FAIR Data Maturity Model: specification and guidelines
Supporting Output title: FAIR Data Maturity Model: specification and guidelines
|Impact: This document describes a maturity model for FAIR assessment with assessment indicators, priorities and evaluation methods. This is useful for the normalisation of assessment approaches to enable comparison of their results.|
Authors: FAIR Data Maturity Model Working Group
Citation: RDA FAIR Data Maturity Model Working Group (2020). FAIR Data Maturity Model: specification and guidelines. Research Data Alliance. DOI: 10.15497/RDA00045
Note: Supporting output Results of an Analysis of Existing FAIR Assessment Tools can be found here.
Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use research data and metadata. The FAIR principles have to be considered as inspiring concepts but not strict rules. Unfortunately, they often lead to diverse interpretations and ambiguity.
To remedy the proliferation of FAIRness measurements based on different interpretations of the principles, the RDA Working Group “FAIR data maturity model” established in January 2019 aims to develop a common set of core assessment criteria for FAIRness, as an RDA Recommendation. In the course of 2019 and the first half of 2020, the WG established a set of indicators and maturity levels for those indicators.
As a result of the work, a first set of guidelines and a checklist related to the implementation of the indicators were produced, with the objective to further align the guidelines for evaluating FAIRness with the needs of the community.
This document specifies the indicators for the FAIR assessment designed for re-use in evaluation approaches and provides guidelines for their use. The guidelines are intended to assist evaluators to implement the indicators in the evaluation approach or tool they manage.
The exact way to evaluate data based on the core criteria is up to the owners of the evaluation approaches, taking into account the requirements of their community. The objective here is then to make sure that the indicators, the maturity levels and the prioritisation are understood in the same way. The maturity model is not meant as a “how to”, but instead as a way to normalise assessment.
Use of this document
The FAIR data maturity model guidelines primarily address owners of (FAIR) assessment methodologies, including questionnaires and automated tools, as listed for example in FAIRassist.
Nevertheless, this document is not only restricted to these stakeholders. It may also be used by researchers, data service owners, funders and infrastructures in different scientific and research disciplines, industry and the public sector, who are active and/or interested in the FAIR data principles and in particular in assessment criteria and methodologies for evaluating their real-life uptake and implementation level. This document provides definitions and examples for every indicator - as mentioned above - in order to avoid confusion or ambiguity, and aims to provide a clear outline of the framework (i.e. indicators with their maturity levels and priorities) linking the indicators to the principles, and suggesting the way the indicators may be evaluated.
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|FAIR Data Maturity Model_ specification and guidelines_v0.90.pdf||755.69 KB|