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: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.70036 |
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| _version_ | 1849320554492854272 |
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| author | Julien Brun Nicholas J. Lyon Angel Chen Ingrid Slette Gabriel De La Rosa Jennifer E. Caselle Frank W. Davis Martha R. Downs |
| author_facet | Julien Brun Nicholas J. Lyon Angel Chen Ingrid Slette Gabriel De La Rosa Jennifer E. Caselle Frank W. Davis Martha R. Downs |
| author_sort | Julien Brun |
| collection | DOAJ |
| description | 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 support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided. Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills. Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. |
| format | Article |
| id | doaj-art-4e9998fc79c042f99b615bbe7d4e6a59 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-4e9998fc79c042f99b615bbe7d4e6a592025-08-20T03:50:02ZengWileyMethods in Ecology and Evolution2041-210X2025-06-011661061107410.1111/2041-210X.70036Enabling data‐driven collaborative and reproducible environmental synthesis scienceJulien Brun0Nicholas J. Lyon1Angel Chen2Ingrid Slette3Gabriel De La Rosa4Jennifer E. Caselle5Frank W. Davis6Martha R. Downs7Research Data Services, Library University of California Santa Barbara Santa Barbara California USANational Center for Ecological Analysis and Synthesis University of California Santa Barbara California USANational Center for Ecological Analysis and Synthesis University of California Santa Barbara California USADepartment of Ecology, Evolution, and Behavior University of Minnesota St Paul Minnesota USANational Center for Ecological Analysis and Synthesis University of California Santa Barbara California USAMarine Science Institute University of California Santa Barbara California USABren School of Environmental Science and Management University of California Santa Barbara California USANational Center for Ecological Analysis and Synthesis University of California Santa Barbara California USAAbstract 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 support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided. Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills. Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error.https://doi.org/10.1111/2041-210X.70036collaborative sciencedata managementenvironmental data sciencereproducible sciencesynthesis science |
| spellingShingle | Julien Brun Nicholas J. Lyon Angel Chen Ingrid Slette Gabriel De La Rosa Jennifer E. Caselle Frank W. Davis Martha R. Downs Enabling data‐driven collaborative and reproducible environmental synthesis science Methods in Ecology and Evolution collaborative science data management environmental data science reproducible science synthesis science |
| title | Enabling data‐driven collaborative and reproducible environmental synthesis science |
| title_full | Enabling data‐driven collaborative and reproducible environmental synthesis science |
| title_fullStr | Enabling data‐driven collaborative and reproducible environmental synthesis science |
| title_full_unstemmed | Enabling data‐driven collaborative and reproducible environmental synthesis science |
| title_short | Enabling data‐driven collaborative and reproducible environmental synthesis science |
| title_sort | enabling data driven collaborative and reproducible environmental synthesis science |
| topic | collaborative science data management environmental data science reproducible science synthesis science |
| url | https://doi.org/10.1111/2041-210X.70036 |
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