Some checklists have been produced to discuss how FAIR research data are and what measures can be taken to improve FAIRness. For example, a quick and basic checklist (by Cornell University) below to see if your data files support the FAIR Data Principles. They also provide additional tips on how to prepare your data.
☐ Your dataset should be open (if available).
☐ Your dataset should have a DOI.
☐ All files should be in open formats.
☐ Your data should be discoverable through an open search protocol (for example, via Google).
☐ The metadata should include useful disciplinary notation and terminology.
☐ The metadata should include machine-readable standards where available (e.g. ORCIDs (for authors and/or data contributors)).
☐ Provide a citation format for the data.
☐ The metadata exportable should be in a machine-readable structured text-based format (e.g., XML, JSON).
Tips for Preparing Your Data for Sharing
Preparing your data files:
☐ You should include raw or processed data or both, depending on what is most useful or common in a discipline.
☐ Your file formats should be common and open.
☐ Organize the files logically according to your project.
Documenting your data and files:
☐ Describe methods of data collection and file structures.
☐ Reference articles and include ORCIDs of all data contributors.
Depositing your data in a repository:
☐ Zip up all files into one package or dataset.
☐ Select a well-known data repository and upload your data.
☐ Make sure the repository provides a DOI to access and re-use the data.
☐ Provide a license attribution for your data, so users can easily copy and attribute.