Skip to Main Content

FAIR Data

FAIR Data

FAIR: Findable, Accessible, Interoperable, Reusable

You may have seen the acronym FAIR used in reference to research data, but what does the acronym mean?

And why should you care about FAIR?

FAIR stands for Findable, Accessible, Interoperable, Reusable and came from the 2016 article "The Fair Guiding Principles for Scientific Data Management and Stewardship,"  which called for more specific guidance to researchers who wanted to be good stewards of their data.  The goal of FAIR is to provide actionable measures for data stewardship through the research lifecycle. 

Why should you embrace FAIR? There are several benefits to you as a researcher. First, you are more likely to get appropriate credit for the data, metadata, and other products you create. Because FAIR promotes the use of persistent identifiers, those who use your data will know how to cite your work accurately. Second, FAIR promotes well-documented, accessible, and interoperable documentation, meaning that your data will be more reusable well into the future. Finally, research funders are asking that research projects be consistent with FAIR principles. In short, using the FAIR principles for your data, metadata, and workflows will ensure a long and loving relationship with your data. 

Questions to Ask Yourself

Below are a few questions to ask yourself about your data.

Findable

  • Are you data online and accessible with a unique persistent identifier (DOI or other persistent link) and rich metadata and documentation?

Accessible:

  • Are users able to access your data and the metadata? Are any access restrictions, such as authentication or authorization, clear to potential users?

Interoperable:

  • Can your data be exchanged across different systems or applications? For example, are they available in open file formats? 
  • Does your metadata follow relevant disciplinary standards and provide adequate documentation? Check our Documentation guide for examples and more information.

Reusable:

  • As the ultimate goal is reusability, are the data and metadata well described with detailed information about provenance as well as a clear data usage license?

Action Steps for a Future with Your Data:

Resources for FAIRifying Your Data