Pre-FAIR assessment

STATUS: RELEASED

FAIR evaluation results can serve as a pointer to where your FAIRness can be improved. (FAIRopoly)

A pre-FAIR assessment evaluates the current state of your data and its alignment with the FAIR principles. Performing this assessment early makes you aware of possibilities for increasing the FAIRness of your data, which in turn increases its impact and ensures its long-term usability.

Short description

In this phase you assess whether your (meta)data already meets FAIR criteria, such as persistent unique identifiers for data elements and rich metadata. By using FAIRness assessment tooling you can quantify 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 (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.

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 solution plan, a workplan specifically designed for reaching these goals. Furthermore, the assessment can be repeated in the Assess FAIRness step, allowing you to compare the results and check the progress of your data towards FAIRness.

How to

1

Choose the right tool type for your FAIR Assessment

Decide whether to use a manual questionnaire or an automated tool based on your data and use case.

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Decide which type of tool fits your goal(s) best. Broadly, the tools fall into two categories.

• Online self-assessment surveys. Here, the user is presented with an online form, which 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, for example, differences in tests performed and subjectivity of self-assessments surveys. See FAIR Assessment Tools: Towards an “Apples to Apples” Comparisons for more information this.

While we focus specifically on the FAIRness of (meta)data in this step, it is also possible to assess general FAIR awareness, for example by using the FAIR Aware FAIR Aware Short online questionnaire that tests how well you know the FAIR principles before you deposit data. Cross-domain tool provided by DANS.

2

Consider using a well-known FAIR assessment tool

Consider reusing trusted tools like FAIR-Checker, FAIR Evaluator or the ARDC self-assessment.

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The tool pages below provide an overview of some of the more popular tools from both categories.

Online self-assessment surveys

ARDC FAIR Data Self-Assessment Tool

Online self-assessment questionnaire of 12 questions that cover the FAIR principles while providing improvement guidance.

Domains:

Cross-domain

FAIR Data Maturity Model

Domains:

Cross-domain

FIP Wizard

Online questionnaire for recording your community's FAIR Implementation Profile and publishing it as nanopublications.

Domains:

Cross-domain

FAIR Aware

Short online questionnaire that tests how well you know the FAIR principles before you deposit data.

Domains:

Cross-domain

Online (semi-) automated tests

FAIR-Checker

Web tool that tests a web page or DOI against FAIR metrics using the metadata embedded in the page, and suggests how to fix failed checks.

Domains:

Cross-domain

FAIR Evaluator

Online service that runs automated FAIR maturity indicator tests on the identifier of a dataset or metadata record and reports which tests pass or fail.

Domains:

Cross-domain

To find even more tools:

• FAIRassist FAIRassist Overview of FAIR assessment tools and metrics, to help you choose an assessment approach. Cross-domain . Offers an overview of available tools;
• RDMkit. Discusses several solutions, such as the FAIR metrics and FAIRshake;
• FAIR assessment tools: evaluating use and performance. Paper comparing several tools.

3

Prepare before you start your FAIR assessment

Read the guidance, explore example assessments and involve experts to ensure a solid start.

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To successfully do a pre-FAIR assessment, do the following:

• learn from examples (see the practical examples section);
• familiarise yourself with the tool you intend to use;
• involve the necessary experts;
• 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.

Practical examples from the community

For an applied example of The FAIR Evaluator FAIR Evaluator Online service that runs automated FAIR maturity indicator tests on the identifier of a dataset or metadata record and reports which tests pass or fail. Cross-domain , see Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic.

Training

DMP Instructional Trainings
Relevant Trainings for this session might those who initially deal with Data Management Plans. Data Management Plans tend to be the starting point to draft a plan to eventually FAIRIFY the data. Every University Hospital and University in The Netherlands has their own DMP relevant training. One way to find them is to look for them in the Health-RI portfolio of Services.

Specific Trainings on FAIRIFICATION of data
Other relevant trainings deal directly with the FAIR principles and how they can be applied to research data. The FAIR Data to Boost Research and Diagnosis organized by the World Duchenne Organization is an example. To see more information about this particular training visit: FAIR Data to Boost Research & Diagnostic | National Health Data Portal

The Go FAIR Foundation also offers multiple Training a year dedicated to FAIR awareness. For more information look into: Upcoming training | GO FAIR Foundation. It’s however important to notice that these trainings do have a cost.

Suggestions

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