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National Data Integrity Conference - Making the web work for science

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Presentation for the 2015 National Data Integrity Conference at Colorado State University - May 6-8, 2015

Veröffentlicht in: Technologie
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National Data Integrity Conference - Making the web work for science

  1. 1. kaitlin thaney @kaythaney ; @mozillascience colorado state / 07 may 2015 making the web work for science
  2. 2. doing good is part of our code
  3. 3. help researchers use the power of the open web to change science’s future.
  4. 4. learning around open source, data sharing needed to further open practice; empowering others to lead in their communities.
  5. 5. learning around open source, data sharing: sprints, collaborate, study groups empowering others: fellowships, mentorship, resbaz
  6. 6. (0)
  7. 7. our current systems are designed to create friction. despite original intentions.
  8. 8. current state of science articles data patents
  9. 9. some have a firehose articles data patents
  10. 10. traditions last not because they are excellent, but because influential people are averse to change and because of the sheer burdens of transition to a better state ... “ “ Cass Sunstein
  11. 11. downside of output-driven recognition systems
  12. 12. “There’s greater reward, and more temptation to bend the rules.” - David Resnik, bioethicist
  13. 13. (1)
  14. 14. leveraging the power of the web for scholarship
  15. 15. leveraging the power of the web for scholarship
  16. 16. (if we’re lucky...)
  17. 17. power, performance, scale
  18. 18. - access to content, data, code, materials. - distributed work environments, participatory. - emergence of “web-native” tools, efficiency. - rewards for openness, interop, collaboration, sharing. - reuse, recomputability, transparency. “web-enabled research”
  19. 19. how this maps to research community technology practices collaborative interoperable open review participatory discoverable data management recognition open tools sharing / reuse mentorship designed for reuse documentation / versioning
  20. 20. (2)
  21. 21. learning from (+ through) open source applying lessons from open source development to science
  22. 22. open, iterative development the “work in progress” effect
  23. 23. code as a research object what’s needed to reuse ? http://bit.ly/mozfiggit
  24. 24. (community driven) metadata for software discovery: JSON-LD http://bit.ly/mozfiggit
  25. 25. http://softwarediscoveryindex.org/report/
  26. 26. http://mozillascience.org/contributorship-badges-a-new-project/
  27. 27. http://www.mozillascience.org/collaborate
  28. 28. (3)
  29. 29. our practices are limiting us. how to further adoption of open, web-enabled science?
  30. 30. research social capital capacity infrastructure layers for efficient, reproducible research open tools standards best practices research objects scientific software repositories incentives recognition / P&T interdisciplinarity collaboration community dialogue training mentorship professional dev new policies recognition stakeholders: universities, researchers, tool dev, funders, publishers ...
  31. 31. fostering a (sustainable) community of practitioners
  32. 32. supports needed for “professional development”
  33. 33. https://mozillascience.github.io/studyGroupHandbook/
  34. 34. working within the reward system.
  35. 35. http://openresearchbadges.org/
  36. 36. resbaz.edu.au
  37. 37. next global sprint: june 4-5, 2015 mozillascience.org/collaborate
  38. 38. lowering barriers to entry (not expectations)
  39. 39. focus on building capacity, not just more nodes.
  40. 40. (4)
  41. 41. shifting practice (and getting it to stick) is challenging. open science’s collective action problem.
  42. 42. 63 nations 10,000 scientists 50,000 participants can we do the same for research on the web?
  43. 43. 1. bake reproducible practices into the fabric of research.
  44. 44. 2. design to unlock latent potential of our systems. (most of the technology is already there.)
  45. 45. 3. rethink how we reward researchers and support roles. (and don’t be afraid to hit refresh.)
  46. 46. 4. use cases as means to show potential, sell trust and drive adoption.
  47. 47. we’re here to help. http://mozillascience.org sciencelab@mozillafoundation.org
  48. 48. kaitlin@mozillafoundation.org @kaythaney ; @mozillascience special thanks:

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