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An approach to review the overall success of can be to have individuals not directly involved in the practical implementation work, but familiar with the overall data, reviewing of the outcomes of the FAIRification work against the initial goals [FAIRPlusFAIRinAction]. This provides independent feedback and prevents the danger for work to continue beyond the point where the benefits exceed the cost.
Furthermore, other types of results, such as lessons learned and developed recipes can be disseminated for the benefit of the community [FAIRPlusFAIRinAction].
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FAIRopoly
FAIRification is a continuous process, do not get discouraged by achieving only some of your goals. If that is the case, it may be time to reassess and restart the process optimising it to your project priorities.
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References & Further reading
[FAIRopoly] FAIRopoly https://www.ejprarediseases.org/fairopoly/
[Generic] A Generic Workflow for the Data FAIRification Process: https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[Elixir] https://faircookbook.elixir-europe.org/content/recipes/introduction/fairification-process.html
[Elixir2] A framework for FAIRification processes: https://faircookbook.elixir-europe.org/content/recipes/introduction/metadata-fair.html
[GOFAIR] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/
[RDMkit] https://rdmkit.elixir-europe.org/machine_actionability.html
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[FAIRPlusFAIRinAction] https://www.nature.com/articles/s41597-023-02167-2
Authors / Contributors
Experts who contributed to this step and whom you can contact for further information