ALLOWING RESEARCH DATA TO SHINE: PROVIDING
TANGIBLE CREDIT FOR DATA SHARING
Joint OpenAIRE-COAR conference, 21-22nd May 20...
ABOUT FACULTY OF 1000
The Seer of Science Publishing
Science 4 October 2013:
Vol. 342 no. 6154 pp. 66-67
DOI: 10.1126/scie...
WHAT IS F1000RESEARCH?
F1000Research is an open access journal for life scientists that
accepts all scientifically sound a...
POST-PUBLICATION PEER REVIEW
Remove the publication delay.
Invited peer review (post-publication).
Transparent refereeing....
WHY SHARE RESEARCH DATA?
WHY SHARING RESEARCH DATA IS IMPORTANT
Transparency and openness are cornerstones of the scientific method
“Not allowing r...
SHARING DATA ALLOWS REPLICATION
“[W]e evaluated the replication of data analyses in 18
articles on microarray-based gene e...
SHARING DATA CORRELATES WITH HIGHER CITATIONS
“We conclude there is a direct effect of third-party data
reuse that persist...
SHARING DATA ADDITIONALLY PROMOTES
• Diversity of analyses and opinion
• Increased return on investment in research
• Redu...
PUSH TOWARDS SHARING RESEARCH DATA
http://www.biosharing.org/policies
@vkf1000 | @f1000research
TECHNICAL CONSIDERATIONS FOR SHARING
RESEARCH DATA
MAKING DATA ACCESSIBLE
‘Openly accessible’ – apply the principles of the Budapest Open Access
Initiative* (originally crea...
DATA ACCESS AT F1000RESEARCH
• Free to view/access
• Free to download
- Available without a paywall
- Use open repositorie...
MAKING DATA USABLE
• Present data in useable format (i.e. not a supplemental PDF!)
• Specify how data was generated
• Prov...
DATA USABILITY AT F1000RESEARCH
@vkf1000 | @f1000research
• Preview large datasets prior to
downloading
• View data withou...
F1000RESEARCH: DATA REVIEW
Internal pre-publication checks:
• Storage (discipline-specific repository where possible)
• Fo...
DISCOVERING DATA AT F1000RESEARCH
Strasser C, Kunze J, Abrams S, Cruse P (2014) DataUp: A tool to help researchers describ...
HELPING RESEARCHERS TO OVERCOME CULTURAL
BARRIERS TO SHARING DATA
JOURNAL/PUBLISHER DATA SHARING POLICIES
• Reproducible research or data sharing statements in
published papers (Annals Int...
MAKING DATA SHARING EASY - IN-ARTICLE DATA MANIPULATION
A fixed-dose randomized
controlled trial of olanzapine
for psychos...
FORMAL CREDIT FOR DATA SHARING
Feeding into currently recognized scholarly outputs
Benefits:
• Appropriate credit for data...
FORMAL CREDIT FOR DATA SHARING
Novel measures of scholarly outputs
• Encourage data peer review, and promote scholarly cre...
DATA PUBLISHING AND SHARING IS NOT A NEW IDEA
“I have begun to think that no one ought to publish
biometric results, witho...
Email: varsha.khodiyar@f1000.com
Twitter: @vkf1000 / @f1000research
Leave a comment on our blog: http://blog.f1000research...
OpenAIRE-COAR conference 2014: Allowing research data to shine: providing tangible credit for data sharing, by Varsha Khod...
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OpenAIRE-COAR conference 2014: Allowing research data to shine: providing tangible credit for data sharing, by Varsha Khodiyar - F1000Research

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Presentation at the OpenAIRE-COAR Conference: "Open Access Movement to Reality: Putting the Pieces Together", Athens - May 21-22, 2014.
Session 2: Research data in the institutional context and beyond.
Allowing research data to shine: providing tangible credit for data sharing, by Varsha Khodiyar - Editorial Biocurator at F1000Research


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OpenAIRE-COAR conference 2014: Allowing research data to shine: providing tangible credit for data sharing, by Varsha Khodiyar - F1000Research

  1. 1. ALLOWING RESEARCH DATA TO SHINE: PROVIDING TANGIBLE CREDIT FOR DATA SHARING Joint OpenAIRE-COAR conference, 21-22nd May 2014 Varsha Khodiyar Editorial Biocurator, F1000Research @vkf1000 f1000research.com @f1000research
  2. 2. ABOUT FACULTY OF 1000 The Seer of Science Publishing Science 4 October 2013: Vol. 342 no. 6154 pp. 66-67 DOI: 10.1126/science.342.6154.66 http://www.sciencemag.org/content/34 2/6154/66.full.pdf @vkf1000 | @f1000research
  3. 3. WHAT IS F1000RESEARCH? F1000Research is an open access journal for life scientists that accepts all scientifically sound articles, ranging from single findings, case reports, protocols, replications, and null or negative results to more traditional articles. Key features: • Publication within a week • Transparent, post-publication peer review • All data included • Accepts non-traditional article types @vkf1000 | @f1000research
  4. 4. POST-PUBLICATION PEER REVIEW Remove the publication delay. Invited peer review (post-publication). Transparent refereeing. Inclusion of all data in all articles. No restriction of access. All article types published, including data only papers. @vkf1000 | @f1000research
  5. 5. WHY SHARE RESEARCH DATA?
  6. 6. WHY SHARING RESEARCH DATA IS IMPORTANT Transparency and openness are cornerstones of the scientific method “Not allowing reuse of data is scientific malpractice” Royal Society; Science as an open enterprise, Final report 2012 http://royalsociety.org/about-us/history/ @vkf1000 | @f1000research
  7. 7. SHARING DATA ALLOWS REPLICATION “[W]e evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005–2006...We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability.” Ioannidis JPA. et al. Repeatability of published microarray gene expression analyses. Nature Genetics 41, 149–55 (2009) @vkf1000 | @f1000research
  8. 8. SHARING DATA CORRELATES WITH HIGHER CITATIONS “We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data...We further conclude that...a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.” Piowar HA., Vision TA. Data reuse and the open data citation advantage. PeerJ (2013) doi: 10.7717/peerj.175 @vkf1000 | @f1000research
  9. 9. SHARING DATA ADDITIONALLY PROMOTES • Diversity of analyses and opinion • Increased return on investment in research • Reduction of error and fraud • Education of new researchers • New research • meta-analyses to create new datasets • testing of new hypotheses • new analysis methods • studies on data collection methods @vkf1000 | @f1000research
  10. 10. PUSH TOWARDS SHARING RESEARCH DATA http://www.biosharing.org/policies @vkf1000 | @f1000research
  11. 11. TECHNICAL CONSIDERATIONS FOR SHARING RESEARCH DATA
  12. 12. MAKING DATA ACCESSIBLE ‘Openly accessible’ – apply the principles of the Budapest Open Access Initiative* (originally created for scholarly articles) to scholarly data • Free to view/access • Free to download • Free to re-analyse (as a individual dataset or as part of a larger meta- analysis) • Free to modify Community norms to be applied regarding acknowledgement and citation of data. * budapestopenaccessinitiative.org @vkf1000 | @f1000research
  13. 13. DATA ACCESS AT F1000RESEARCH • Free to view/access • Free to download - Available without a paywall - Use open repositories to host data • Free to re-analyse and modify - Use of CC0 licence • Acknowledge data authors - Ensure data is citable @vkf1000 | @f1000research
  14. 14. MAKING DATA USABLE • Present data in useable format (i.e. not a supplemental PDF!) • Specify how data was generated • Provide quality assurance (overview of limitations; peer review) • Share data in non-proprietary formats • Specify access to software required to view data • Specify parameters in software used to analyze the data @vkf1000 | @f1000research
  15. 15. DATA USABILITY AT F1000RESEARCH @vkf1000 | @f1000research • Preview large datasets prior to downloading • View data without leaving the article • Usage statistics provided • Legends and DOIs for data • We encourage authors to - Use non-proprietary formats, e.g. CSV over XLS/XLSX - Include detailed methods to allow replication • Quality assurance with transparent peer review
  16. 16. F1000RESEARCH: DATA REVIEW Internal pre-publication checks: • Storage (discipline-specific repository where possible) • Format • Layout and labelling • Adequate data? • Adequate protocol information? Referees are asked to check: • Methods were appropriate? • Format/structure usable? • Data limitations and sources of error included? • Adequate information to enable potential replication? • Does the data ‘look’ OK? @vkf1000 | @f1000research
  17. 17. DISCOVERING DATA AT F1000RESEARCH Strasser C, Kunze J, Abrams S, Cruse P (2014) DataUp: A tool to help researchers describe and share tabular data [v1; ref status: awaiting peer review, http://f1000r.es/2n7] F1000Research 2014, 3:6 @vkf1000 | @f1000research
  18. 18. HELPING RESEARCHERS TO OVERCOME CULTURAL BARRIERS TO SHARING DATA
  19. 19. JOURNAL/PUBLISHER DATA SHARING POLICIES • Reproducible research or data sharing statements in published papers (Annals Internal Medicine, BMJ) • Data sharing implied by submission (BioMed Central) • Data sharing as a condition of publication (PLOS, NPG) • As above AND data must be available to reviewers/editors • Open data as a condition of submission (F1000Research) • Papers rejected if data unavailable freely* Policy strength increases @vkf1000 | @f1000research
  20. 20. MAKING DATA SHARING EASY - IN-ARTICLE DATA MANIPULATION A fixed-dose randomized controlled trial of olanzapine for psychosis in Parkinson disease [v1; ref status: indexed, http://f1000r.es/1au] Michelle J Nichols, Johanna M Hartlein, Meredith GA Eicken, Brad A Racette, Kevin J Black F1000Research 2013, 2:150 @vkf1000 | @f1000research
  21. 21. FORMAL CREDIT FOR DATA SHARING Feeding into currently recognized scholarly outputs Benefits: • Appropriate credit for data producers with a citable publication • Data accessible from repository regardless of journal subscription • Data independently discoverable via bi-directional linking • Data available in usable form • Potential increase in ‘value’ of data, as increasing numbers of studies are carried out @vkf1000 | @f1000research
  22. 22. FORMAL CREDIT FOR DATA SHARING Novel measures of scholarly outputs • Encourage data peer review, and promote scholarly credit for peer review • Record basic metrics for datasets • Promote data sharing as a formal research output • Participate in cross-publisher initiatives to ease data sharing • Participate in discussions about research data feeding in to the tenure process @vkf1000 | @f1000research
  23. 23. DATA PUBLISHING AND SHARING IS NOT A NEW IDEA “I have begun to think that no one ought to publish biometric results, without lodging a well-arranged and well- bound manuscript copy of his data in some place where it should be accessible, under reasonable restrictions, to those who desire to verify his work.” Galton F. Biometry. Biometrika 1 (1): 7-10 (1901) doi: 10.1093/biomet/1.1.7 @vkf1000 | @f1000research
  24. 24. Email: varsha.khodiyar@f1000.com Twitter: @vkf1000 / @f1000research Leave a comment on our blog: http://blog.f1000research.com/

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