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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 . By using FAIRness assessment tooling . By quantifying you can quantify the level of FAIRness of the data based on its current characteristics and environment, the . The assessment outcomes can help shape the necessary steps and requirements needed to achieve the desired FAIRification objectives (see A Generic Workflow for the Data FAIRification Process and FAIR in Action Framework by FAIRplus).
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Based on FAIRassist, a website with a manually created collection of various tools, and the publication FAIR assessment tools: evaluating use and performance, several of the more popular tools can be found below. Generally, doing pre-FAIR assessments will require the expertise of a data steward to get a reliable value.
Online self-assessment surveys
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. 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. | |
A FAIR Implementation Profile (FIP) is a collection of FAIR implementation choices made for all FAIR Principles by a community (for example a research project or an institute). It was developed by the GO FAIR Foundation. Pending: asked Kristina questions about how to use the FIPs to assess in practice. TBD: should the FIPs be here? | Fill in the 10 questions in the Mini Questionnaire or create an account on the Datastewardship Wizard for a more user-friendly experience. |
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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]
PRISMA Example (todo)
References & Further reading
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