An article about computational neurophysiological data analysis or modeling in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the analysis/modeling results.
Jon Claerbout (with minor contextual adaptations).
As a reader and/or a referee of neurophysiological papers you probably already wondered a couple of times about questions like:
We can of course all think of a dozen of similar questions. The problem is to find a way to address them. Clearly the classical journal article format cannot do the job. Editors cannot publish 2 versions of each figures to satisfy different readers. Many intricate analysis and modeling methods would require a too long description to fit the usual bounds of the printed paper, etc, etc... Moreover, some, not to say most, readers are still perfectly happy with the papers in their present format. This is perfectly reasonable for we all have a lot of different things to do and we cannot afford to systematically look at every piece of work as thoroughly as described in the previous paragraph. Nevertheless, many people feel uncomfortable with the present way of diffusing scientific information as a canonical (printed) journal article, even if now those can be "enhanced" with supplementary web material. What is needed is a more systematic and more explicit way to describe how the analysis (or modeling) was done.
These considerations have apparently been in air for the last 10 years or so and are now referred to has the reproducible research issue. If you want to know more about it I suggest you to take a look at the following web pages and papers:
You might agree with what I, following others, wrote up to this point but be wondering if it is possible to implement these ideas and actually get closer to reproducible neurophysiological data analysis. The answer if yes, that can be done, thanks to the great Sweave function of the great data analysis software R. R allows you to process some peculiar files (files with a .Rnw suffix) where documentation in LaTeX or HTML format and R code are mixed. Using function Sweave R executes the so called code chunks of these files and outputs a LaTeX or an HTML file where the LaTeX (or HTML) parts of the previous file have been directly copied and where the code chunks have been replaced by the result of their execution (including figures if figures have been generated). In short it allows you to dynamically re-generate an analysis, thanks to Friedrich Leisch and his Sweave R function.
Our spike-sorting software SpikeOMatic comes with two such vignettes in the R terminology or compendiums in the Gentleman and Temple Lang terminology allowing you to re-generate tutorials 1 and 2.
We moreover give now access to a compendium allowing you to fully re-generate our paper Efficient spike-sorting for multi-state neurons using inter-spike intervals information. It contains the complete raw data used in the paper and everything you need to reproduce all our figures. (A Windows version is available as well.)
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