Collaborative Research: Collaboration Front and Center: Evaluating Interdisciplinary Networks in Research Institutes

About This Grant

The aim of this project is to analyze how centers influence scientific collaboration across U.S. research universities, identifying the structural and organizational factors that predict their success. University research centers and institutes play a pivotal role in advancing interdisciplinary collaboration and knowledge production. These entities are often designed to break down disciplinary boundaries and promote innovation. This research fills a gap in knowledge of their long-term impact on researcher networks, institutional structures, and scientific outcomes. The project serves the national interest by improving the effectiveness of research investments—guiding universities, funders, and decision makers in designing more effective research environments—and by developing research methods using artificial intelligence. Public-facing tools, including an open database, make these insights broadly accessible to decision-makers. This work also strengthens U.S. research infrastructure by equipping institutions with evidence-based strategies to support interdisciplinary science, translational research, and the next generation of cutting-edge research centers. This study builds a comprehensive, longitudinal dataset of research centers and institutes at over 300 U.S. universities linked to affiliated faculty, funding sources, and collaborative research outputs. Using this dataset, the project (1) maps center affiliations and associated collaboration networks, (2) classifies centers by their founding collaboration structures and analyzes how those relate to long-term performance, interdisciplinarity, and scientific impact, and (3) uses natural experiments to estimate the causal effects of center affiliation on individual collaboration practices and interdisciplinary research production. These aims are accomplished through advanced computational and AI techniques, including affiliation disambiguation algorithms, network analysis, large language models, and quasi-experimental methods. The resulting empirical and methodological contributions inform theories of collaboration, organizational science, and the science of science, while also generating practical tools and guidance to support the future of interdisciplinary research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.