Advancing Research Impact Evaluation in the Digital Era

Insights from EU-Funded Rare Disease Projects

Abstract

This study presents a data-driven methodology for evaluating the impact of publicly funded research, addressing the growing complexity of research and innovation landscapes. By integrating diverse data sources (including publications, clinical trials, and company websites) and leveraging advanced analytics such as natural language processing (NLP) and deep learning workflows, this approach overcomes traditional limitations in research impact evaluation. A case study on rare diseases demonstrates how the methodology uncovers pathways linking research outputs to societal benefits while balancing automation with expert validation to ensure accuracy and relevance. These findings underscore the strategic importance of robust, data-driven insights for aligning research priorities with evolving societal imperatives.