The FAIR principles (and their 15 subprinciples) provide guidelines for creating digital resources such as datasets, code, workflows, and research objects, in a way that makes them Findable, Accessible, Interoperable, and Reusable (FAIR).
The FAIR principles do not strictly define how to achieve a state of "FAIRness". Rather they describe a continuum of features, attributes, and behaviours that will move a digital resource closer to that goal.
Read more about the FAIR guiding principles and find guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets at:
In each principle one or two elements were identified that are specific on what a researcher actually needs to do. These elements have been rephrased in so-called FAIRmetrics to make them measurable. By providing information on these FAIRmetrics, a researcher can show how FAIR his data are.
The scheme shows the measurable items within the FAIR principles, the FAIRmetrics.
Read more about the FAIR principles and levels of FAIRness in:
A design framework and exemplar metrics for FAIRness (2018)
The article shows how a groups of stakeholders established a FAIR Metrics group (http://fairmetrics.org) to define ways to measure FAIRness of data (and other digital objects) by making use of FAIRmetrics. The group states that:
FAIRmetrics may involve specific challenges for research communities. A community may therefore define certain metrics according to standards or procedures that they (should) use.
Read more how FAIRmetrics relate to community specific challenges, and find a framework for creating metrics that are defined for a research community or topic.
Ideally, reusable data (and other digital resources) can be found on the internet. Therefore they need to be accompanied by metadata (= information about the dataset) that can be read by a computer. In other words: the information about the reusable dataset needs to be machine readable or machine actionable.
FAIRmetrics are a specific type of metadata. They tell us about the FAIRness of a dataset. Also FAIRmetrics need to be machine actionable.
By applying the design framework for creating FAIRmetrics, you ensure that the metrics become machine actionable.
Read more about how GO FAIR organises so-called “Metadata-4-Machines” workshops to define community specific FAIRmetrics that are findable on internet (machine actionable).