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

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, textitUrbanDataMiner, which supports dataset-level search and filtering over more than 60,000 urban datasets extracted from over 15,000 Nature-affiliated publications. textitUrbanDataMiner is enabled by textitPaper2Data, 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 textitPaper2Data achieves high recall (approximately 90%) in dataset identification and high field-level precision (above 80%). In addition, textitUrbanDataMiner 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 availablefootnotehttps://github.com/Yourunwen/Paper2Data.