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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.
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Why is this step important
This still step will help you assess the current FAIRness level of your data, which can help . Comparing the current FAIRness level to the previously defined FAIRification objectives, will help you shape the necessary steps and requirements needed to achieve your FAIRification goals [Jolanda] (see this step)[Jolanda]. FAIRInAction].
Furthermore, the outcomes of this assessment can be used to compare against in the Assess FAIRness step to track the progress of you data towards FAIRness . [Hannah; copied from above]
Expertise requirements for this step
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