Have you ever tried working with data that is Frustrating, Obfuscated, Unmanaged, and maybe even Lost? FOUL data can negatively impact all aspects of your research workflow and eat up lots of extra time and effort. Instead, strive for data that is Findable, Accessible, Interoperable, and Reusable – in a word, FAIR. Data that are truly FAIR are even machine-findable, -accessible, -interoperable, and -reusable. If that’s not enough to convince you to keep a weather eye out for FAIR data, then consider this: FAIR data are citable and lead to trackable credit and impact.

Whether your data are FOUL or FAIR is in your hands. You can avoid FOUL (frustrating, obfuscated, unmanaged, lost) data and start turning it FAIR with attention to a few areas:

• Findable: When you publish your data to a research data repository, make sure you get a globally unique and persistent identifier (e.g., DOI) that, when linked, directs those interested to a landing page with rich metadata.
• Accessible: Choose a repository or data publishing platform that can negotiate Findability through a standard communications protocol (e.g., HTTP), and if required, allows controlled access to restricted data via clearly articulated requirements (e.g., HTTPS, HMAC authentication).
• Interoperable: When structuring your data and writing your metadata use standard schemes and vocabularies.
• Reusable: Think about what you would need to know about a data set in order to feel confident about reusing it, and then do that! Don’t forget to provide a data use license so that people know how those data can and should be used.

Questions about finding your way to FAIR? Contact us at Data Management Services (data-management@mit.edu) and start developing your data management practices so you can avoid a FOUL data front and start experiencing the promise of FAIR data today!