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03 keynote dillo

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

Published in: Data & Analytics
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03 keynote dillo

  1. 1. Sharing research data: a FAIRytale? Ingrid Dillo, Deputy Director DANS Sharing is Caring X Amsterdam Rijksmuseum 22 November 2019
  2. 2. This is me..
  3. 3. ..and who are you? Go to www.menti.com and enter the code
  4. 4. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  5. 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. 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. 7. DANS Core Services
  8. 8. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  9. 9. Open Science free exchange of information for the good of science and society at large
  10. 10. Components of Open Science transparent research practices open access publishing data sharing Open Science
  11. 11. Scope Open data Data should be as open as possible and as closed as necessary
  12. 12. Why data sharing is important Replication and validation of research outcomes (scientific integrity and transparency)
  13. 13. Niederlande Renommierter Psychologe gesteht Fälschungen
  14. 14. Why data sharing is important Re-use of data (efficiency, return on investment, standing on the shoulders of others)
  15. 15. https://www.theguardian.com/en vironment/2015/dec/17/the- 19th-century-whaling-logbooks- that-could-help-scientists- understand-climate-change
  16. 16. https://www.escardio.org/The- ESC/Press-Office/Press-releases/16- year-study-suggests-air-temperature- is-external-trigger-for-heart-attack
  17. 17. …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
  18. 18. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  19. 19. 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
  20. 20. Quality dimensions • Scientific quality • Technical quality • Fitness for use
  21. 21. 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.
  22. 22. 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.
  23. 23. 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),
  24. 24. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  25. 25. 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
  26. 26. FAIR guiding principles (2016) https://www.nature.com/articles/sdata201618
  27. 27. FAIR metrics See: http://datafairport.org/fair-principles-living-document-menu and https://www.force11.org/group/fairgroup/fairprinciples
  28. 28. 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
  29. 29. 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
  30. 30. The concept of FAIR: what does it really mean?
  31. 31. Responsible management of your data !?
  32. 32. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  33. 33. “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?”
  34. 34. 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
  35. 35. 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
  36. 36. Where to store your data? https://www.re3data.org
  37. 37. 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)
  38. 38. 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
  39. 39. • 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.
  40. 40. Topics DANS Data sharing: the why Data quality FAIR data Trust and certification GLAM sector
  41. 41. …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
  42. 42. 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
  43. 43. Research data management training https://datasupport.researchdata. nl/en/start-the-course/
  44. 44. Guidelines to FAIRify data in the Arts and Humanities https://zenodo.org/record/2668479#.XT231y2Q3OQ
  45. 45. 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
  46. 46. PID Guide http://www.ncdd.nl/en/pid-wijzer/
  47. 47. https://dans.knaw.nl/en/about/services/easy/information-about- depositing-data/before-depositing/file-formats
  48. 48. 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
  49. 49. FAIRsFAIR https://www.fairsfair.eu
  50. 50. Let us share our knowledge and expertise!
  51. 51. ingrid.dillo@dans.knaw.nl www.dans.knaw.nl

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