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Data Sharing and the Polar Information Commons

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Data Sharing and the Polar Information Commons

  1. 1. data sharing and the polar information commons kaitlin thaney program manager, science creative commons This presentation is licensed under the CreativeCommons-Attribution-3.0 license.
  2. 2. access is step one content needs to be legally and technically accessible
  3. 3. knowledge? journal articles data ontologies annotations plasmids and cell lines
  4. 4. knowledge? journal articles data ontologies annotations plasmids and cell lines ... how to treat? like content? software?
  5. 5. as a means to achieve Open Access
  6. 6. data? not necessarily. (it’s complicated)
  7. 7. copyright and databases what’s protected? is it legal? facts are free to what extent is there creative expression?
  8. 8. the data “rights” conundrum...
  9. 9. © “creative expression”
  10. 10. is it creative?
  11. 11. is it creative?
  12. 12. is it creative?
  13. 13. category errors
  14. 14. the problem of... Non-Commercial for data
  15. 15. Non-Commercial what’s a commercial use of the data web?
  16. 16. the problem of... Share Alike for data
  17. 17. 1854
  18. 18. issue of license proliferation whatever you do to the least of the databases, you do to the integrated system (the most restrictive wins) risk for unintended consequences
  19. 19. the problem of... Attribution for data
  20. 20. the problem of... any license for data
  21. 21. national law / jurisdiction-based hurdles sui generis, “sweat of the brow” Crown copyright “level of skill” how internat’l data sharing efforts are affected?
  22. 22. attribution vs. citation which one applies? which is best fit? what’s the difference? “credit where credit is due”
  23. 23. attribution: (legal entity) “triggered by making of a copy” does it apply to facts? how to attribute? (papers, ontologies, data) “in a manner specified by ...” attribution stacking
  24. 24. citation: (gentle(wo)man’s club) legal requirement? interoperability? credit where credit is due entrenched scientific norm
  25. 25. we shouldn’t use the law to make it hard to do the wrong thing ...
  26. 26. need for a legally accurate and simple solution reducing or eliminating the need to make the distinction of what’s protected requires modular, standards based approach to licensing
  27. 27. ... must promote legal predictability and certainty. ... must be easy to use and understand. ... must impose the lowest possible transaction costs on users. full text: http://sciencecommons.org/projects/publishing/open-access-data-protocol/
  28. 28. norms approach set of principles (not license) open, accessible, interoperable create legal zones of certainty
  29. 29. calls for data providers to waive all rights necessary for data extraction and re-use requires provider place no additional obligations (like share-alike) to limit downstream use request behavior (like attribution) through norms and terms of use
  30. 30. Creating norms for polar data 1. How to preserve the source information? How should the user or copier preserve the provenance of the data set. What can be required by PIC that is locally relevant and acceptable? DOIs? Something like a notice inside the data? Ping to a URL at PIC? RDFa inside a section of every database that is provided by PIC? 2. How to cite the data set? Many examples out there including http://ipydis.org/data/citations.html 3. How to preserve quality standards? Perhaps we leave it up to the users? 4. How to note and release user contributions, mashups, repurposing? Do we need release guidelines of contributions, annotations, etc. to data sets. How to reward and track individual contributions to a collective - trackback, user accounts, etc.? A simple “share alike” request?
  31. 31. Some draft norms of appropriate scientific behavior when using PIC data • Acknowledge the source of the data in accordance with the wishes of the provider, and explicitly cite the data when they are used in formal scientific publication (http:// ipydis.org/data/citations.html). • Maintain a link to the original information in any derived products, ideally through a persistent identifier, such as a Digital Object Identifier. • Understanding that the data are made available “as is” and the accuracy of the data or documentation are not guaranteed. The provider assumes no responsibility for misuse or misinterpretation. • Notify the data provider in the manner they describe on how you plan to use the data. For projects integrally dependent on the data consider requesting collaboration and/or co-authorship from the provider. • Share any derived products in the PIC. • Agree to IPY Data Policy 37
  32. 32. others?
  33. 33. 5.4 million bibliographic records
  34. 34. at best, we’re partially right. at worst, we’re really wrong.
  35. 35. data without structure and annotation is a lost opportunity. data should flow in an open, public, and extensible infrastructure support recombination and reconfiguration into computer models, queryable by search engine treated as public good
  36. 36. resist the temptation to treat as property embrace the potential to treat instead as a network resource

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