All Things Open 2014 - Day 1 Wednesday, October 22nd, 2014 Arfon Smith Chief Scientist for GitHub Open Government/Open Data What Academia Can Learn from Open Source Find more by Arfon here: https://speakerdeck.com/arfon

1. What Academia Can Learn
from Open Source
!
Arfon Smith
arfon@github.com
@arfon
Creative Commons Attribution 3.0 Unported License
"

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. httttp://www.flflickr.com/photos/jeasmoaiesgtirlobneortmy/

17. http://amandabauer.blogspot.com/

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. 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. Numbers, data Science!
Complex stuff

70. Reproducibility
Data intensive

71. Verification may take years
(if at all)

73. What do open source
collaborations do well?

74. Open Source vs
Open Collaborations
Open source collaborations

75. Open Source: the right to
modify, not the right to
contribute.
Open source collaborations

76. Open Collaborations: a highly
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: a highly
collaborative THIS
development
process and are receptive to
contributions Open source of collaborations
code,
documentation, discussion, etc
from anyone who shows
competent interest.

78. Ubiquitous culture of reuse

79. Expose their collaborative
process

80. How do 4000 people
work together?

81. The pull request

89. Code first, permission later
discuss improve

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. Open = Public?
(doesn’t have to mean this)

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. 10n ?
Level 1 (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 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
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