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arXiv · 2604.16317

Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature

Runwen You·Tong Xia·Jingzhi Wang·Jiankun Zhang·Tengyao Tu·Jinghua Piao·Yi Chang·Yong Li
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Abstract

Urban data support a wide range of applications across multiple disciplines. However, at the global scale, there is no unified platform for urban data discovery. As a result, researchers often have to manually search through websites or scientific literature to identify relevant datasets. To address this problem, we curate an open urban data discovery portal, \textit{UrbanDataMiner}, which supports dataset-level search and filtering over more than 60{,}000 urban datasets extracted from over 15{,}000 Nature-affiliated publications. \textit{UrbanDataMiner} is enabled by \textit{Paper2Data}, a novel large-scale LLM-driven pipeline that automatically identifies dataset mentions in scientific papers and structures them using a unified urban data metadata schema. Human-annotated evaluation demonstrates that \textit{Paper2Data} achieves high recall (approximately 90\%) in dataset identification and high field-level precision (above 80\%). In addition, \textit{UrbanDataMiner} can retrieve over 9\% of datasets that are not easily discoverable through general-purpose search engines such as Google. Overall, our work provides the first large-scale, literature-derived infrastructure for urban data discovery and enables more systematic and reusable data-driven research across disciplines. Our code and data are publicly available\footnote{https://github.com/Yourunwen/Paper2Data}.

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Runwen You, Tong Xia, Jingzhi Wang, Jiankun Zhang, Tengyao Tu, Jinghua Piao, Yi Chang, Yong Li. 2026-02-09. Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature. https://arxiv.org/abs/2604.16317

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