I’m excited about a new initiative to promote data and analysis/paradigm code sharing, called the The Peer Reviewers Openness Initiative (PRO) https://opennessinitiative.org//. Openness and transparency are core values of science. PRO outlines practical steps to improve open science, and I would like to see improved openness of code and data particularly in my areas of the behavioural sciences and cognitive neuroscience.
Technology (the internet) has advanced to a point now where open access data and code for scientific publications is possible, however the uptake of open science practices has been slow for a number of reasons. For one thing, better incentives are needed for the transition to open science practices. But I think another key thing holding our area of science back is that most researchers in our area don’t even know what things like GitHub, Mozilla Science Lab, and FigShare, etc are (I didn’t until very recently). Also at the outset it seems like a bit hassle to learn how to use these tools. Even though I’m now finding it is not too bad – I’m a beginner with these kinds of open science methods but there are some really good free short courses from Johns Hopkins University to help learn how to use some of the tools for open science, I’ve provided links to 3 of these below:
(1) https://www.coursera.org/course/datascitoolbox – a short course on tools for open science including how to use GitHub to make all of your code available (and also use other’s code!)
(2) https://www.coursera.org/course/rprog – a short course to use** R** to script your whole analysis for a publication from start to finish to show others exactly what was done from raw data to results (any stats software that allows scripting will do, but R is better than SPSS, for example, since R can be downloaded for free). I’ve just started making the switch from SPSS to R for my inferential statistics since R makes sharing analysis code easier. I will still likely do my signal processing in MATLAB though.
(3) https://www.coursera.org/course/repdata – a short course on tools for** Reproducible Research** – e.g. combine GitHub, R pubs, FigShare to make both the data and code easily available and citable with a DOI
I’m going through these courses in my spare time at the moment, and hope to make my next scientific publication fully open access, in line with the ideal of reproducible research, so that other scientists can verify and build upon my findings.
Open science FTW!