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In this pre-FAIRification phase you assess whether your (meta)data already contains FAIR features, such as persistent unique identifiers for data elements and rich metadata, by using FAIRness assessment tooling [Generic]. By quantifying the level of FAIRness of the data based on its current characteristics and environment, the assessment outcomes can help shape the necessary steps and requirements needed to achieve the desired FAIRification objectives [FAIRInAction]. The outcomes of this assessment can be used to compare against in the Assess FAIRness step (step X) to track the progress of you data towards FAIRness.
[Jolanda] Different assessment tools are available which are based on the FAIR principles.
Why is this step important
This still will help you assess the current FAIRness of your data, which can help shape the necessary steps and requirements needed to achieve your FAIRification goals [Jolanda] (see this step)[Jolanda].
Expertise requirements for this step
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[Hannah] There are also these tools [Mijke: these are the ones Nivel used in a recent project - have them write the community example?]:
FIP Mini Questionnaire from GO-FAIR: https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/fip-mini-questionnaire/
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