Why is it so important to document or describe my research?
Adam Savage (Mythbusters) says it best.
An important part of the research process is documenting the plan, what actually happened, and your thoughts about what happened or didn't, and why.
Always take better notes than you think you need. Many problems that happen during analysis and reporting can be prevented by taking detailed notes. Think of it as writing notes to your future self.
Think about what information would be needed to understand and analyze your data, and/or replicate your results in 20 years. Then think about how researchers in your field usually do that. Do they use lab notebooks, procedures manuals, protocols, readme.txt files, or something else? If you don't know, ask your faculty advisor or supervisor.
The answers to these two questions should tell you what about your research is important to describe and how. For more details about what you might need to document for your project and the data specifically, see the lists below.
Project-level details you should document:
Name of the project
Principal investigator and collaborators
Dataset handle (DOI or URL)
Data publication date
Time period of data collection
Dataset usage rights
Data-level documentation is much more specific and may include, among others:
Data origin: experimental, observational, raw or derived, physical collections, models, images, etc.
Data type: integer, Boolean, character, floating point, etc.
Data acquisition details: sensor deployment methods, experimental design, sensor calibration methods, etc.
File type: CSV, mat, xlsx, tiff, HDF, NetCDF, etc.
The RDA Metadata Standards Directory Working Group is supported by individuals and organizations involved in the development, implementation, and use of metadata for scientific data. The overriding goal is to develop a collaborative, open directory of metadata standards applicable to scientific data can help address infrastructure challenges.
This checklist is designed to help you understand what someone outside your research project (or you in 5-10 years) would need to know about your data in order to build on your work. For more information on preparing your data for reuse, check out our exercise on how to plan for data reuse.