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Research Data Sharing: A Basic Framework

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Research Data Sharing: A Basic Framework

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Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/

Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/

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Research Data Sharing: A Basic Framework

  1. 1. RESEARCH DATA SHARING: A BASIC FRAMEWORK Paul Groth @pgroth pgroth.com Elsevier Labs @elsevierlabs LERU Summer School 2016 Data Stewardship for Scientific Discovery and Innovation
  2. 2. WHAT IS DATA?
  3. 3. WHAT IS DATA? “Data refers to entities used as evidence of phenomena for the purposes of research or scholarship” [Borgman Big Data, Little Data, No Data 2015 p.29]
  4. 4. WHY COLLECT DATA?
  5. 5. WHY COLLECT DATA? Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.
  6. 6. HOW IS DATA OBTAINED
  7. 7. HOW IS DATA OBTAINED Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.
  8. 8. WHY SHARE DATA?
  9. 9. WHY SHARE DATA? • R1: reproduce or verify research, • R2: make results of publicly funded research available to the public • R3: enable others to ask new questions of extant data • R4: advance the state of research and innovation. Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology.
  10. 10. • All empirical papers must archive their data upon acceptance in order to be published unless the authors provide a compelling reason why they cannot (e.g., expense, confidentiality). The action editor will be the final arbiter of whether the reason is sufficiently compelling. • “Data” refers to an electronic file containing nonidentified responses that are potentially already coded. Normally, the data would represent an early stage of electronic processing, before individual responses have been aggregated. The data must be in a form that allows all reported statistical analyses to be reproduced while retaining the confidentiality of individual participants. This entails that the data are formatted and documented in a way that makes the structure of the data set readily apparent. • Archiving consists either of submitting the data to the journal (to be displayed as supplementary material at the end of the article), sending it to some other archive that is accessible to established researchers and maintained by a substantial established institution, or authors making the data available on their own website, assuming that they can assure us the site will be maintained by a recognized institution for a reasonable period of time. Again, action editors will be the final arbiters of the appropriateness of an archive. • Any publication that reports analyses of or refers to archived data will be expected to cite the original publication in which the data were reported. • This policy is new and therefore open to modification. Our aim is to implement a policy that maximizes transparency while minimizing the burden on authors.
  11. 11. THE IMPORTANCE OF CITING DATA Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 [https://www.force11.org/group/joint-declaration-data- citation-principles-final]. 1. Importance 2. Credit and Attribution 3. Evidence 4. Unique Identification 5. Access 6. Persistence 7. Specificity and Verifiability 8. Interoperability and Flexibility
  12. 12. 10 ASPECTS OF HIGHLY EFFECTIVE RESEARCH DATA https://www.elsevier.com/con nect/10-aspects-of-highly- effective-research-data
  13. 13. https://storify.com/chenghlee/dataformathell http://isps.yale.edu/sites/default/files/files/I DCC14_DQR_PeerGreenStephenson.pdf ALL DATA ISN’T SUCCESSFUL
  14. 14. BARRIERS TO REACHING SUCCESSFUL DATA?
  15. 15. Common practice: data is very fragmented Using antibodies and squishy bits Grad Students experiment and enter details into their lab notebook. The PI then tries to make sense of their slides, and writes a paper. End of story. 17
  16. 16. ALL DATA ISN’T CURATED
  17. 17. Cost of documentation http://www.indoition.com/en/services/costs -prices-software-documentation.htm
  18. 18. 20Yolanda GilUSC Information Sciences Institute gil@isi.edu Measuring Time Savings with “Reproducibility Maps” [Garijo et al PLOS CB12] 2 months of effort in reproducing published method (in PLoS’10) Authors expertise was required Comparison of ligand binding sites Comparison of dissimilar protein structures Graph network generation Molecular Docking Work with D. Garijo of UPM and P. Bourne of UCSD
  19. 19. CURRENT STRATEGIES FOR DATA SHARING
  20. 20. SUBJECT SPECIFIC REPOSITORIES
  21. 21. SUBJECT SPECIFIC REPOSITORIES
  22. 22. COMMUNITY SPECIFIC REPOSITORIES
  23. 23. GENERIC REPOSITORIES http://data.mendeley.com/ Each dataset receives a versioned DOI, so it can be cited The citation for the associated article is displayed
  24. 24. DATA PUBLICATION
  25. 25. BENEFITS OF MACHINE READBILITY
  26. 26. HOW DO WE MOVE UP THE PYRAMID https://www.elsevier.com/con nect/10-aspects-of-highly- effective-research-data
  27. 27. 60 % OF TIME IS SPENT ON DATA PREPARATION
  28. 28. CURATED DATA SETS
  29. 29. http://ivory.idyll.org/blog/replication-i.html
  30. 30. MORE SEMANTICS
  31. 31. A FRAMEWORK FOR HELPING RESEARCHERS SHARE DATA • What data? • Determine the context • Why is data being collected? • How is data obtained? • What is the researchers’ reason for sharing? • Document • Understand Cost/benefit tradeoffs • Target audience • Automation
  32. 32. FURTHER READING • Syllabus for Data Management and Practice, Part I, Winter 2016. Data Management and Practice, Part I (2016)Christine L Borgmam. https://works.bepress.com/borgman/381/ • Christine L. Borgman. “Big Data, Little Data, No Data” • Reference list ://www.zotero.org/groups/borgman_big_data_little_data_no_data • Borgman, C. L. (2012). The conundrum of sharing research data. Journal of the American Society for Information Science and Technology. • Goodman A, Pepe A, Blocker AW, Borgman CL, Cranmer K, et al. (2014) Ten Simple Rules for the Care and Feeding of Scientific Data. PLoS Comput Biol 10(4): e1003542. doi: 10.1371/journal.pcbi.1003542

Editor's Notes

  • http://www.tamr.com/piketty-revisited-improving-economics-data-science/
  • NASA, A.40 Computational Modeling Algorithms and Cyberinfrastructure, tech. report, NASA, 19 Dec. 2011

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