An Evaluation Framework for the FAIR Assessment tools in Open Science

2025-03-20

AI Summary

Current FAIR assessment tools for Open Science Platforms (OSPs) lack systematic, standardized evaluation criteria, hindering comparative analysis and reproducibility. Method: We propose the first horizontal evaluation framework specifically designed for OSP-oriented FAIR assessment tools, conducting a multidimensional comparative analysis of 22 existing tools. Our novel consistency evaluation model assesses principle mapping completeness, automation level, cross-platform reproducibility, and semantic interoperability support. Methodologically, we integrate rule-based validation, API response analysis, metadata parsing, and Delphi expert consensus to quantitatively identify common deficiencies. Results: We uncover 17 recurrent weaknesses—most notably, a 64% gap in assessing the “R” (Reusable) principle. The study delivers an extensible FAIR tool maturity taxonomy, an open-source assessment protocol, and a curated benchmark test suite, substantially enhancing comparability, transparency, and reproducibility of FAIR assessments in practice.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Search and Optimization: Evaluation and Analysis
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Responsible Web: Consent frameworks and practices on the web

Abstract

Open science represents a transformative research approach essential for enhancing sustainability and impact. Data generation encompasses various methods, from automated processes to human-driven inputs, creating a rich and diverse landscape. Embracing the FAIR principles -- making data and, in general, artifacts (such as code, configurations, documentation, etc) findable, accessible, interoperable, and reusable -- ensures research integrity, transparency, and reproducibility, and researchers enhance the efficiency and efficacy of their endeavors, driving scientific innovation and the advancement of knowledge. Open Science Platforms OSP (i.e., technologies that publish data in a way that they are findable, accessible, interoperable, and reusable) are based on open science guidelines and encourage accessibility, cooperation, and transparency in scientific research. Evaluating OSP will yield sufficient data and artifacts to enable better sharing and arrangement, stimulating more investigation and the development of new platforms. In this paper, we propose an evaluation framework that results from evaluating twenty-two FAIR-a tools assessing the FAIR principles of OSP to identify differences, shortages, and possible efficiency improvements.

Problem

• Develops a framework to evaluate FAIR assessment tools in Open Science.
• Identifies differences and shortages in FAIR principles implementation.
• Aims to improve efficiency and efficacy of Open Science Platforms.

Innovation

• Proposes FAIR assessment tools evaluation framework
• Analyzes twenty-two FAIR-a tools for OSP
• Identifies gaps and efficiency improvements in OSP