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Making neurophysiological data analysis reproducible. Why and how?

Abstract : Reproducible data analysis is an approach aiming at complementing classical printed scientific articles with everything required to independently reproduce the results they present. ''Everything'' covers here: the data, the computer codes and a precise description of how the code was applied to the data. A brief history of this approach is presented first, starting with what economists have been calling replication since the early eighties to end with what is now called reproducible research in computational data analysis oriented fields like statistics and signal processing. Since efficient tools are instrumental for a routine implementation of these approaches, a description of some of the available ones is presented next. A toy example demonstrates then the use of two open source software for reproducible data analysis: the ''Sweave family'' and the org-mode of emacs. The former is bound to R while the latter can be used with R, Matlab, Python and many more ''generalist'' data processing software. Both solutions can be used with Unix-like, Windows and Mac families of operating systems. It is argued that neuroscientists could communicate much more efficiently their results by adopting the reproducible research paradigm from their lab books all the way to their articles, thesis and books.
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Contributor : Christophe Pouzat Connect in order to contact the contributor
Submitted on : Thursday, September 1, 2011 - 6:21:34 PM
Last modification on : Monday, November 30, 2020 - 2:56:04 PM
Long-term archiving on: : Thursday, March 30, 2017 - 2:25:25 PM


  • HAL Id : hal-00591455, version 3


Matthieu Delescluse, Romain Franconville, Sébastien Joucla, Tiffany Lieury, Christophe Pouzat. Making neurophysiological data analysis reproducible. Why and how?. 2011. ⟨hal-00591455v3⟩



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