What are the best practices in documentation, archiving, publishing of code, data, and other materials? How to keep your file structure organized on your own computers?
This series featured two awesome guests from University of Pennsylvania:
Quick summary of the most important points:
- Always keep future you in mind. And with future, we don’t mean the next few weeks, but the next few years.
- Keep your files on your computer tidy. Organize things which you are likely to recycle (e.g. slides, writing, graphics) together. Organize your research material (e.g. data, analysis files, stimuli) in project folders and stay consistent with folder structure and naming.
- Don’t panic-save everything. Archive data files at the most important steps in a separate folder (raw, preprocessed, analysis#1, analysis#2, wide format) and use the copies in your analysis directory for ongoing analyses and trying out things. Overwrite old files when moving on (Don’t panic, you have that safety copy).
- Write up the documentation of your study directly into a manuscript file which will later be the basis of a published paper. Writing up the experiment protocols, materials and methods descriptions, analysis steps, and the results right away will save you a lot of time and frustration.
- Use OSF (https://osf.io) to archive your materials and data. This is also great for working with multiple people on one project. Keep this as tidy as possible. As soon as you make it public eventually (which we recommend for most projects) this should be the best reflection of your work as a scientist with highest standards.
- If your work entails any kind of coding or writing analysis scripts, github is a great way to keep version control and publish your code.
- Always work as if a stranger would have to pick up your project at any time and finish it for you.