In today's data-driven world, the FAIREST principles serve as a framework for researchers to assess the viability and sustainability of the data they generate, analyze, and share. Data often outlast their creators and users, making it essential for researchers to curate, store, and provide access to this data in ways that facilitate future use. By following the FAIREST principles, researchers can ensure that their data remains relevant and usable long after their initial project concludes.
It’s important to note that not all of the FAIREST principles apply to every project. Researchers are also advised that it may be challenging to incorporate them late in the research process. Therefore, we recommend that researchers consider these principles at the start of a project. If you are reviewing them later, strive to implement as many of the principles as feasible.
Ideally, researchers should aim to create and share data that are:
Findable: easy to search and find for both human users and machines now and in the future
Interoperable: data are in a file or format that allows use by common tools or can be converted into a format that is
Reusable: data are in a format that allow them to be reused
Ethics: data are collected ethically, the methodology is transparent and clear, and community agreements are honored
Source: the source of data collection and the context in which it was created are clearly documented
Trust: users can trust that the data are reliable and valid, were legally and ethically obtained, conform to copyright laws, and will remain accessible for the long-term (as outlined in a long-term data curation plan).
Some other criteria to consider:
Engaging: data are clearly organized and the metadata descriptors are clearly defined
Expertise: Researchers acknowledge their expertise and positionality in the data collection or creation process
Social Connections: social power dynamics and implication of the collection or creation of data set are declared
Timestamped: the time of data creation or collection is easily available to the users
In 2016, a group of scientists proposed the FAIR Data Principles to support the reuse of research data. FAIR data is Findable, Accessible, Interoperable, and Reusable. Good data management, the authors argue, "is the key conduit leading to knowledge discovery and innovation." These principles are recorded and updated in a "living document" hosted by the GO FAIR Initiative.
Three years later, in 2019, a group of Indigenous Studies scholars proposed the CARE principles as a complement to FAIR. They argued that good data management practices that value community relations and social responsibility prioritize Collective Benefit, Authority to Control, Responsibility, and Ethics. According to these principles, users and stewards of Indigenous data should "Be FAIR and CARE."
More recently, a team of data scientists (2023) and a digital humanist (2024) separately proposed expanding FAIR to FAIREST. The former added Engagement, Social Connections, and Trust to FAIR while the latter instead recommended Expertise / Ethics, Source-Mention, and Time-stamping.