Every PhD student must use Git (aka research data management)

Last Thursday and Friday the SURFAcademy Masterclass Research Data Management in Nederland took place, and Chris Evelo and I presented some biology-world use cases. He focused more on the larger projects (e.g. ISA-TAB, GSCF, and FAIRPort) while I exposed my day to day data management. My day to day work habit looks more or less like this.

Day 0 is to think about how to do it, but the answer is pretty simple: use a version control system, like Git. Because it tracks every bit of what you do, allows for easy back ups, and makes it easy to continue working on a different machine in case you forget to take your laptop adapter home :)

• Day 1: keep an electronic lab notebook (e.g. a version control system; read Git from the Bottom Up)
• Day 2: carefully select data you build on (can you indeed share it with the rest of your arguments in your next paper?)
• Day 3: do you research and store everything
• Day 4: integrate data repositories in your data analyses, e.g. rrdf and knitr
• Day 5: if you like scientific dissemination, collaboration, and progressing science, share your data in public repository, like FigShare, Data Dryad, Dutch Dataverse, 3TU.Datacentrum, DANS, etc. (that's a lot of D-D-D-Data...) or in a domain specific database, like WikiPathways, XMetDb, or DrugMet. And data copyright and licenses and particularly, whatever you chose, be explicit about it and don't let others guess (wrong).
• Day 6: think ahead of reuse, and suitable formats. Consider semantic web and linked data.
• Day 7: did you get impact? Think DataCite, ImpactStory, and Altmetric (and ORCID and DOI along the way).

And here are the slides: