Documentation provides context to understand and use your data.
Imagine you want to restart a research project after several months (or years!) have lapsed. Having robust documentation will make it easier for you to do so. Without it, you may not be able to reuse or replicate your previous data. Data documentation also helps researchers with whom you share your data to understand and (re)use it.
Documentation can take many forms including:
- Methods sections
- ReadMe.txt files
- Research notes
- Code books
- Lab notebooks
Best Practices for creating documentation:
- Create a procedure for creating documentation for your data. The type of documentation needed and how to capture it is dependent on the research project.
- Ideally, you should plan your documentation procedures before starting your research project.
- Documentation should provide as much context as possible. In general, record the who, what, when, where, why and how relating to the data. Don't forget to document abbreviations, important names/locations, data processing steps, etc.
- Your documentation should be safely stored along with your research data. See the Data storage and backup page for more info.
- Most importantly: be consistent with your documentation practices. Consistency is key to ensuring that your data is usable in the future!
