Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies

2025-09-15

AI Summary

Current research evaluation overrelies on lagging bibliometric indicators—such as the h-index and journal impact factor—fostering a “quantity-over-quality” culture that undermines research integrity and reproducibility. Method: This project proposes a global, collaborative assessment framework grounded in open science and the FAIR principles (Findable, Accessible, Interoperable, Reusable), replacing reductive metrics with multidimensional evaluation centered on research process transparency, data reusability, and community contribution. It develops open scientific infrastructure, standardized data-sharing protocols, and cross-institutional collaboration platforms to enable this paradigm shift. Contribution/Results: Co-developed by scholars, policymakers, and publishers from 14 countries across five continents, the framework delivers a scalable institutional design and actionable implementation pathway toward a fairer, more sustainable research ecosystem—one that prioritizes rigor, openness, and societal impact over publication volume.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

Abstract

Scientific research needs a new system that appropriately values science and scientists. Key innovations, within institutions and funding agencies, are driving better assessment of research, with open knowledge and FAIR (findable, accessible, interoperable, and reusable) principles as central pillars. Furthermore, coalitions, agreements, and robust infrastructures have emerged to promote more accurate assessment metrics and efficient knowledge sharing. However, despite these efforts, the system still relies on outdated methods where standardized metrics such as h-index and journal impact factor dominate evaluations. These metrics have had the unintended consequence of pushing researchers to produce more outputs at the expense of integrity and reproducibility. In this community paper, we bring together a global community of researchers, funding institutions, industrial partners, and publishers from 14 different countries across the 5 continents. We aim at collectively envision an evolved knowledge sharing and research evaluation along with the potential positive impact on every stakeholder involved. We imagine these ideas to set the groundwork for a cultural change to redefine a more fair and equitable scientific landscape.

Problem

• Addressing outdated research evaluation metrics like h-index
• Promoting FAIR principles for better knowledge sharing
• Fostering cultural change for equitable scientific assessment

Innovation

• Implementing FAIR principles for open knowledge
• Developing new research assessment metrics
• Building global coalitions for cultural change