Data Purchasing in Research‑Intensive Universities: Mechanisms, Maturity, and Change from 2023 to 2026

Licensed datasets are increasingly essential for computational, quantitative, and interdisciplinary research, yet institutional support for purchasing these resources remains inconsistent across academic environments. This presentation shares findings from a longitudinal study examining how research intensive universities supported data purchasing in 2023 and how those mechanisms evolved by 2026. The analysis focuses on four primary support models: request a purchase workflows, grant or award programs, special collection acquisition pathways, and institution level procurement policies. Using a structured environmental scan, university library websites, service pages, and procurement policies were reviewed across two time points. Mechanisms were classified using standardized criteria and assigned maturity scores ranging from unclassified to fully institutionalized procurement policy. Quantitative analysis was used to compare support status, mechanism transitions, maturity trajectories, and limitations associated with each mechanism. Results show modest growth in institutional support between 2023 and 2026, with request based mechanisms emerging as the most expanded model. However, maturity trajectories varied substantially across institutions, with some advancing toward more formalized governance structures and others regressing. Limitations remained prevalent across most mechanism types, suggesting persistent structural barriers for researchers seeking access to licensed datasets. Such structural limitations can exacerbate existing inequities in access to datasets. These findings highlight opportunities for libraries to strengthen data purchasing workflows, improve transparency, and reduce friction for researchers navigating dataset acquisition. By the end of this session, attendees will have a concise overview of national trends, a maturity framework for evaluating local data-purchasing infrastructure, and practical insights to improve support models within data services and research support units.