Computing & Data: From academia to open source

Slides for a lightning talk for the UC Berkeley workshop "Supporting Data Science - A Campus-Wide Workshop": http://vcresearch.berkeley.edu/datascience/workshop

Transcript

1. Computing & Data
   From academia to open source
   Fernando Pérez
   http://fperez.org, @fperez_org
   Fernando.Perez@berkeley.edu
   Henry H. Wheeler Jr. Brain Imaging Center, UC Berkeley
   Supporting Data Science
   Feb 23, 2013
   

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2. Computing and data
   Now part of the DNA of science
   Much more than “the third/fourth branch” of science
   Computing and data are everybody’s problem...
   Therefore they are nobody’s problem
   

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3. An educational problem:
   the computer as a research tool
   All scientists need to own their computational processes.
   This means literacy in statistics, linear algebra, algorithms,...
   But also in ’software carpentry’ skills:
   version control, software design, testing, documentation, ...
   NOT yet another department on campus (ask Dave Culler)...
   

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4. Open Source:
   skills, tools and practices we need!
   The culture where these things get done.
   Wildly collaborative
   Reproducible by necessity
   Version control, testing, documentation, public peer review, etc.
   

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5. Reward Structure in academia:
   we punish all of the above
   Departmental boundaries: interdisciplinary work is a great buzzword,
   not such a great career path.
   Computational heritage is built on code, not on citations of prior
   literature.
   Continuous evolution vs publication milestones
   Authorship in collaborative works vs the first-author paper.
   Scholarship and intellectual effort embedded in the code.
   

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