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Tool | Description | Work on your side |
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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. From October 2023 until May 2024, the site had around 2500 visitors who actively interacted with the page. | Fill 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 checklist with FAIR maturity indicators and guidelines. These can be used to : assess the FAIRness of (meta)data ;implement the indicators in an evaluation approach. The FAIR Data 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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Another promising development is the FAIR Implementation Profile (FIP), developed by the GO FAIR Foundation. A FIP is a collection of FAIR implementation choices made for all FAIR Principles by a community (for example a research project or an institute).
Once published, a FIP can be reused by others, thus acting as a recipe for making data FAIR by a community, for example a research project or an institute, based on agreements and standards within that community. Therefore, a FIP aids in achieving FAIR principle R1.3, which states that “(Meta)data meet domain-relevant community standards.". A FIP can , in the future, potentially be used to compare your currently used FAIR implementation choices, such as standards used in your dataset, to those used by your community, thus providing a Pre-FAIR score. FIPs and their usage are currently still under active development. For more information, see Creating a FAIR Implementation Profile (FIP), FIP Mini Questionnaire and the FIP Data Stewardship Wizard.
Step 3
Generally, doing To successfully do a pre-FAIR assessments will require the expertise of a data steward to get a reliable value. Once you’ve familiarised assessment, do the following:
learn from examples (see the practical examples section);
familiarise yourself with the tool you intend to use
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;
involve the necessary experts
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(see expertise requirements section);
perform the assessment.
The final evaluation will give insight into the current FAIRness of your data. Depending on the tool used, you may receive feedback on how to improve the FAIRness of your data. Thus, the outcome of the pre-FAIR assessment helps you determine the next steps to achieve your FAIRification goals.
Expertise requirements for this step
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