Checklist to Ensure Your Data Support the FAIR Principles

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.

FAIR Checklist


☐ 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).

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☐ 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.

☐ Indicate any terms of use clearly.

☐ The metadata exportable should be in a machine-readable structured text-based format (e.g., XML, JSON)​.

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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.