Abstract

Researchers and Research Managers (RM) face information overload when tracking funding opportunities in the evolving research landscape. To address this, the UC3M Research Service, in collaboration with Machine Learning (ML) researchers, is developing a recommendation system that matches researchers with relevant European Commission and other funding programs.
The system benefits both researchers and RM by recommending tailored funding calls based on researcher profiles and identifying the best candidates for each opportunity. Results are ranked with a similarity score, showing the percentage match between the researcher and the funding option.
The tool's pilot uniquely integrates the HORIZON Work Programme 2023-2024 (Pillars II and III) with the University Research Portal, which includes researchers' data like publications, projects, leadership, collaborations, and ORCID. Unlike similar tools, it enhances predictions by considering not only publications but also projects. Furthermore, it analyses researchers' output over time to suggest funding relevant to their current and recent interests, accounting for shifts in research trajectories.
We are validating the recommendation system in two steps: 1. comparing its call rankings with actual proposals submitted by our researchers over three years, 2. surveying researchers on whether they would have applied to the top three calls and the suitability of the proposals. A preliminary manual validation is promising, with a more thorough one to be completed by INORMS 2025.
The new tool automates the identification process, improving research management and reducing the administrative burden. It improves accessibility by making it easier to find funding opportunities, thus promoting a more inclusive research environment. This system benefits researchers facing barriers to access, including early career researchers, non-EU researchers, parents with limited time, and researchers in less active or niche HORIZON areas. The tool ensures higher quality proposals by matching funding with expertise and adapts to trends through ML. It also provides insights for strategic decision-making and long-term planning, making research management more sustainable and effective.
Finally, the long-term sustainability of the profession depends on establishing a common understanding and language between RM and researchers. To achieve this, RM must keep pace with the evolving researchers' needs, adopting the same technologies and methodologies that drive their work.