Ben Vincent over at inferencelab.com has a nice post on two simple ways to get set up with a Python scientific computing environment. I hadn’t heard of Wakari before – thanks Ben! Basically this lets you run python scripts on cloud computers, and share these scripts with others without requiring them to set up an identical software installation to yours. You could send collaborators, reviewers or readers a link to a web based interface that would allow them to re-run your analysis. Pretty cool! Yay free software!
Academic Torrents looks like it might be pretty cool for sharing larger data sets (e.g. brain imaging or eye tracking data)…
You may be interested in a recent paper by Jonas Kubilius (link) detailing his reproducibility framework
Psychopy_ext. This is a Python wrapper package for PsychoPy (for stimulus generation and presentation) and various data analysis packages that promises to streamline workflows for conducting typical psychophysical experiments. Looks really useful – great work Jonas!
Regarding my last post, you might be particularly interested in Jonas’ Figure 2, which contains a slightly different suggestion for how to lay out a project directory.
Further to my last post and this blog in general, PLoS Biology has just published an article on Best Practices for Scientific Computing. It’s a great overview of some of the stuff I’m planning to talk about here. It’s by at least one of the developers of Software Carpentry, which I recommend you check out.