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<p>Environmental exposures play a critical role in shaping physical and mental health, yet integrating such data into biomedical research remains technically complex and fragmented. The EnvironMENTAL Climate, Urbanicity, Environment and Society (CLUES) framework is an open-source, end-to-end workflow for generating individual-level environmental exposure data. CLUES automates the selection and download of open-access geospatial datasets, standardises spatial and temporal formats, and maps projections, and links resulting environmental variables to individual-level biomedical data, requiring no prior expertise in geospatial data. CLUES covers key environmental domains, including urban and natural space, climate and weather extremes, air pollution, and regional socioeconomic conditions. Designed for extensibility and cross-cohort applicability, it enables multidimensional exposure mapping across global settings and adheres to FAIR (Findability, Accessibility, Interoperability and Reusability) and privacy-compliant data protection principles. In this work, we present the CLUES framework and evaluate its scalability, computational performance, and reproducibility for large-scale biomedical research.</p>

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CLUES A Comprehensive Workflow for Integrating Geospatial Data in Biomedical Research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Marcel Jentsch,
  • Elli Polemiti,
  • Paul Renner,
  • Sören Hese,
  • Kerstin Schepanski,
  • Roland Eils,
  • Andre Marquand,
  • Sven Twardziok,
  • Gunter Schumann,
  • Rieke Aden,
  • Kofoworola Agunbiade,
  • Ole A. Andreassen,
  • Helga Ask,
  • Anastasios-Polykarpos Athanasiadis,
  • Tobias Banaschewski,
  • Antoine Bernas,
  • Sarah J. Böttger,
  • Ragnhild Brandlistuen,
  • Vince D. Calhoun,
  • Xiao Chang,
  • Di Chen,
  • Nina Christmann,
  • Isabelle Claus,
  • Nicholas Clinton,
  • Yuxiang Dai,
  • Sylvane Desrivières,
  • Francisco José Eiroa-Orosa,
  • Guillem Feixas,
  • Sara Fernández-Cabello,
  • Andreas J. Forstner,
  • Jaime Gallego,
  • Stefanie Heilmann-Heimbach,
  • Andreas Heinz,
  • Esther Hitchen,
  • Per Hoffmann,
  • Nathalie Holz,
  • Reiya Itatani,
  • Karina Janson,
  • Tianye Jia,
  • Viktor Jirsa,
  • Hedi Kebir,
  • Rikka Kjelkenes,
  • Vanessa Köhler,
  • Tristram A. Lett,
  • Yuzhu Li,
  • Carina M. Mathey,
  • Andreas Meyer-Lindenberg,
  • Abigail J. Miller,
  • Frauke Nees,
  • Maja Neidhart,
  • Gaia Novarino,
  • Markus M. Nöthen,
  • George Ogoh,
  • Myrto Patraskaki,
  • Charlie Pearmund,
  • Spase Petkoski,
  • Markus Ralser,
  • Michael A. Rapp,
  • Jean-Charles Roy,
  • Tamara Schikowski,
  • Karen Schmitt,
  • Ameli Schwalber,
  • Emanuel Schwarz,
  • Tatjana Schütz,
  • Beke Seefried,
  • Emin Serin,
  • Mel Slater,
  • Peter Sommer,
  • Bernhard Spanlang,
  • Bernd C. Stahl,
  • Ulrike H. Taron,
  • Paul Thompson,
  • Heike Tost,
  • Mira Tschorn,
  • Nilakshi Vaidya,
  • Dennis van der Meer,
  • Henrik Walter,
  • Lars T. Westlye,
  • Johannes H. Wilbertz,
  • Yunman Xia,
  • Allan H. Young,
  • Xinyang Yu,
  • Jiacan Yuan,
  • Yanqing Zhang,
  • Zuo Zhang

摘要

Environmental exposures play a critical role in shaping physical and mental health, yet integrating such data into biomedical research remains technically complex and fragmented. The EnvironMENTAL Climate, Urbanicity, Environment and Society (CLUES) framework is an open-source, end-to-end workflow for generating individual-level environmental exposure data. CLUES automates the selection and download of open-access geospatial datasets, standardises spatial and temporal formats, and maps projections, and links resulting environmental variables to individual-level biomedical data, requiring no prior expertise in geospatial data. CLUES covers key environmental domains, including urban and natural space, climate and weather extremes, air pollution, and regional socioeconomic conditions. Designed for extensibility and cross-cohort applicability, it enables multidimensional exposure mapping across global settings and adheres to FAIR (Findability, Accessibility, Interoperability and Reusability) and privacy-compliant data protection principles. In this work, we present the CLUES framework and evaluate its scalability, computational performance, and reproducibility for large-scale biomedical research.