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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
GigaScience
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/39937595/ |
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| _version_ | 1868266244707188736 |
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| author | Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan |
| author_facet | Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan |
| collection | PubMed - marine biology |
| contents | Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan Ecology Software Data Analysis Reproducibility of Results Computational Biology 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 "atoms" composing an analytical procedure. When generalized, "atoms" 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. |
| format | Artículo científico |
| id | pubmed_39937595 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | GigaScience |
| record_format | pubmed |
| spellingShingle | Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan Ecology Software Data Analysis Reproducibility of Results Computational Biology Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. Royaux, Coline Mihoub, Jean-Baptiste Jossé, Marie Pelletier, Dominique Norvez, Olivier Reecht, Yves Fouilloux, Anne Rasche, Helena Hiltemann, Saskia Batut, Bérénice Marc, Eléaume Seguineau, Pauline Massé, Guillaume Amossé, Alan Bissery, Claire Lorrilliere, Romain Martin, Alexis Bas, Yves Virgoulay, Thimothée Chambon, Valentin Arnaud, Elie Michon, Elisa Urfer, Clara Trigodet, Eloïse Delannoy, Marie Loïs, Gregoire Julliard, Romain Grüning, Björn Le Bras, Yvan Ecology Software Data Analysis Reproducibility of Results Computational Biology 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 "atoms" composing an analytical procedure. When generalized, "atoms" 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. |
| title | Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. |
| topic | Ecology Software Data Analysis Reproducibility of Results Computational Biology |
| url | https://pubmed.ncbi.nlm.nih.gov/39937595/ |