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

arXiv: https://arxiv.org/abs/2604.28061 · HTML: https://mlsanity.com/paper/2604.28061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The citation advantage of linking publications to research data](https://mlsanity.com/paper/1907.02565/) (2019)
- [How and Why do Researchers Reference Data? A Study of Rhetorical Features and Functions of Data References in Academic Articles](https://mlsanity.com/paper/2302.08477/) (2023)
- [From Data Creator to Data Reuser: Distance Matters](https://mlsanity.com/paper/2402.07926/) (2024)
- [Research Data in Scientific Publications: A Cross-Field Analysis](https://mlsanity.com/paper/2502.01407/) (2025)
- [An analysis of the effects of open science indicators on citations in the French Open Science Monitor](https://mlsanity.com/paper/2508.20747/) (2025)

## 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.