@article{bibcite_5261,
  author = {Coline Royaux and Jean-Baptiste Mihoub and Marie Joss{\'e} and Dominique Pelletier and Olivier Norvez and Yves Reecht and Anne Fouilloux and Helena Rasche and Saskia Hiltemann and B{\'e}r{\'e}nice Batut and Marc El{\'e}aume and Pauline Seguineau and Guillaume Mass{\'e} and Alan Amoss{\'e} and Claire Bissery and Romain Lorrilliere and Alexis Martin and Yves Bas and Thimoth{\'e}e Virgoulay and Valentin Chambon and Elie Arnaud and Elisa Michon and Clara Urfer and Elo{\"\i}se Trigodet and Marie Delannoy and Gregoire Lo{\"\i}s and Romain Julliard and Bj{\"o}rn Gr{\"u}ning and Yvan Le Bras and The community},
  title = {Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology},
  abstract = {<p>Numerous conceptual frameworks exist for best 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 for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as {\textquotedblleft}atoms{\textquotedblright} composing an analytical procedure. When generalized, {\textquotedblleft}atoms{\textquotedblright} can be used in more than a single case analysis. 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 atomized and generalized.</p>},
  year = {2025},
  journal = {GigaScience},
  volume = {14},
  pages = {12},
  url = {https://hal.sorbonne-universite.fr/IGN-ENSG/hal-04639601v1},
  doi = {https://dx.doi.org/10.1093/gigascience/giae122},
}