FAIR Guiding Principles
The FAIR guiding principles were designed to ensure that all digital resources can be Findable, Accessible, Interoperable and Reusable by machines and humans published by Wilkinson et al 2016  as shown in Table 1 below. These principles serve as guidelines (rather than being a standard) to support best practices in data management by researchers and data stewards . The FAIR data principles are aspirational; they do not define how to achieve “FAIRness” of data but they describe the features, attributes and behaviours that enable better data management for usage by machines and humans alike.
Table 1: The FAIR Guiding principles adapted from Wilkinson et al 2016 
FAIR Maturity Indicators
Despite widespread agreement over the desirability of the FAIR guiding principles there is a variety of implementations prone to diverse interpretation. The FAIR Toolkit has adopted the open community-driven phase approach of a FAIRness evaluation framework described by Wilkinson et al 2019 , as summarised in Table 2 below. The framework comprises of an intial set of FAIR Maturity Indicators (originall call metrics) which have evolved as a result of community feedback They have been deployed as a public FAIR Evaluator service to create a registry of evaluation results [4,5].
Table 2: FAIR Maturity Indicators adapted from Wilkinson et al 2019 .
FAIR cookbook recipes
- Wilkinson, M. D. et al. 2016 The FAIR Guiding Principles for scientific data management and stewardship. Data 3, 160018. doi.org/10.1038/sdata.2016.18
- Mons, B. et al. 2017 Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Serv. Use 37, 49–56. doi.org/10.3233/ISU-170824
- Wilkinson MD et al 2019 Evaluating FAIR maturity through a scalable, automated, community-governed framework. Sci. Data 6, 174. doi.org/10.1038/s41597-019-0184-5
- FAIR Evaluation Services: https://w3id.org/AmIFAIR
- FAIR Maturity Indicator Tests: https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/metrics