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

• URL: http://arxiv.org/abs/2604.16317v1
• Date: Mon, 09 Feb 2026 13:24:55 GMT
• Title: Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature
• Abstract summary: textitUrbanDataMiner supports dataset-level search and filtering over more than 60,000 urban datasets extracted from over 15,000 Nature-affiliated publications.<n>textitPaper2Data automatically identifies dataset mentions in scientific papers and structures them using a unified urban data metadata schema.<n>textitUrbanDataMiner can retrieve over 9% of datasets that are not easily discoverable through general-purpose search engines such as Google.
• Score: 20.05631006931984
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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