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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Bibliographic Details

Published in:	Methods in Ecology and Evolution
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:	Article
Language:	English
Published:	Wiley 2025-06-01
Subjects:	collaborative science data management environmental data science reproducible science synthesis science
Online Access:	https://doi.org/10.1111/2041-210X.70036