Randomized trial introduces a self-assessment tool for dataset quality in researchers, suggesting improved compliance with FAIR principles.
Key Points
The aim is to develop a self-assessment tool that helps researchers validate dataset quality before submission, enhancing compliance with FAIR principles.
Developed an open-source review dashboard integrated with Dataverse.
Introduced Python-based automated checks for systematic quality assessment.
Created a self-assessment tool providing feedback on metadata completeness and consistency.
The self-assessment tool helps prevent common metadata issues before formal review, improving submission success.
Automated checks flag issues like missing DOIs, unclear licensing, and insufficient documentation, improving metadata standards.
Research support strengthened through actionable feedback, leading to more complete datasets.