Sharing research data: a FAIRytale? by Ingrid Dillo Sharing is Caring X Amsterdam 2019

1. Sharing research data: a FAIRytale?
Ingrid Dillo, Deputy Director DANS
Sharing is Caring X Amsterdam
Rijksmuseum
22 November 2019

2. This is me..

3. ..and who are you?
Go to www.menti.com and enter the code

4. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

5. Institute of
Dutch Academy
and Research
Funding
Organisation
(KNAW & NWO)
since 2005
First predecessor
dates back to
1964 (Steinmetz
Foundation),
Historical Data
Archive 1989
Mission: promote
and provide
permanent
access to digital
research
resources
DANS is about keeping data FAIR

6. DANS Core Services
DataverseNL: short and
mid-term data storage
EASY: certified long-term data repository
NARCIS: Gateway to scholarly
information In the Netherlands

7. DANS Core Services

8. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

9. Open Science
free exchange of information
for the good of science and society at large

10. Components of Open Science
transparent
research
practices
open access
publishing
data
sharing
Open Science

11. Scope
Open data
Data should be as open as possible and as closed as necessary

12. Why data sharing is important
Replication and validation of research outcomes
(scientific integrity and transparency)

13. Niederlande
Renommierter Psychologe gesteht
Fälschungen

14. Why data sharing is important
Re-use of data (efficiency, return on investment, standing on
the shoulders of others)

15. https://www.theguardian.com/en
vironment/2015/dec/17/the-
19th-century-whaling-logbooks-
that-could-help-scientists-
understand-climate-change

16. https://www.escardio.org/The-
ESC/Press-Office/Press-releases/16-
year-study-suggests-air-temperature-
is-external-trigger-for-heart-attack

18. …but what about the researchers?
Science, Digital; Fane, Briony; Ayris, Paul; Hahnel, Mark;
Hrynaszkiewicz, Iain; Baynes, Grace; et al. (2019): The State of
Open Data Report 2019. figshare. Report.
https://doi.org/10.6084/m9.figshare.9980783.v2

19. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

20. Data quality
Trust is a central element in
research.
The data re-user wants to know:
• Where do these data come from?
• How were they collected?
• What has happened with them
along the way?
Quality - provenance

21. Quality dimensions
• Scientific quality
• Technical quality
• Fitness for use

22. Dimension 1: Scientific quality
Principles:
Honesty, scrupulousness, transparency,
independence, responsibility.
Shared responsibility:
• the individual responsibility of the researcher;
• the institutional context within which the
research is carried out;
• the informal networks that play a vital role
within the research community.

23. Dimension 2: Technical data quality
CREATING
DATA
PROCESSING
DATA
ANALYSING
DATA
PRESERVING
DATA
GIVING
ACCESS TO
DATA
RE-USING
DATA
Based on UK Data Archive lifecycle:
https://www.ukdataservice.ac.uk/manage-
data/lifecycle
• As a product, data have quality,
resulting from the process by
which data are generated.
• Managing and documenting data
through all stages helps to build
trust.

24. Dimension 3: Fitness for use
• definition of data quality as
“fitness for use”
• data quality judgment
depending on data consumers
Wang, R. Y., & Strong, D. M. (1996) Beyond Accuracy:
What Data Quality Means to Data Consumers. Journal of
Management Information Systems 12(4),

25. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

26. FAIR Data Principles (2014)
During the 2014 workshop “Designing a data FAIRport” for the life sciences in Leiden
a minimal set of community-agreed guiding principles were formulated.
The FAIR Data Principles:
• Easy to find by both humans and
machines based on metadata
• With well-defined use license and
access conditions (Open Access if
possible)
• Ready to be linked with other
datasets
• Ready to be re-used for future
research and to be processed
further using computational
methods and tools

27. FAIR guiding principles (2016)
https://www.nature.com/articles/sdata201618

28. FAIR metrics
See: http://datafairport.org/fair-principles-living-document-menu and
https://www.force11.org/group/fairgroup/fairprinciples

29. Policy makers/funders and FAIR
https://publications.europa.eu/en/pu
blication-detail/-
/publication/7769a148-f1f6-11e8-
9982-01aa75ed71a1/language-
en/format-PDF/source-80611283

30. Researchers and FAIR
Science, Digital; Fane, Briony; Ayris, Paul; Hahnel, Mark;
Hrynaszkiewicz, Iain; Baynes, Grace; et al. (2019): The State of
Open Data Report 2019. figshare. Report.
https://doi.org/10.6084/m9.figshare.9980783.v2

31. The concept of FAIR: what does it really mean?

32. Responsible management of your data !?

33. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

34. “Perhaps the biggest challenge in sharing data is
trust: how do you create a system robust enough
for scientists to trust that, if they share, their
data won’t be lost, garbled, stolen or misused?”

35. Data sharing a FAIRytale?
“Research data will not become nor stay FAIR by magic. We need skilled people,
transparent processes, interoperable technologies and collaboration to build,
operate and maintain research data infrastructures.”
Mari Kleemola,
CoreTrustSeal Board
https://tietoarkistoblogi.blogspot.com/2018/11/being-trustworthy-and-fair.html

36. Importance of sustainable data infrastructure
”36% of respondents have lost data on
which they were working and there is,
unsurprisingly, a high correlation between
the vehicle for storing data and where it was
lost - computer hard drives were the most
common culprit here.”
Science, Digital; Hahnel, Mark; Treadway, Jon; Fane,
Briony; Kiley, Robert; Peters, Dale; et al. (2017): The
State of Open Data Report 2017. figshare. Paper.
https://doi.org/10.6084/m9.figshare.5481187.v1

37. Where to store your data?
https://www.re3data.org

38. Trusting digital repositories
• actions and attributes of the trustee (integrity, transparency, competence,
predictability, guarantees, positive intentions)
• external acknowledgements:
• reputation (researchers)
• third party endorsements (funders, publishers)

39. CoreTrustSeal
• Community driven repository certification
• Developed under the umbrella of RDA
• 16 requirements (organizational infrastructure, digital object management,
technology and security)
• Peer review, 3 year cycle, transparent processes
• Global uptake, discipline agnostic
CoreTrustSeal repository certification:
• Gives data producers the assurance that their data will be stored in a
reliable manner and can be reused;
• Provides funding bodies with the confidence that data will remain available
for reuse;
• Enables data consumers to assess the repositories where data are held;
• Supports data repositories in the efficient archiving and distribution of data.
https://www.coretrustseal.org

40. • We need to share our data in order to turn open science into a reality;
• The FAIR principles help us to define high quality and transparent research data
management practices;
• Certification mechanisms, like CoreTrustSeal for digital repositories, help us to
create trust in the research data infrastructure we need in order to safeguard the
accessibility and assessibility of our (FAIR) data for the future.

41. Topics
DANS
Data sharing: the why
Data quality
FAIR data
Trust and certification
GLAM sector

42. …and what does it all mean for the GLAM sector?
• Open Access trend leading to a deluge
of digital surrogates of material objects
• Scientific research data

43. Facing the same data challenges
• Copyright management and proper licensing
• Importance of rich metadata for exploring the context of art, for discovery of
connections, for finding meaning
• Long-term accessibility and accessibility of the data
• Etc., etc.
open access is not enough,
responsible, high quality data management is needed

44. Research data management
training
https://datasupport.researchdata.
nl/en/start-the-course/

45. Guidelines to FAIRify data in the Arts and Humanities
https://zenodo.org/record/2668479#.XT231y2Q3OQ

46. Aim and users
• 20 guidelines structured around the letters of FAIR: Findable,
Accessible, Interoperable, Reusable
• Intended users are:
• data producers / researchers who need clear and simple
guidelines on how to start with RDM
• RIs and Data Archives
• Intended to be a first entry point for good RDM practices

48. PID Guide
http://www.ncdd.nl/en/pid-wijzer/

50. https://dans.knaw.nl/en/about/services/easy/information-about-
depositing-data/before-depositing/file-formats

51. Check out the Research Data Alliance!
https://doi.org/10.5281/zenodo.3355145
• Archives and records professionals
for research data
• Digital practices in history and
ethnography
• Libraries for research data
• Indigenous data
• Empirical humanities metadata
https://www.rd-alliance.org

52. FAIRsFAIR
https://www.fairsfair.eu

53. Let us share our knowledge and expertise!

54. ingrid.dillo@dans.knaw.nl
www.dans.knaw.nl