Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results

Abstract:Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to understand the 'downstream' effects or impacts of open science. PLOS and DataSeer have developed a new LLM-based indicator to measure an important effect of open science: the reuse of research data. Our results show a data reuse rate of 43%, which is higher than established bibliometric techniques. We show that data reuse can be measured at scale using LLMs and generative artificial intelligence. The positive effects of research data sharing and reuse may currently be underestimated.

v2: 12 pages. Revised and accepted following feedback from review process of 30th Annual International Conference on Science and Technology Indicators. Revisions are the addition of 1) information on the definition of "data reuse" and 2) discussion of prevalent errors identified in the model. V1: 12 pages. Submitted to 30th Annual International Conference on Science and Technology Indicators

Digital Libraries (cs.DL); Computation and Language (cs.CL)

arXiv:2604.28061 [cs.DL]

(or arXiv:2604.28061v2 [cs.DL] for this version)