FAIR Metrics : Framework to understand how increasing the FAIRness is ...

In the last years, a lot has been going on in the space of FAIR (Findable, Accessible, Interoperable, and Reusable) data and other online resources, to make them comply with the 15 Principles of FAIR Data (as they were originally stated in the “The FAIR Guiding Principles for scientific data management and stewardship“).

To understand how increasing the FAIRness of digital resources might maximize their reuse, we need to evaluate the FAIRness of digital objects (DOs) through metrics, - to obtain an assessment that provides feedback to content creators about the degree that they enable others to find and reuse DOs. 

This means, that communities must not only understand what is meant by FAIR, but must also be able to monitor the FAIRness of their digital resources, in a realistic, but quantitative manner. 

"FAIRness - the degree to which a digital resource is Findable, Accessible, Interoperable, and Reusable - is aspirational, yet the means of reaching it may be defined by increased adherence to measurable indicators", - A design framework and exemplar metrics for FAIRness, by Wilkinson e t al., bioRxiv, 2017

The GO FAIR Metrics Group focuses on the development of such metrics to assess compliance to each and every one of the FAIR principles. To this end, the group has created a cogent framework for developing FAIR metrics manifested as a simple form with questions that structures fruitful conversations about proposed metrics.

The group has created several exemplar metrics that could be broadly applicable; however, additional metrics may be designed and published through the group's open submission process, or simply shared within your community through your normal communication channels.

The groups' proposed FAIR Metrics can be found HERE.

The approach proposed to publishing FAIR Metrics is, itself, FAIR...

This takes the form of a FAIR Accessor (a kind of Linked Data Platform Container), which describes :

  • a subset of metrics,
  • the community to which they are applicable,
  • other relevant metadata, and
  • links to each of the associated metrics metadata documents. These metadata documents are written in YAML, and follow the smartAPI annotation patterns for Web Services. As such, each of these documents contains a link to the Metric itself - a Web interface capable of testing a resource's compliance with that metric.

Final recommendation for the core set of FAIR metrics is planned to be released in March 2018. FIND OUT MORE

Related content: 

Interoperability in practice and FAIR data principles
How to make EOSC services FAIR? Experience and challenges
Revisiting the FAIR principles for the European Open Science Cloud


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