Metroline Step: Pre-FAIR assessment

STATUS: READY FOR REVIEW

Short description

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

In this pre-FAIRification 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. 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 

Step 1

There are many tools that help to assess the FAIRness of your (meta)data before starting the FAIRification process:

• for an overview of available tools see FAIRassist;

• several tools are evaluated and compared in FAIR assessment tools: evaluating use and performance;

• RDMkit discusses several solutions.

While we focus specifically on the FAIRness of (meta)data in this step, it is also possible to possible to assess general FAIR awareness, for example by using the FAIR Aware tool provided by DANS.

Step 2

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

• 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 EOSC’s FAIR Assessment Tools: Towards an “Apples to Apples” Comparisons for more information this.

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

Online self-assessment surveys

Online (Semi-) automated tests

FAIR Implementation profiles

Another promising development is the FAIR Implementation Profile (FIP), developed by the GO FAIR Foundation. 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. A FIP can 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

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 (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 

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, are described below.

• FAIR data stewards. Specialist who can help filling out the surveys and questionnaires.

• Research software engineers. Specialists who can help running some of the specialised software.

Practical examples from the community 

Pending - Nivel Example

Training

Relevant training will be added in the future if available.

Suggestions

Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.