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
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
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)...
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.
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.