"straightaway", "pre-
protocol", "at the outset",
etc.)
51%
"Always" ("all
the time",
"throughout")
16%
Project development ("proposal writing", "for
ethical review", "planning", etc.)
14%
Before or after "data collection"
("questionnaire design", "fieldwork
preparation", etc.)
4%
During the project ("periodically",
"halfway through", "1st year of PhD")
4%
"Never"
4%
"Late" / "too late"
2%
End of the project ("at the
end", "towards the end")
1%
Project completion ("ready
for publication", "database
completion")
1%
"Ad-hoc"
1%
When a
problem
occurred
0,5%
"Not until I took
this survey"
0,5%
Qu.61 At what stage of the project did you think about data management? (217
respondents; free text)

Content
 LEARN
 LEARN Toolkit
and Recommendations
 Conclusions
10

 23 chapters of Best
Practice Case Studies
in 8 sections
 http://learn-
rdm.eu/en/about/
 Policy and Leadership
 Advocacy
 Subject approaches
 Open Data
 Research Data
Infrastructure
 Costs
 Roles, Responsibilities,
Skills
 Tool development
11

12
Case Study 11:
Professor Geoffrey Boulton: Why Open Data?

Case Study 18: Paul Ayris & Ignasi Labastida:
Training Early Career Researchers
13
 LERU (League of
European Research
Universities) held
week-long Doctoral
Summer School in
July 2016 on
research data
 UCL (University
College London)
has begun a
Training Programme
with the Doctoral
School
WHO Postgrad/PhD Senior Researcher Librarian Data Scientist
WHEN Early stages of
postgraduate study
As needed, or at
beginning of research
project/proposal state
CPD for subject
librarians/during
library education
Discipline-specific
academic courses
(doctoral)/CPD
WHAT Basics of data
management
practice, FAIR
principles, data
citation, data
evaluation.
Competence in legal
and ethical issues.
Training on discipline-
specific data
management
practices; an
understanding of the
FAIR principles; how
to write a data
management plan
(tailored as necessary
to funder
requirements), data
reuse skills.
Competence in legal
and ethical issues.
Data curation. An
understanding of the
FAIR principles.
Some disciplinary-
specific e-research
methods (TDM)/data
collection skills, IT
skills. Competence in
legal and ethical
issues
Discipline-specific
skills for data
management/
exploitation/
interoperability. An
understanding of
the FAIR principles.
Competence in
legal and ethical
issues
HOW Credited models Practical training Accredited
CPD/Professional
courses
Professional
(academic) courses
and accredited
CPD

Case Study 23:
Paul Ayris & Ignasi
Labastida: Surveying your
level of preparation for
research data management
 13 Questions
 Answers Red, Amber,
Green (RAG)
 Score reveals your level
of preparation
 Survey can be taken
iteratively to show
progress
 350 responses (Mar17) 14

15
Take the survey - http://learn-
rdm.eu/en/rdm-readiness-
survey/

Case Study 22:
Fernando-Ariel López:
Developing a Data Management Plan:
a Case Study from Argentina
16

 Main elements of an RDM Policy:
 Identify what questions the policy is meant to answer and who
owns the policy
1. Preamble, setting policy statements into a local context
2. Scope of the policy – who it covers and how it deals with current
legal commitments
3. Intellectual Property Rights
4. Handling research data – curation, destruction,
17

 Main elements of an RDM Policy:
5. Responsibilities, Rights, Duties
6. Review period for the policy
 Annex of appropriate definitions to help with policy
development
18

Palo Budroni and the University of Vienna:
LEARN Model RDM Policy
(drawn from evaluation of 20 European policies)
19

Content
 LEARN
 LEARN Toolkit
and Recommendations
 Conclusions
20

 Executive Briefing in 6 languages which presents strategic
issues for decision makers
 Presents the RDM challenge, Solutions, the need for an
RDM Policy, the benefits of FAIR data, the requirement for
RDM stewardship, infrastructure and training and RDM
funding needs
21

20 Best Practice Recommendations
 20 Recommendations derived from the RDM community
attending LEARN Workshops
 Policy and Leadership
 Open Data
 Advocacy
 Costs
 Roles, Responsibilities and Skills
22

Conclusions
 Data-driven research
changing the way
research is undertaken
 LEARN has provided
 Model RDM policy
 Exemplar case studies
 Executive Briefings and
Recommendations
 Self-assessment survey
and KPIs
 LEARN will help deliver
infrastructure for data-
driven Scholarship

LEARN Final Conference: Tutorial Group | Implementing the LEARN RDM Toolkit

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LEARN Final Conference: Tutorial Group | Implementing the LEARN RDM Toolkit