Truth be told, I’m not a star at managing data. Often enough I still find myself virtually lost in folders, especially at the end of a rushed day of collecting data, converting files and running analyses. A maze of folders with the most obscure of names, unordered and stored in different directories. What is the difference between ‘results re-analysis 4’ and ‘results analysis version 2’? I wouldn’t be able to say.
This represents bad data management: a france rcs data system that does not work. An alternative to good and bad data management is ugly data management. This is a system that does work, but which nobody would be proud of. A system of quick fixes and shortcuts, of lengthy labels and unusually ordered data files. It’s on the road to good data management and where I occasionally still find myself, at this stage of my research career.
Representing bad data management
Image: “Bad data management”, personal image / CC BY
This is why I admire the concerted effort of faculty and networks of researchers (such as Project TIER) to teach students good data management: to fix both the ugly and the bad. With the wisdom of hindsight, I now know that I have only recently grown to fully appreciate how good data management provides transparency. How it allows us to understand the systems of others, to reproduce the analyses presented in published work and to potentially solve contradicting findings. At the same time, good data management helps us understand and navigate our own systems. It saves time and (potentially) a lot of headache.