This document summarizes a presentation given by Robin Rice from the University of Edinburgh on research data management and the role of academic libraries. The presentation covered open science and the FAIR data principles, drivers for research data management policy changes, examples of research data management services, and the changing skills needed in academic libraries to support research data. It provided an overview of the University of Edinburgh's research data services, which include tools and support across the data lifecycle from writing data management plans to long-term data preservation. The presentation also discussed the skills important for data librarians and ways for librarians to develop skills in open science and research data management.

1. Research data support: a
growth area for academic
libraries?
Robin Rice
University of Edinburgh
Future of Libraries Conference
Indian Institute of Management
Bangalore
26-Feb-2019

2. On Future of Libraries theme…
http://www.hopesandfears.com/hopes/future/technology/168465-future-of-libraries

3. Overview
1. Open Science (Open Research) as a current
and future driver for Research Data
Management (RDM)
2. The impetus for RDM policy and culture
change within Higher Education Institutions –
libraries as leaders for research data support
3. What do research data management services
look like? University of Edinburgh as example
4. Changing skills and priorities in academic
libraries to adapt to the data revolution

4. The four pillars of Open Science
Image from Foster - What is open science online training course

5. Open Science: a definition
• Open Science has been defined as the combination
of “Open Source, Open Data, Open Access, Open
Notebook”, which together signify the goals of:
– Transparency in experimental methodology,
observation, and collection of data;
– Public availability and reusability of scientific data;
– Public accessibility and transparency of scientific
communication;
– Using web-based tools to facilitate scientific
collaboration
[Dan Gezelter, http://www.openscience.org/blog/?p=269]
With acknowledgement to Martin Donnelly and the Digital Curation Centre

7. FAIR paradigm: Open Data by Default
• FINDABLE: “Metadata and data should be easy to find for both
humans and computers. Machine-readable metadata are essential for
automatic discovery of datasets and services.”
• ACCESSIBLE: “Once the user finds the required data, she/he
needs to know how can they be accessed, possibly including
authentication and authorisation.”
• INTEROPERABLE: “The data usually need to be integrated
with other data. In addition, the data need to interoperate with
applications or workflows for analysis, storage, and processing.”
• REUSABLE: “The ultimate goal of FAIR is to optimise the reuse
of data. To achieve this, metadata and data should be well-described
so that they can be replicated and/or combined in different settings.”

8. FAIR Data visualised
From: European Commission Horizon 2020 infographic; July 2016

9. Benefits of Open Data
- Journal of Open Archaeology Data,
CC-BY 3.0

10. Not all data can be open: “As open as
possible, as closed as necessary”
From: European Commission Horizon 2020 infographic; July 2016

11. How can research libraries drive culture
change? e.g. LIBER 2018-22 strategy
• Open Access is the predominant
form of publishing;
• Research Data is Findable,
Accessible, Interoperable and
Reusable (FAIR);
• Digital Skills underpin a more
open and transparent research
life cycle;
• Research Infrastructure is
participatory, tailored and
scaled to the needs of the
diverse disciplines;
• The cultural heritage of
tomorrow is built on today’s
digital information.

12. RDM policy and culture change within
Higher Education Institutions (HEIs)
• Research data management  FAIR data
• BUT culture change can’t be forced and
academic norms are resistant to change
• HEIs need to create Open Science/RDM
policies in response to pressure from
funders, publishers, the public, laws
• How can librarians lead their institutions
if their job is to provide support?...

13. Provide user-centric support /service!
• Data-driven researchers may not value
libraries; a chance to change their mind
• Researchers suffering from information
overload (data deluge), lacking skills
• Start by finding RDM champions; collaborate
to pilot new services & evaluate
• Gather evidence; do a needs assessment
• Focus on advice and guidance first; learn how
to tailor services that suit through
engagement

14. A maturity model for RDM services
Cox, A. et al. “Developments in Research Data Management in Academic Libraries:
Towards an Understanding of Research Data Service Maturity” Journal of the
Association for Information, Science and Technology - September 2017 p. 2191.
DOI: 10.1002/asi

15. Pragmatic pointers for libraries to get
started in RDM
• A “top ten” list of recommendations for
libraries to get started with research
data management from LIBER,
https://bit.ly/2NuUhAs
• Research Data Alliance (RDA) 23 things
http://bit.ly/RDAthing1
• LEARN RDM Toolkit including a model
policy https://bit.ly/2oaL0nN

16. About UoE Information Services
• Applications
• IT Infrastructure
• Learning, Teaching & Web
• Library & University
Collections
• User Services
• Information Security
• EDINA
• Digital Curation Centre
16
Argyle House © CoStar

17. Univ. of Edinburgh: RDM Policy (2011)
as framework for building services
“The University will provide
mechanisms and services for
storage, backup, registration,
deposit and retention of research
data assets in support of current
and future access, during and
after completion of research
projects.”
“All new research proposals must
include research data
management plans or protocols
that explicitly address data
capture, management, integrity,
confidentiality, retention, sharing
and publication.”
“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 deposit and
retention in an appropriate
national or international data
service or domain repository, or
a University repository.”
https://www.ed.ac.uk/is/research-
data-policy

18. UoE Research Data Service = Tools and support
for working across the data lifecycle
18
https://www.ed.ac.uk/is/research
-data-service

19. Tools and Support Description
DMPOnline Online tool to create a data
management plan, based on
University and funders’ templates
Support and DMP Review Answer enquiries and review plans,
provide advice; in-depth or quick
turaround
Sample DMPs Library of successful plans to show
researchers in different disciplines
Before your research project begins
19

20. Tools and Support Description
Finding data ‘Finding data’ portal and data librarian
consultancy; help with accessing / purchase
of datasets or data subscriptions
Active data storage (DataStore) Central, backed up storage for all researchers
- individual and shared spaces
Sensitive data
(Data Safe Haven)
New, secure facility for working with sensitive
data on remote server. We are pursuing ISO
27001 security certification
Code versioning (Subversion,
Gitlab)
Private or public software code storage and
management. Documents all code and allows
rollback to prior versions
Collaboration and data sync’ing
(DataSync)
Open source tool to allow external partners
to access your research data
Electronic Lab Notebook
(RSpace)
Data management for laboratory based
research; interoperable with local systems
Research in progress
20

21. Tools and Support Description
Open Access data repository
(DataShare)
Allows researchers to share
data publicly and preserve for
long-term
Long-term retention
(DataVault)
Deposit datasets for a specified
retention period (for example,
10 years), immutable copy
Data asset register through the
University CRIS (Pure for
datasets)
Record a description of your
dataset along with your
publications and research
projects
Approaching completion
21

22. Tools and Support Description
General RDM support Answer enquiries by email, phone or
appointment; track through central helpdesk
system
Online training (MANTRA
and RDMS MOOC)
Learn online at your own pace or with a cohort
of peers through our open educational
resources
Scheduled and bespoke
training
Sign up for a scheduled workshop or request a
special training session for your research group
Research Data Service
website
All the tools and support in one place,
increasingly self-serve
Blog and promotional
materials
New developments on our Research Data Blog.
Service video and brochure
RDM Forum & Sharepoint
site
Regular meetings for school support staff plus
access to shared resources
Dealing with Data event Attend an annual conference of researchers
talking about their data challenges and solutions
Training and support throughout your project
22

23. Changing skills and priorities in
academic libraries? (A. Cox, et al)
Cox, A. et al. “Developments in Research Data Management in Academic Libraries: Towards an
Understanding of Research Data Service Maturity” JOURNAL OF THE ASSOCIATION FOR
INFORMATION SCIENCE AND TECHNOLOGY - September 2017 p. 2191. DOI: 10.1002/asi

24. What skills do data librarians find
important? (1 of 3 slides)
• Federer, Lisa. (2018). Defining data
librarianship: A survey of competencies, skills,
and training. Journal of the Medical Library
Association. 106. 10.5195/JMLA.2018.306.
• Methods: Librarians who do data-related work
were surveyed about their work and
educational backgrounds and asked to rate
the relevance of a set of data-related skills and
knowledge to their work.

25. • “Personal Attributes” - most highly rated category
overall (70% respondents ranked Very important +)
• “Library Skills” - lowest rated category (40%)
• Top 5 items: “Developing relationships with
researchers, faculty, etc.”; “Oral communication and
presentation skills”; “Teamwork and interpersonal
skills”; “Written communication skills”; and “One-on-
one consultation or instruction.”
• Bottom 5 items: “PhD or doctoral degree”;
“Professional memberships”; “Cataloging”; “Graduate
degree in a [subject discipline]”; & “Collection dev’t.”
Surprise! Soft skills highly rated

26. “Data generalists vs subject specialists”
• Federer found both of these types of data
professionals in her study.
• Items that subject specialists rated as more
important than the data generalists reflected
the more specialized areas that these
respondents likely supported.
• For example, subject specialists rated
“Bioinformatics support,” “Support for clinical
data management,” and “Support for data
resources.

27. Librarians can skill themselves up on
open science /data -
• Data, software, & library carpentry
• FOSTER open science online training
• Research Data MANTRA
• Research Data and Sharing MOOC
• Other data analysis, analytics and data
science MOOCs
• When all else fails, read a book

28. Many thanks!
r.rice@ed.ac.uk