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Webinar osf 2018 olivier klein

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Data sharing 101: a principled approach to transparency in (psychological) science by Olivier Klein (ULB)

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Webinar osf 2018 olivier klein

  1. 1. (DATA) SHARING 101:A PRINCIPLED APPROACHTOTRANSPARENCY IN (PSYCHOLOGICAL) SCIENCE Olivier Klein, Université Libre de Bruxelles oklein@ulb.ac.be Open science Webinar, October 24, 2018 @olivier_klein
  2. 2. https://www.collabra.org/ articles/10.1525/collabra.158/
  3. 3. WHY SHARE DATA?
  4. 4. • Trust • Reusability • Data loss • Selfishness Reasons for engaging in transparent research practices
  5. 5. INCREASES IN… • Citations • Media Attention • Job Opportunities • Funding • Potential collaborators McKiernan, E. C., Bourne, P. E., Brown, C.T., Buck, S., Kenall,A., Lin, J., et al. (2016). How open science helps researchers succeed. eLife, 5, 1–19.
  6. 6. Piwowar, H.A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PloS one, 2(3), e308. Piwowar, H.A., &Vision,T. J. (2013). Data reuse and the open data citation advantage. PeerJ, 1, e175.
  7. 7. Your closest collaborator is you 6 month ago but you don’t respond to emails
  8. 8. WHY NOT? Houtkoop, B. L., Chambers, C., Macleod, M., Bishop, D.V., Nichols,T. E., & Wagenmakers, E. J. (2018). Data Sharing in Psychology:A Survey on Barriers and Preconditions. Advances in Methods and Practices in Psychological Science, 1(1), 70-85.
  9. 9. WHY NOT
  10. 10. WHY NOT
  11. 11. WHY NOT
  12. 12. HOWTO SHARE?
  13. 13. Formulation of Data Management plan Preregistration: Hypotheses / Materials placed on public repository (with optional access control) Full accessibility of preregistration, scripts and data Preregistered information + Script + Data on Public Repository (accessible at least to editors and reviewers & cited in the paper) Planning Data Collection Paper Submission Paper Publication Ethical Approval A transparent workflow
  14. 14. WHATTO SHARE • Hypotheses • Ethics forms • Detailed Study protocol • Materials • Report / Article • Data
  15. 15. DATA Raw Processed Meta-data Transformation Code / Analysis procedures Analyses
  16. 16. META-DATA Kai Horstmann, Humboldt University Source: https://osf.io/azppp/
  17. 17. WHERETO STOREYOUR STUFF ? OSF.io
  18. 18. • Use stable identifiers • Allow licensing • Control access • Have some persistence • Respect local legislation • Have large storage capacity • Ethical financial model • Track re-use • Be easy to use • Yes (URLs or DOI) • Choose your license • Private vs. Public Project • 50 year preservation funds. • Compliance With GDPR • Max 5GB per file but adds-ons. • COS • Stats • Demonstration A repository should……
  19. 19. @olivier_klein oklein@ulb.ac.be Slides here: osf.io/ v5nzf

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