Without description, data is hard to understand and use. Make your data FAIR (findable, accessible, interoperable, reusable) by describing it with metadata (data about data). Metadata is sometimes captured through deposit in data repositories, but you can also prepare data dictionaries, codebooks and README files to further describe and contextualize your work.
README files are plain text documents that sit at the top level of project folders and describe the purpose of the project, contact details, and organization of files. Including a README with your work helps ensure that future users will understand the data, any terms, and more.
README files should include:
- Title
- Principle Investigator(s)
- Dates/Locations of data collection
- Keywords
- Language
- Funding
- Descriptions of every folder, file, format, data collection method, instruments, etc.
- Definitions
- People involved
- Recommended citation
For more information about writing README files for data, we recommend Cornell University's comprehensive guide to readme files.