Guidance framework to apply good practices in ecological data analysis: Lessons learned from building Galaxy-Ecology

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Coline Royaux, Jean-Baptiste Mihoub, Marie Jossé, Dominique Pelletier, Olivier Norvez, Yves Reecht, Anne Fouilloux, Helena Rasche, Saskia Hiltemann, Bérénice Batut, Marc Eléaume, Pauline Seguineau, Guillaume Massé, Alan Amossé, Claire Bissery, Romain Lorrilliere, Alexis Martin, Yves Bas, Thimothée Virgoulay, Valentin Chambon, Elie Arnaud, Elisa Michon, Clara Urfer, Eloïse Trigodet, Marie Delannoy, Gregoire Loïs, Romain Julliard, Björn Grüning, Yvan Le Bras


Numerous conceptual frameworks exist for good practices in research data and analysis (e.g. Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework to achieve good practices for building analytical procedures based on atomisation and generalisation. We introduce the concept of atomisation to identify analytical steps which support generalisation by allowing us to go beyond single analyses. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomised and generalised.



Bioinformatics, Other Ecology and Evolutionary Biology


biodiversity, Reproducible analyses, Galaxy, Good practices, Atomisation, Generalisation, workflows, ecoinformatics, Conda, container, Common Workflow Language, RO-CRATE


Published: 2024-04-11 14:35


CC BY Attribution 4.0 International

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