Acknowledged organizations in biomedical and life sciences research: a large-scale analysis of classification, citation, and topic evolution

Abstract Funding support plays a vital role in research, particularly in biomedicine. However, the impact of different types of acknowledged organizations on academic recognition and citation influence remains underexplored. Acknowledgements in scholarly articles provide valuable insights into institutional contributions and the collaborative nature of research. This study conducts a quantitative evaluation of acknowledged organizations in biomedical and life sciences research to assess their influence on citation performance. By categorizing acknowledged entities such as universities, enterprises, research institutions, and government agencies, this study examines four key research evaluation questions related to acknowledgement frequency and citation impact. Additionally, co-acknowledgement network analysis is employed to map the distribution of acknowledgements and identify key funding organizations. The study also tracks the evolution of acknowledged research topics over time to assess alignment between funding priorities and scientific trends. Findings reveal that government agencies and universities are the most frequently acknowledged, while industry collaborations exhibit a notable impact on citations. The National Institutes of Health (NIH) emerges as a central node in co-acknowledgement networks, underscoring its key role in biomedical research funding. Moreover, the alignment between funding mandates and research topics highlights the strategic influence of funding agencies in shaping scientific priorities. These insights contribute to a deeper understanding of the role of acknowledgements in research evaluation and offer evidence-based guidance for funding agencies, universities, and research policymakers. By clarifying the structure of research support networks, this study enhances our comprehension of the academic community that supports scientific progress and provides a data-driven perspective for evaluating funding effectiveness in biomedical research.