Use a checklist to help determine what measures can be taken to improve the FAIRness of your research data.
The quick and basic checklist below (by Cornell University) provides additional tips on how to prepare your data.
Dataset/Files
☐ Your dataset should be open (if available).
☐ Your dataset should have a DOI.
☐ All files should be in open formats (e.g. .PDF, .XML, .rtf)
☐ Your data should be discoverable through an open search protocol (for example, via Google).
Metadata
☐ 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 pre-formated citation for the data and license if appropriate.
☐ Indicate any terms of use clearly.
Tips for Preparing Your Data for Sharing
Preparing your data files:
☐ 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 (avoid proprietary formats like .sav or .psd)
☐ Organize the files logically according to your project.
Documenting your data and files:
☐ Describe methods of data collection and file structures in a READ ME document.
☐ Reference published articles using data 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 license attribution for your data, so users can easily copy and attribute.
"Preparing FAIR data for reuse and reproducibility" By Cornell University, Data Services Management Group is licensed under CC BY 4.0 International