EGUsphere EGUsphere EGUsphere EGUsphere Copernicus Publications Göttingen, Germany 10.5194/egusphere-2026-3537 Ideas and perspectives: Addressing environmental challenges using distributed data generation: From collaborative networks to artificial intelligence-enabled science Goldman Amy E. https://orcid.org/0000-0003-0490-6451 1 Stegen James C. https://orcid.org/0000-0001-9135-7424 2 3 Borer Elizabeth T. 4 Hensley Robert https://orcid.org/0000-0001-8542-087X 5 Myers-Pigg Allison https://orcid.org/0000-0002-6905-6841 6 Borton Mikayla A. 7 Muradyan Paytsar https://orcid.org/0000-0002-6903-9435 8 Ward Nicholas D. https://orcid.org/0000-0001-6174-5581 9 Andino Jean M. 10 Richardson Andrew D. https://orcid.org/0000-0002-0148-6714 11 12 Leverkus Alexandro B. https://orcid.org/0000-0001-5452-3614 13 Lowry Christopher S. 14 McDowell Nate G. 2 3 Patel Kaizad F. https://orcid.org/0000-0001-6437-7809 2 Davidson Scott J. https://orcid.org/0000-0001-8327-2121 15 Forbes Brieanne https://orcid.org/0000-0001-9240-9687 1 Downs Martha R. https://orcid.org/0000-0003-2833-956X 16 Bolton W. Robert 17 Scheibe Timothy D. 2 Malhotra Avni https://orcid.org/0000-0002-7850-6402 2 Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA School of the Environment, Washington State University, Pullman, WA, 99163, USA Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN, 55108, USA National Ecological Observatory Network, Battelle, Boulder, CO, 80301, USA Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Sequim, WA, 98382, USA Department of Food Science and Human Nutrition, Colorado State University, Fort Collins, CO, 80526, USA Environmental Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA Energy and Environment Directorate, Pacific Northwest National Laboratory, Sequim, WA, 98382, USA Arizona State University, School for the Engineering of Matter, Transport and Energy, Tempe, AZ, 85226, USA School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA Departamento de Ecología, Facultad de Ciencias, Universidad de Granada, Av. Fuentenueva s/n, 18071, Spain University at Buffalo, Department of Earth Sciences, Buffalo, NY, 14260, USA Département des sciences biologiques, Université du Québec à Montréal, Montréal, QC, H2X 1Y4, Canada Long-Term Ecological Research Network, National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA, 93101, USA Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA 10 07 2026 2026 1 26 Copyright: © 2026 Amy E. Goldman et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3537/ The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3537/egusphere-2026-3537.pdf

Distributed data generation, or data collected from multiple sources and locations using standardized approaches and involving coordination among investigators, has emerged as a powerful approach to meet contemporary demands for scalable environmental knowledge. However, practitioners often lack guidance on best practices for distributed data generation, and a framework classifying its modalities is missing.</p> <p>To address these gaps, we developed a conceptual framework organizing distributed data generation along two axes: participant-based (ranging from highly formalized to highly flexible) and method-based (from experimental to observational). This framework provides common vocabulary across modalities and describes how different approaches affect data generation logistics and outcomes. We propose operational best practices across three critical pillars: outreach, operations, and output (i.e., publications, data), leveraging lessons learned from over 35 existing distributed data projects. Lastly, we explore how emerging artificial intelligence (AI) capabilities may help address longstanding challenges in distributed data generation, including in coordination, adaptive sampling, and cross-project data integration. This perspective provides strategies and identifies opportunities to advance distributed data generation for addressing pressing biogeochemical, environmental, and societal challenges. We underscore the transformative potential of distributed data generation for modern, broad-scale environmental research, and provide guidance on how to realize that potential.

 Pacific Northwest National Laboratory N/A National Science Foundation DEB 2142144 DEB 2224545 DEB 2311608 DEB 2425352 DEB 2419138 DBI 2217817 U.S. Department of Energy DE-SC0024218 DE-SC0023520 COMPASS-FME Molecular Observation Network (MONet) River Corridor Science Focus Area DE-AC02- 06CH11357 Next Generation Ecosystem Experiment in the Arctic project European Commission SMART-OAK, funded by the European Union through FORWARDS (Horizon Europe Project No. 101084481) grants to third parties managed by European Forest Institute Universidad de Granada RESISTRES (C-EXP267-UGR23) Québec Ministère du Développement Durable, de l’Environnement et de la Lutte Contre les Changements Climatiques Plan pour une économie verte (PEV)