Description
This scenario illustrates which Metroline steps apply to “Evaluating the FAIRness of your data”. Since it’s a small scenario with no actual FAIR improvements, steps such as “Design solution plan” and “Assess FAIRness” are not part of the workflow. Thus, the workflow consists of three steps:
- Define FAIRification objectives to properly define your goals.
- Have a FAIR data steward on board to make sure you have the necessary support.
- Pre-FAIR assessment to assess the FAIRness of your data.
Workflow
Applied example
In this practical example, a researcher’s institute has defined minimum FAIR data requirements. The researcher wants to assess the current FAIRness of their data to determine whether they already comply with these institutional standards.
Define FAIR objectives
| Step | Applied |
|---|---|
| 1 - Understand the FAIR principles | I have a basic understanding of the FAIR principles. |
| 2 - Identify FAIR requirements | Institutional requirements. My institute has set minimal FAIR requirements for data. I want to get an indication if I meet these requirements. |
| 3 - Assess impact | Currently, the impact is minimal, since it’s just exploratory. |
| 4 - Define the target FAIR level | Not applicable. I would like a general indication of my data’s FAIRness to determine whether it complies with my institute’s requirements. |
| 5 - Identify required resources | I’ll need help from a FAIR data steward to find out how FAIR my data currently is. My institute has data stewards. |
| 6 - Organise and outline necessary steps | * Objective 1. Evaluate the FAIRness of my dataset. * Objective 2. Gain insight on how to further improve the FAIRness of my dataset. If do not meet my institute’s FAIR requirements yet, I will use these insights to set up new objectives. |
Have a FAIR data steward on board
Since FAIR is not my expertise, I enlist the help of a FAIR data steward who brings FAIR knowledge.
| Step | Applied |
|---|---|
| 1 - Identify the right data steward | I need a research-oriented data steward. |
| 2 - Determine the steward’s position in the organisation | Department- or division-based. Our department has dedicated data stewards. |
| 3 - Hire or consult a data steward | I need to consult a data steward that has knowledge about: 1. Compliance. Advise on institutional compliance with RDM policies and regulations. 2. Data sharing & publishing. Identify gaps in support for data sharing and publishing. |
| 4 - Ensure support and compliance | Not applicable, this aspect is for the data steward’s manager to take care of. |
SdR. Not 100% sure what to write for step 3 here
Pre-FAIR assessment
I do this step together with the FAIR data steward from the previous step.
| Step | Applied |
|---|---|
| 1 - Types of tools | Since the data isn’t published yet, we decide an online self-assessment survey is most suitable. |
| 2 - Popular tools | After discussing our options, we pick the ARDC FAIR self assessment. This tool: * helps assess how FAIR our data currently is; * provides suggestions on how to further improve the FAIRness of the data. |
| 3 - Effective approach to conducting a pre-FAIR assessment | Since I have the help of a professional FAIR data steward, I’m confident we’ll get accurate results. |
Finished
After completing the pre-FAIR assessment, the researcher has:
- knowledge about the FAIRness of our data (objective 1);
- knowledge on how to further improve the FAIRness (objective 2).
This knowledge can be used to define new FAIRification objectives if necessary.