Introduction to the work of the Digital Curation Centre and FAIR initiatives, presented to the Public Record Office of Victoria in Australian

1. DCC & FAIR initiatives
Sarah Jones
Digital Curation Centre
sarah.jones@glasgow.ac.uk
Twitter: @sjDCC

2. DCC – who we are and what we do
Disclaimer: we do more than drink!
Cheers to 15 years!

3. What is Digital Curation Centre?
a centre of expertise in digital information curation with a focus
on building capacity, capability and skills for research data
management and open science
www.dcc.ac.uk
Training | Events | Tools | Advocacy | Consultancy | Guidance | Publications | Projects

4. Defining RDM and digital curation
Create
Document
Use
Store
Share
Preserve
“the active management and appraisal of data over the
lifecycle of scholarly and scientific interest”

5. Mapping data skills landscape
Research community Support community
Knowledge of research discipline
Knowledge of data management and curation
Data scientist
Research Software Engineer
e-Research
Data librarian
Digital preservation
Data steward

6. CODATA/RDA Data Science Schools
Curriculum covering:
• Open Science & RDM
• Ethical use of data
• Data analysis
• Data visualisation
• Machine learning
• Computational infrastructure
http://www.codata.org/working-groups/
research-data-science-summer-schools

7. Relevance: data is used everywhere!
Apps to identify music,
constellations or objects…
To drive recommendations
and sell us things!
To monitor and improve lives

8. We offer tools
RISE – Research Infrastructure
Self Evaluation
https://sparceurope.org/evaluate
-your-rdm-offering
DMPonline for Data
Management Planning
https://dmponline.dcc.ac.uk

9. Help in developing RDM services
http://www.dcc.ac.uk/resources/how-
guides/how-develop-rdm-services

10. New MOOC!
Delivering RDM services
Starts 2nd Sept 2019
www.futurelearn.com/courses/delivering-
research-data-management-services
Learn with us!

11. Track data policy shifts
c.2000 – 2008
• Data management
• Data sharing
• Preservation
• Good research
• conduct codes
c.2010 on
• Open Science
• Open Data
c.2016 on
• FAIR data
• Reproducibility
• Ethical
?
* Anecdotal, not scientific. Personal observation on how I feel global data policy rhetoric and terminology has changed

13. Many projects past and present

14. DCC is increasingly international
Collaborate with ARDC
Training workshops
at Hong Kong UST,
NTU & Malaysian
unis
Run DMPTuuli service for Finland
& DMP services for 45+
organisations internationally
Doing consultancy for
International Development
Research Council in Canada, OECD,
World Bank and many others
Many projects and
partners across Europe
Delivered
data science
schools in
Africa and
Brazil
Collaborations with UCT
and NeDICC in South Africa
Open source code
partnership with UC3 at
California Digital Library
on Data Management
Planning tools
MOU with
KISTI

15. INTERNATIONAL
DIGITAL
CURATION
CONFERENCE
Collective Curation:
the many hands that
make data work
#idcc20
www.dcc.ac.uk/events/idcc20
Organised by
www.dcc.ac.uk www.dri.ie
2020
17 – 20 February 2020
Dublin, Ireland15

16. FAIR – the new buzz word
Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4

17. What is FAIR?
A set of principles that describe the attributes
data need to have to enable and enhance reuse,
by humans and machines
Image CC-BY-SA by SangyaPundir

18. What FAIR means: 15 principles
Findable
F1. (meta)data are assigned a globally unique and
eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable
resource.
F4. metadata specify the data identifier.
Interoperable
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles.
I3. (meta)data include qualified references to other
(meta)data.
Accessible
A1 (meta)data are retrievable by their identifier using a
standardized communications protocol.
A1.1 the protocol is open, free, and universally
implementable.
A1.2 the protocol allows for an authentication and
authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no
longer available.
Reusable
R1. meta(data) have a plurality of accurate and relevant
attributes.
R1.1. (meta)data are released with a clear and
accessible data usage license.
R1.2. (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community
standards.
Slide CC-BY by Erik Schultes, Leiden UMC
doi: 10.1038/sdata.2016.18

19. The FAIR data principles explained
• Clarifications from the Dutch
Techcentre for Life Sciences
• Each principle is a link to further
clarification, examples and
context
https://www.dtls.nl/fair-data/fair-
principles-explained
R1. Meta(data) are richly described with a plurality of accurate and relevant
attributes
• By giving data many ‘labels’, it will be much easier to find and reuse the data.
• Provide not just metadata that allows discovery, but also metadata that richly
describes the context under which that data was generated
• “plurality” indicates that metadata should be as generous as possible, even to the
point of providing information that may seem irrelevant.

20. FAIR is nothing new
• Various research communities have been sharing their data
in a ‘FAIR’ way long before the term emerged
• Meaningful and memorable articulation of concepts
• Natural desire to want to be ‘fair’
• FAIR is gaining significant international traction

21. Open, FAIR and RDM
• Paper explores overlaps between
concepts of Open, FAIR and RDM.
• Proposes using Open and FAIR as
ways to engage researchers in
managing data well, as this is a
prerequisite for both.
• Recommends making data FAIR
and Open wherever possible
Higman, R., Bangert, D. and Jones, S., 2019. Three camps, one destination: the
intersections of research data management, FAIR and Open. Insights, 32(1), p.18.
DOI: http://doi.org/10.1629/uksg.468

22. How do Open, FAIR & RDM intersect?
Open
FAIR data
Managed data
Internal
Self-interest
External
Community benefit

23. FAIR and Open
• The greatest potential
reuse comes when data
are both FAIR and Open
• Align and harmonise FAIR
and Open data policy
Concepts of FAIR and Open should not be conflated.
Data can be FAIR or Open, both or neither

24. FAIR Data Expert Group
Take a holistic approach to lay out what needs to be done to
make FAIR a reality, in general and for EOSC
Addresses the following key areas:
1. Concepts for FAIR
2. Creating a FAIR culture
3. Creating a technical ecosystem for FAIR
4. Skills and capacity building
5. Incentives and metrics
6. Investment and sustainability
Turning FAIR into Reality: Report and Action Plan
https://doi.org/10.2777/1524

25. Address culture and technology
Incentives
Metrics
Skills
Investment
Cultural and
social aspects
that drive the
ecosystem and
enact change
Policies
DMPs
Identifiers
Standards
Repositories
Cloudofregistries
Two sides of one whole

26. EOSC Governance 2019-2020
EOSC governance structure

27. EOSC Exec Board Working Groups
https://www.eoscsecretariat.eu/eosc-working-groups

28. Many, many FAIR projects
All funded by
European Commission
clusters
National initiatives
• EOSC-Nordic
• EOSC-Pillar
• EOSC-synergy
• ExPaNDS
• NI4OS-Europe

30. Thanks! Any questions?