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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 using FAIRness assessment tooling [Generic]. By quantifying the level of FAIRness of the data based on its current characteristics and environment, the assessment outcomes can help shape the necessary steps and requirements needed to achieve the desired FAIRification objectives [FAIRInAction](see A Generic Workflow for the Data FAIRification Process and FAIR in Action Framework by FAIRplus).
The how-to section describes a variety of assessment tools based on the FAIR principles.
[Mijke: RDMkit has a page on this → https://rdmkit.elixir-europe.org/compliance_monitoring#how-can-you-measure-and-document-data-management-capabilities ]
Why is this step important
This step will help you assess the current FAIRness level of your data. Comparing the current FAIRness level to the previously defined FAIRification objectives will help you shape the necessary steps and requirements needed to achieve your FAIRification goals [FAIRInAction].
and help you create your solution plan. Furthermore, the outcomes of this assessment can be used to compare against in the Assess FAIRness step to track the progress of you data towards FAIRness . [Hannah; copied from above]
Expertise requirements for this step
The expertise required may depend on the assessment tool you want to use. Experts that may need to be involved, as described in Metroline Step: Build the Team, include:
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Data stewards: can help filling out the surveys and questionnaires
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Research Software Engineer: can help running some of the specialised software
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How to
There are many tools which can help you to gain insight into the FAIRness of your (meta)data before you commence the FAIRification process. Broadly, they fall into two categories: online self-assessment surveys and (semi) automated tests. The first category presents the user with an online form, which the users fills in manually, whereas the second category performs (semi) automated tests 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. Pending: usage statistics | Fill in the 12 questions in the survey, potentially with help of a FAIR expert/data steward. | |
Provided by DANS, this online survey gives a FAIRness score. Furthermore, it provides advice on how to improve the FAIRness of your (meta)data. | Fill in the 12 questions in the survey, potentially with assistance 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 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 zenodoZenodo. | |
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. Q: Where can we find the community FIPs?? Q: How can we get a useful score / outcome that will help us? Advice?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 expierenceexperience. |
Online (Semi-) automated tests
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For even more surveys and (semi-) automated tools, see FAIRassist. Also, see RDMkit discussion several solutions.
Based on the outcomes of the FAIR assessment, you can start to build your solution plan.
Expertise requirements for this step
The expertise required may depend on the assessment tool you want to use. Experts that may need to be involved, as described in Metroline Step: Build the Team, include:
Data stewards: can help filling out the surveys and questionnaires
Research Software Engineer: can help running some of the specialised software
ELSI experts: can help filling out the ELSI related questions surveys and questionnaires
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects. Nivel Example
[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]
[Hannah - copied from the Define FAIR objectives Metroline step]
Amsterdam University of Applied Sciences have a “FAIR enough checklist”. They describe it as follows:
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The first checklist describes the minimum effort for Urban Vitality (UV) research projects and can be applied by researchers with minimal assistance from a data steward. Following this checklist makes the research data quite FAIR to people and somewhat FAIR to machines (computers). The checklist should be used immediately after obtaining research funding.
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Source: https://www.amsterdamuas.com/uv-openscience/toolkit/open-science/fair/fair-data.html
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References & Further reading
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