Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918476018352128 |
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| author | Cadwallader, Lauren Hrynaszkiewicz, Iain sarin, parth Vines, Tim |
| author_facet | Cadwallader, Lauren Hrynaszkiewicz, Iain sarin, parth Vines, Tim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_28061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results Cadwallader, Lauren Hrynaszkiewicz, Iain sarin, parth Vines, Tim Digital Libraries Computation and Language 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. |
| title | Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results |
| topic | Digital Libraries Computation and Language |
| url | https://arxiv.org/abs/2604.28061 |