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Enabling data‐driven collaborative and reproducible environmental synthesis science

Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data. When providing scientific computing supp...

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Main Authors: Julien Brun, Nicholas J. Lyon, Angel Chen, Ingrid Slette, Gabriel De La Rosa, Jennifer E. Caselle, Frank W. Davis, Martha R. Downs
Format: Artigo
Jezik:Inglês
Izdano: Wiley 2025-06-01
Serija:Methods in Ecology and Evolution
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Online dostop:https://doi.org/10.1111/2041-210X.70036
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