1. How to effectively engage researchers with data management
LCRDM Data Stewardship meeting
Utrecht, 3 October 2017

2. About me Marta Teperek Data Stewardship Coordinator
Technical University Delft
@martateperek

3. LINK First things first…
Unless indicated otherwise, images are under CC0 licence
Slides are available
LINK

4. Structure of this talk Danger of top-down approaches
Case study from Cambridge:
Data Champions
Case study from TU Delft:
Data Stewardship
Going forward

5. Painfully learnt lessons – dangers of top-down approaches

6. Open Data requirements in H2020

7. Open Data requirement in NWO
To make data that emerges from NWO-funded research as accessible and reusable as possible, NWO has decided to implement the data management policy in all NWO funding instruments with effect from 1 October 2016.

8. Journals now also require supporting data…

9. All these new requirements are sometimes met with immediate reactions
This is not my priority
People will steal my results!
My data is not interesting
The person who had the data left
Data management is a waste of time (and money)
It would take me 5 years to find all my data!

10. A different approach

11. Selfish benefits of data management and sharing – different arguments!

12. What if I stole your laptop now?

13. How much data would you lose if I stole your laptop?
Nothing – everything is backed up
Less than 1 day of work
1 day – 7 days of work
1 week – 1 month of work
Prefer not to think about it…

14. These things really happen…
Department of Chemistry,Cambridge

15. Why? Never Almost never Sometimes Frequently
How frequently do you have problems finding a specific research data file in your collection?
Never
Almost never
Sometimes
Frequently
Why?

16. How do you organise your data?
2 minutes
Vincent Gaggioli

17. How do you organise your data?
2 minutes
Vincent Gaggioli

18. How do you organise your data?
Copyright: Stijn van Boxmeer, CiTG, TU Delft

19. File naming
Copyright:
****
****

20. Good practices within teams
Think about the last time a student left your group:
did they hand over organised data?
Was it easy to re-use it?
What if your key postdoc quit the job tomorrow?

21. Why sharing? Again, think about yourself
What if someone asked you for data supporting your recent publication?
What if someone asked you for data supporting your 5 or 10 years’ old paper?

22. Datasets available ‘on request’ are not available
Data availability decreases by 17% per year
Chance of address working decreases by 7% per year

23. Selfish reason: share once and don’t be bothered

24. Selfish reason: share once and don’t be bothered
People can self-service instead of bothering you

25. Selfish reason: increase your impact and boost your online presence
Exercise:
Google yourself.
What do you find?

26. But who looks in the repository?
Google does!!!

27. Google searches the repository

28. Track citations of your data
DOI to link/cite your data

29. Engaging researchers – 2 case studies
From the University of Cambridge
From TU Delft

30. Case study from Cambridge

31. Bottom up approach: Data Champions
Volunteers – local advocates for good data management

32. What did the champions do?...
Original task: deliver training on data management.
Instead:
Workshops – using GitHub for version control
Weekly data management ‘tips’ s
Training needs analysis for the department
Embedded data management teaching
Open data FAQs for chemists
Original purpose of initiative was to get more training done
Junior members not confident enough to do training yet and SOME more senior people happy to be involved and advocate but may not want to run formal training
So far information sessions rather than full workshops seem to be popular with more senior people

33. Byte-sized data management emails

34. Open Data FAQ for chemists

35. What worked well and what are the issues?
Community engagement
Innovative, unexpected approaches
Services aligned with the user needs
Trust between service providers and end users
Very rewarding
Time consuming
Resource intensive - careful planning required
Senior management disengagement
No longer-term recognition
No framework and lack of standardisation – difficult to evaluate

36. How to strike a balance? Case study from TU Delft

37. TU Delft - strong emphasis on the “open”
“The world is facing challenges that our university of technology alone cannot meet.”
Karel Luyben, Rector Magnificus, TU Delft

38. Good data management is a necessary prerequisite to open science

39. Data Stewardship project at TU Delft
Goal: Create mature working practices and policies across TU Delft faculties, so that data from every research project can be managed well on a daily basis
Key to this: working practices (and policies) need to be discipline-specific and relevant to local communities

40. Data Stewardship at TU Delft
Subject-specific Data Steward at every Faculty

41. Who are the Data Stewards?
Disciplinary experts:
Research expertise related to Faculty’s research area
Research Data Management experts:
Intense internal and external training on data management
See the programme: stewards/
Embedded in the Faculty
The ‘go-to’ people
Led centrally by the Data Stewardship Coordinator

42. However…
Can one Steward per Faculty have sufficient disciplinary understanding?
Would one Steward per Faculty successfully engage with researchers?

43. However…
How about Data Stewards working with Data Champions?...

44. Is it going to work? Benchmarking strategy from the start
Short survey on data management practice
Comments on metrics welcome:
Challenge: 1 year is a short time!

45. Is it going to work?

46. Watch out for updates:

47. Interested in hearing more case studies?

48. 15 November, Cambridge

49. Going forward

50. What’s changing?...

51. Decision makers realised that rewards system needs to change

52. Funding going preferentially to those who practise open science?

53. Our comments on the proposal

54. What’s next?
Role for LCRDM in defining data expertise? Working with VSNU?
What are the new roles that need to be defined in NL?
What are the pathways for career progression?
Eg., what level of data knowledge is needed to get to a Phd? To become a Postdoc? To become Assistant Professor etc.

55. Thank you Questions?
@martateperek
@alastairdunning
LINK