FAIR metrics - Starring your data sets

Organisers: Ingrid Dillo, Elly Dijk, Peter Doorn, Marjan Grootveld, Ellen Leenarts - Data Archiving and Networked Services (DANS)

Duration: 1.5 hours

Do you want to join our effort to put the FAIR data principles into practice? Come and explore the assessment tool that DANS, Data Archiving and Networked Services in the Netherlands, is developing for data repositories.

The aim of our work is to implement the FAIR principles into a data assessment tool so that every dataset which is deposited or reused from any digital repository can be assessed in terms of a score on the principles Findable, Accessible, Interoperable, and Reusable, using a ‘FAIRness’ scale from 1 to 5 stars. In this interactive session participants can explore the pilot version of FAIRdat: the FAIR data assessment tool. The organisers would like to inform you about the project, and look forward to all feedback to improve the tool, or to improve the metrics that are used.

Workshop abstract

The FAIR data principles have rapidly gained a wide support, but to operationalise, measure and implement them appears to be no unambiguous task. Moreover, the FAIR principles bear a great resemblance to the principles underlying the earlier existing Data Seal of Approval (DSA). Actually, they complement each other very well: the DSA can be used to assess the quality of repositories, FAIR the fitness for use of datasets. DANS has started working on a practical operationalisation of the FAIR principles in such a way that they can be measured independently, meaning that a rating of a dataset under one principle is independent from ratings under one of the others. Our aim is to implement the FAIR principles into a data assessment tool (with the name FAIRdat) so that every dataset which is deposited or reused from any data repository can be assessed for its score on the principles of FAIR and overall ‘FAIRness’ on a scale of 5 stars. The present proposal is influenced by existing badge schemes, e.g. the Open Data Certificate, however here we are using star badges to represent the quality of a dataset based on each FAIR principle. Following an assessment using the assessment tool, a dataset will gain its own FAIR profile which can be displayed alongside its record in the FAIRdat database and/or in its location (i.e. repository). We aim to implement this initially into repositories at DANS, but the idea is to expand this internationally, so that any dataset in any location can be reviewed via the independent tool. The aim is to create a FAIRdat website which will be independent from DANS. Moreover, there are more initiatives to develop FAIR metrics, for example in the context of the GO FAIR project, in the Research Data Alliance, and in a Horizon 2020 expert group. DANS is lining up with these initiatives, and aims ultimately to arrive at FAIR data metrics that are globally accepted and that can be applied across disciplines.

This workshop is closely related to the workshop on Open Science Monitor, an EOSCpilot workshop.

About the FAIRdat prototype: The tool runs a series of questions (usually only maximum of 5 per principle) which follow routing options to display the star rating scored per principle. At the end of the assessment, the tool will display the star score of each principle and will also calculate and display the overall ‘R’ FAIRness score.

Target Audience

Research data repository representatives, data librarians, data support staff, researchers, FAIR data experts, and data publishers


  • Introduction to the FAIRdat tool and its future goals, by Dr. Peter Doorn (Director of DANS) - 20 minutes
  • Explore FAIRdat in small groups: assessment of datasets from various disciplines - 45 minutes
  • Feedback and suggestions for improvement - 25 minutes 


  • Peter Doorn, Data Archiving and Networked Services (DANS)
  • Marjan Grootveld, Data Archiving and Networked Services (DANS)
  • Elly Dijk, Data Archiving and Networked Services (DANS)



See full OSFAIR 2017 programme here.