Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results
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Abstract (arXiv)
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.
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References · 6
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2019The citation advantage of linking publications to research datacited by 7
- 2023How and Why do Researchers Reference Data? A Study of Rhetorical Features and Functions of Data References in Academic Articlescited by 3
- 2024From Data Creator to Data Reuser: Distance Matterscited by 3
- 2025Research Data in Scientific Publications: A Cross-Field Analysiscited by 1
- 2025An analysis of the effects of open science indicators on citations in the French Open Science Monitorcited by 1
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