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Provided by Australian Research Data Commons, this 12-question online survey provides a visual indication on the FAIRness level of your (meta)data and provides resources on how to improve it. Pending: usage statistics | Fill in the 12 questions in the survey, potentially with help of a FAIR expert/data steward. | |
Based on the FAIR principles and sub-principles, the Research Data Alliance created a list of universal 'maturity indicators'. These indicators are designed for re-use in evaluation approaches and are accompanied by guidelines for their use. The guidelines are intended to assist evaluators to implement the indicators in the evaluation approach or tool they manage. Their work resulted in a checklist (with extensive description of all maturity indicators), which can be used to assess the FAIRness of your (meta)data. The FAIR Maturity Model is recommended by, amongst others, HL7. | Download the Excel file from Zenodo and in the ‘FAIR Indicators_v0.05’ tab, give a score to the 41 different ‘maturity indicators’, by selecting the level from the drop-down menu in the ‘METRIC’- column, that fits the status if your (meta)data best. Potentially perform this with assistance of a FAIR expert/data steward. View the results in the ‘LEVELS' tab. Detailed definitions and examples for all 'maturity indicators’ can be found in the documentation on Zenodo. |
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[Mijke: Nivel has done a pre-assessment in a recent project - have them write the community example? The ZonMw program have written FAIR Improvement Plans, we can contact some of those and ask for example]
Training
Relevant training will be added in the future.
Further reading
Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic has an excellent example of a Pre-FAIRification evaluation using the FAIR Evaluator.