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Predicting the future of publishing

Predicting the future of publishing

My slides from ReCon 2015 in which I attempt to make some predictions about the future of publishing software and data products in academia.

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Arfon Smith

June 19, 2015
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  1. Predicting the future of publishing Arfon Smith @arfon Creative Commons

    Attribution 3.0 Unported License
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  7. !

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  15. 4,000,000 8,000,000 12,000,000 16,000,000 20,000,000 2007 2008 2009 2010 2011

    2012 2013 2014 Repositories
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    2013 2014 Users
  17. not doing this

  18. Software & Data Services

  19. Three assumptions

  20. 1. Open is the new normal

  21. 2. The PDF is an increasingly unsatisfactory way of sharing

    research
  22. 3. We are generally unprepared to share data and software

    in a useful (and creditable) way
  23. Software & Data Services

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  28. BIG SCIENCE

  29. New tools. New ways of working.

  30. New tools. New ways of publishing.

  31. Reproducibility Data intensive

  32. Complex (unpublished) things Numbers, data Science!

  33. Should academia behave more like open source?

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  37. Verification & benchmarking services Likely thing #1:

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  51. Software is an unforgiving medium

  52. Automating processes

  53. “open source is… reproducible by necessity” Fernando Perez http://blog.fperez.org/2013/11/an-ambitious-experiment-in-data-science.html

  54. Benchmarking services

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  64. Most innovation around shared challenges/data products Likely thing #2:

  65. 10 ? n Level 1 (continual) Level 2 (periodic)

  66. Software composed of many components

  67. Your software is the thing that is different

  68. Open Source: Ubiquitous culture of reuse

  69. Ecosystem around data products

  70. Stars Rocks SN WR NEOs Josh Bloom’s Type Ia supernovae

    Level 1 (continual) 10 n
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  74. ‘Normal’ citations won’t be sufficient for software Likely thing #3:

  75. “Academic environments of today do not reward tool builders” Ed

    Lazowska, OSTP event http://lazowska.cs.washington.edu/MS/MS.OSTP.pdf
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  79. “publishing a paper about code is basically just advertising” David

    Donoho http://www.stanford.edu/~vcs/Video.html
  80. Transitive Credit

  81. Paper Author 1 Author 2 Paper Software Data 0.2 0.2

    0.4 0.1 0.1 Paper Software Software Author 1 0.5 0.3 0.1 0.1 http://arxiv.org/abs/1407.5117, Katz & Smith
  82. Authorship isn’t static

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  91. Where does progress come first?

  92. Where do communities form?

  93. Around a shared challenge?

  94. Around shared data?

  95. Be more exact

  96. Where peers can most easily recognise value

  97. Open source has solved much of what academia needs

  98. The challenge is to adapt and evolve the academy in

    this new collaborative age
  99. Thanks. arfon@github.com @arfon "