What academia can learn from open source

My slides from All Things Open - http://allthingsopen.org/

Transcript

1. What Academia Can Learn from Open Source Creative Commons Attribution 3.0 Unported License Arfon Smith arfon@github.com @arfon "
2. !
3. What is a GitHub?
11. A story from my life (10 years ago)
12. Astronomer
13. tl;dr - technical, but brimming with inefficiencies
14. http://www.flickr.com/photos/blachswan
15. http://www.flickr.com/photos/esoastronomy/
16. http://www.flickr.com/photos/esoastronomy/ http://www.flickr.com/photos/jamiegilbert
17. http://amandabauer.blogspot.com/
20. Diffraction grating Telescope Detector
26. 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 > cat bad_pix_mask.txt
27. Wasteful
28. Wasteful 2 days work
29. Wasteful 2 days work 3 observing runs/week
30. Wasteful 2 days work 3 observing runs/week 52 weeks in year
31. Wasteful 2 days work 3 observing runs/week 52 weeks in year 15 year detector lifetime
32. Wasteful 2 days work 3 observing runs/week 52 weeks in year 15 year detector lifetime 2*3*52*15 = 4680 days (13 years)
33. Wasteful… but the norm 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 story from my life (2 months ago)
41. Software composed of many components
42. Your software is the thing that is different
43. Open Source: Ubiquitous culture of reuse
44. Why isn’t academia like this?
47. http://dx.doi.org/ 10.1051/0004-6361
48. Careers are based on paper counts
49. Careers are based on paper citations
50. Three major problems
51. 1. ’Novel’ results preferred
52. 2. Reduced collaboration
53. 3. The format sucks
55. Explain what you did
56. So that others can repeat
57. Everybody learns
58. It’s the way that we explain that matters most
60. State of the art technology
61. State of the art technology… for the late 17th century* * Michael Nielsen
63. Data, methods, prose
64. http://www.nature.com/news/2011/111005/full/478026a.html
65. BIG SCIENCE
69. Complex stuff Numbers, data Science!
70. Reproducibility Data intensive
71. Verification may take years (if at all)
73. What do open source collaborations do well?
74. Open source collaborations Open Source vs Open Collaborations
75. Open source collaborations Open Source: the right to modify, not the right to contribute.
76. Open source collaborations Open Collaborations: a highly collaborative development process and are receptive to contributions of code, documentation, discussion, etc from anyone who shows competent interest.
77. Open source collaborations Open Collaborations: a highly collaborative development process and are receptive to contributions of code, documentation, discussion, etc from anyone who shows competent interest. THIS
78. Ubiquitous culture of reuse
79. Expose their collaborative process
80. How do 4000 people work together?
81. The pull request
89. discuss improve Code first, permission later
90. Every time this happens the community learns
95. Merged pull requests
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. (doesn’t have to mean this) Open Public? =
101. ‘Open Source’ way of working
102. Open (within your team, department or institution)
103. Electronic & Available
104. Asynchronous, exposed process
105. Lock-free
106. Low friction collaboration
107. Academia can learn from open source
108. Academia must learn from open source
110. What’s happening in academia today?
111. Collaboration around code
117. Collaborative authoring
120. Collaborative teaching
124. Where might more significant change happen?
125. Where do communities form?
126. Around a shared challenge?
127. Around shared data?
129. 10 ? n Level 1 (continual) Level 2 (periodic)
130. Supernovae Weak lensing Active Galactic Nuclei Solar System Galaxies Transients/variable stars Large-scale structure Stars, Milky Way Strong lensing Informatics and Statistics Dark Energy (DESC)
132. Software composed of many components
133. Your software should be the thing that is different
134. science too! Your software should be the thing that is different
135. Scientific data is becoming more open
136. http://www.nature.com/news/2011/111005/full/478026a.html
137. How do we make this behaviour the norm?
138. Credit
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 paper about code is basically just advertising” David Donoho http://www.stanford.edu/~vcs/Video.html
150. How to derive meaningful metrics from open contributions?
152. Trust
158. Discoverability
160. Barriers are cultural, not technical
161. Why should we care?
162. Because we paid for it?
163. Because open=good?
164. Because care about the creation of knowledge?
165. Open source has solved much of what academia needs
166. Our challenge is to adapt and evolve the academy in this new collaborative age
167. Thanks arfon@github.com @arfon "