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The how-to section describes a variety of assessment tools based on the FAIR principles. The results of the FAIR assessment will help you develop your
Why is this step important
This step will help you assess the current FAIRness level of your data. Comparing the current FAIRness to the previously defined FAIRification objectives will help you shape the necessary steps and requirements needed to achieve your FAIRification goals and help you create your
How to
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There are many tools that help to assess the FAIRness of your (meta)data before starting the FAIRification process. For :
for an overview of available tools see FAIRassist
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several tools are evaluated and compared in FAIR assessment tools: evaluating use and performance
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RDMkit discusses several solutions.
Step 2
Decide which tool fits your goal(s) the best. Broadly, the tools fall into the two categories : online described below.
Online self-assessment surveys
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. Here, the user is presented with an online form, which
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is filled in manually.
(Semi) automated tests. Here (semi) automated tests are performed on a dataset by providing the tool with, for example, a link to an already published dataset.
In both cases, the result gives an indication about the FAIRness of the (meta)data. Additionally, tools may give advice how to improve FAIRness. It is important to bear in mind that outcomes of tools may vary due to differences in tests performed and subjectivity of the self-assessments surveys.
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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 engaged 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 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.The work resulted in a checklist (with extensive description of all maturity indicators), which checklist with FAIR maturity indicators and guidelines. These can be used to:
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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Generally, doing pre-FAIR assessments will require the expertise of a data steward to get a reliable value. Once you’ve familiarised yourself with the tool you intend to use, involve the necessary experts for the final evaluation.
and maybe you can get some more steps from FAIR cookbook, and some of the models that have this step?
What do you think? I can help/do it with one of you if you need help
Expertise requirements for this step
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