Download to read offline
Uploaded byAll Things Open
What Academia Can Learn from Open Source
The document discusses how academia can learn from open source principles, highlighting issues such as inefficiencies in research practices and the need for collaboration. It contrasts open source collaborations with traditional academic approaches, advocating for a culture of reuse and improved communication in research. Emphasizing the importance of reproducibility and shared challenges, the author argues for adapting academic practices to foster a more collaborative environment.
More Related Content
Similar to What Academia Can Learn from Open Source
What Academia Can Learn from Open Source
- 1. What Academia CanLearn from Open Source ! Arfon Smith arfon@github.com @arfon Creative Commons Attribution 3.0 Unported License "
- 2.
- 3. What is aGitHub?
- 11. A story frommy life (10 years ago)
- 12.
- 13. tl;dr - technical,but brimming with inefficiencies
- 14.
- 15.
- 16.
- 17.
- 20. Diffraction grating Telescope Detector
- 26. > cat bad_pix_mask.txt 130 130 1 2048 189 189 258 258 480 562 378 378 493 521 390 397 851 851 247 274 319 319 304 580 493 511 610 636 188 188 228 228
- 27.
- 28. Wasteful 2 dayswork
- 29. Wasteful 2 dayswork 3 observing runs/week
- 30. Wasteful 2 dayswork 3 observing runs/week 52 weeks in year
- 31. Wasteful 2 dayswork 3 observing runs/week 52 weeks in year 15 year detector lifetime
- 32. Wasteful 2 dayswork 3 observing runs/week 52 weeks in year 15 year detector lifetime 2*3*52*15 = 4680 days (13 years)
- 33. Wasteful… but thenorm 2 days work 3 observing runs/week 52 weeks in year 15 year detector lifetime 2*3*52*15 = 4680 days (13 years)
- 34. A second storyfrom my life (2 months ago)
- 41. Software composed ofmany components
- 42. Your software isthe thing that is different
- 43. Open Source: Ubiquitous culture of reuse
- 44. Why isn’t academialike this?
- 47.
- 48. Careers are basedon paper counts
- 49. Careers are basedon paper citations
- 50.
- 51. 1. ’Novel’ resultspreferred
- 52.
- 53. 3. The formatsucks
- 55.
- 56. So that otherscan repeat
- 57.
- 58. It’s the waythat we explain that matters most
- 60. State of theart technology
- 61. State of theart technology… for the late 17th century* * Michael Nielsen
- 63.
- 64.
- 65.
- 69. Numbers, data Science! Complex stuff
- 70.
- 71. Verification may takeyears (if at all)
- 73. What do opensource collaborations do well?
- 74. Open Source vs Open Collaborations Open source collaborations
- 75. Open Source: theright to modify, not the right to contribute. Open source collaborations
- 76. Open Collaborations: ahighly collaborative development process and are receptive to contributions Open source of collaborations code, documentation, discussion, etc from anyone who shows competent interest.
- 77. Open Collaborations: ahighly collaborative THIS development process and are receptive to contributions Open source of collaborations code, documentation, discussion, etc from anyone who shows competent interest.
- 78.
- 79. Expose their collaborative process
- 80. How do 4000people work together?
- 81.
- 89. Code first, permissionlater discuss improve
- 90. Every time thishappens the community learns
- 95.
- 98. “open source is… reproducible by necessity” Fernando Perez http://blog.fperez.org/2013/11/an-ambitious-experiment-in-data-science.html
- 99. Better at collaborating because they have to be
- 100. Open = Public? (doesn’t have to mean this)
- 101. ‘Open Source’ wayof working
- 102. Open (within yourteam, department or institution)
- 103.
- 104.
- 105.
- 106.
- 107. Academia can learnfrom open source
- 108. Academia must learnfrom open source
- 110. What’s happening inacademia today?
- 111.
- 117.
- 120.
- 124. Where might moresignificant change happen?
- 125. Where do communitiesform?
- 126. Around a sharedchallenge?
- 127.
- 129. 10n ? Level1 (continual) Level 2 (periodic)
- 130. Informatics and Statistics Active Galactic Nuclei Solar System Dark Energy (DESC) Stars, Milky Way Strong lensing Transients/variable stars Galaxies Large-scale structure Supernovae Weak lensing
- 132. Software composed ofmany components
- 133. Your software shouldbe the thing that is different
- 134. science too! Yoursoftware should be the thing that is different
- 135. Scientific data isbecoming more open
- 136.
- 137. How do wemake this behaviour the norm?
- 138.
- 139. “Academic environments of today do not reward tool builders” Ed Lazowska, OSTP event http://lazowska.cs.washington.edu/MS/MS.OSTP.pdf
- 148. “publishing a paperabout code is basically just advertising” David Donoho http://www.stanford.edu/~vcs/Video.html
- 150. How to derivemeaningful metrics from open contributions?
- 152.
- 158.
- 160. Barriers are cultural,not technical
- 161. Why should wecare?
- 162. Because we paidfor it?
- 163.
- 164. Because care aboutthe creation of knowledge?
- 165. Open source hassolved much of what academia needs
- 166. Our challenge isto adapt and evolve the academy in this new collaborative age
- 167.