As a researcher, you may need to understand how FAIR your data currently is to comply with institutional requirements or to identify areas for improvement. In this use case, you assess the current FAIRness level of your dataset to check compliance and pinpoint specific areas for improvement in how your data is described, accessed, and reused.
Examples
My institute requires datasets to meet a minimum FAIR standard before submission. I want to check how my dataset currently scores and identify what needs improving before I proceed.
Complexity
Low
Key Experts
Data Steward
Outcome
FAIRness Assessment Report
Your Journey
Define your goals for evaluating FAIRness.
Define your goals for evaluating FAIRness.
Start by identifying your goal: assessing how FAIR your dataset currently is to determine whether it meets your institutional requirements. Understanding baseline FAIRness helps you prioritize improvements.
Examples
Researcher
I reviewed the FAIR requirements from my institute and planned publication venue, then checked which aspects of my dataset are most relevant to assess, such as identifiers, metadata, access conditions, and licensing.
Guidance steps
Define FAIRification objectives
Define clear, project-specific FAIRification objectives by aligning requirements, assessing impact and planning the resources and steps needed to make their data FAIR.
View step details
Connect with a data steward for expert guidance.
Connect with a data steward for expert guidance.
Reach out to a FAIR data steward who brings expertise in FAIR principles and assessment tools. Having professional support ensures accurate evaluation and actionable recommendations.
Examples
Researcher
I discussed my dataset, repository options, and metadata with a data steward, who helped me decide which FAIR criteria were most relevant and which assessment approach fit my situation.
Guidance steps
Have a FAIR data steward on board
Understand why a FAIR data steward is needed and how to identify, position and support this role to guide FAIR data management throughout a project.
View step details
Assess your data's current FAIRness level.
Assess your data's current FAIRness level.
Together with your data steward, conduct a pre-FAIR assessment using an appropriate tool (like ARDC FAIR self-assessment or FAIR-Checker) to evaluate how well your data meets FAIR principles and identify specific gaps.
Examples
Researcher
I completed a FAIR assessment of my dataset and metadata using FAIR-Checker, then reviewed the results to identify gaps such as limited metadata, missing persistent identifiers, or unclear reuse conditions.
Guidance steps
Pre-FAIR assessment
Assess the current level of FAIRness of their data using appropriate tools and use the results to identify gaps and plan the next steps in the FAIRification process.
View step details
🎯
FAIR assessment complete
You understand your data's FAIRness and next steps.
Congratulations! You now have clear insights into how FAIR your data is and specific recommendations for improvement. You can use this knowledge to define new FAIRification objectives if needed or confirm compliance with institutional requirements.
