1. FAIR Across – Implementation of FAIR into research practice
NeiC Conference, FAIR Session, Thursday May
Anne Sofie Fink,
National Forum for Research Data Management,
Digital Service, Danish National Archives

2. About FAIR Across Motivation for the activity
From talk about FAIR to FAIR action
A view shared among members of National Forum for Research Data Management
Overall objective for the activity
Understanding challenges for FAIR data in action
Take FAIR data principles to researchers in adapted ways in order to be implemented into research practice.
Method
FAIR interview cases (initial interview and follow-up with researchers from 5 scientific disciplines)
FAIR Action (data pilots with presentation of tools based on interviews)
Read more about FAIR Across in e-science Vidensportalen

3. Metadata on the Across Activity
Activity funded by National Forum for Research Data Management (DeiC and Deff)
Participants from universities and preservation institutions
Representative cases across research disciplines, project size, national/international, sensitive/non-sensitive data content, data with/without copyright
Time period for activity

4. Deliverables Phases for activity Deliverables Product
Common understanding of FAIR and implementation of FAIR
FAIR Poster, post cards and presentation
‘What’s in it for me?’
Phase 2
Selection of interview cases and data pilots
Interviews
Phase 3
Train-the-Trainer 1 say session FAIRification tools
Shared knowledge of FAIRification Tools
Phase 4
2nd interviews and FAIR data pilots Knowlegde base for FAIR Tool box
More FAIR data in reseach cases based on demo of tools
Phase 5
Conference: Exploring the FAIR data principles across disciplines
FAIR TOOLBOX… for the (FAIR) future

5. Minimal prior knowledge among researchers with exceptions
FAIR Findings from interview cases and data pilots A Short – but showing – Sample
Minimal prior knowledge among researchers with exceptions
Research data may be FAIR without FAIR claim
High quality data and metadata come with a prize.
Licensing and PIDs as FAIR manifestations
Focus on GDPR squishes FAIR out – that’s not FAIR!
Activities to support FAIR data is done in collaboration

6. For researchers, research support staff, data professionals, etc.
FAIR Toolbox
For researchers, research support staff, data professionals, etc.

7. Poster

8. Post cards

9. Presentation of FAIR principles: What’s in it for me?

10. The FAIR model to uncover FAIR challenges

11. Tools in the Box (based on interviews and pilots)
Customized FAIRification Tools for
any research project
The FAIRytale
What’s in it for me?
The FAIR model to uncover
FAIR challenges
The FAIR Myths Busters

12. FAIR Myths (one of the tools)
FAIR data?
1. FAIR data & data confidentiality
There is no need to try out something new and do more than necessary
2. FAIR data & article publication
I cannot share my data openly, because they are personal or confidential
3. FAIR data & time/benefit for you/the researcher
My data is already published - in a research article
4. FAIR data & career advancement
I do not have time for that. And it is not worth the effort
5. FAIR data & social science
Why should I care about that? All that matters are citations and impact factors
6. FAIR data & social science
I do not need that - it is only for engineers and scientists
7. FAIR data & the researcher role
My data is too unique to fit one standard
8. FAIR data & using already collected data
I am a researcher - not a data manager
9. FAIR data & work effect/difficulty
I do not collect any data, I only use what is already available elsewhere
Get a
Post
Card
With
The
Answer

13. FAIR Toolbox, all inclusive;)
Understanding FAIR data
Poster
Post cards
Presentation
Taking FAIR into action
The FAIR proces model for uncovering FAIR Challenges
Customized FAIRification Tools for any research project
The FAIR Myths Buster post cards
The FAIRytale
Happy ending… a Tool Box in use… More FAIR data

14. From findings and to action points
FAIR awareness and knowledge encourages FAIR behaviour
More information about FAIR (Toolbox!) for more FAIR actions in the future
Build-in incentive for FAIR in the research process (DMPs, data sharing support tools etc.)
FAIR as assurance for paying credit to all contributors in a research project
Awareness of data and metadata as re-useable resources act as FAIR incentive
FAIR front runners: Professors, funders and research support staff
FAIR Data is team work (for researchers, research support staff, data managers etc.).
There are tools for all kinds of FAIR support work. Create visibility for tools.
Focus on FAIR manifestations – PID, licenses, version control etc.
FAIR Funding for research projects
Funder demand for FAIR data
Costs for making data (and services) FAIR as integrated costs in research projects
Make FAIR data count! For individuals, communities, institutions and society!

15. Putting into perspective
From FAIR action with grassroot approach to the (FAIR) international scene
Well-received promotional products for FAIR (FAIR Toolbox) far beyond DM Forum
More FAIR action? = FAIR Tooling, FAIR funding, FAIR competences etc.
FAIR Across sustainablity…

16. From FAIR Across to RDA in Denmark
Point of departure same as FAIR Across

17. Denmark in RDA and RDA in Denmark
Encourage and support the uptake of RDA recommendations and outputs in Denmark
Foster active engagement in RDA activities such as RDA working groups and interest groups by Danish researchers and research support organisations
Coordinate with relevant Nordic and European organisations on RDA related results and activities
Use of existing national network for RDM (National Forum for RDM) and DeiC (Danish e-Infrastructure Consortium)
Danish RDA ambassador supports the node

18. Towards => More FAIR and Open Science Data+
Research Teams
Across
DM Forum &
RDA project group

19. Comments and questions?
Thank you!

20. More knowlegde about FAIR Across and DK-RDA-node
Visit eScience Vidensportalen:
FAIR på Tværs
Danish RDA National Node (on the way!)
Visit RDA web site:
RDA in Denmark
And/or contact us:
Anne Sofie Fink
Zaza Nadja Herbert-Hansen