HEJBLUM, Boris; KUNZMANN, Kevin; LAVAGNINI, Ennio; HUTCHINSON, Anna; ROBERTSON, David; JONES, Sacha; ECKES-SHEPHARD, Annemarie

doi:10.20944/preprints202006.0002.v1

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HEJBLUM, Boris

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

KUNZMANN, Kevin

LAVAGNINI, Ennio

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Document de travail - Pré-publication

Résumé en anglais

The complexity of analysis pipelines in biomedical sciences poses a severe challenge for the transparency and reproducibility of results. Researchers are increasingly incorporating software development technologies and methods into their analyses, but this is a quickly evolving landscape and teams may lack the capabilities to set up their own complex IT infrastructure to aid reproducibility. Basing a reproducible research strategy on readily available solutions with zero or low set-up costs whilst maintaining technological flexibility to incorporate domain-specific software tools is therefore of key importance. We outline a practical approach for robust reproducibility of analysis results. In our examples, we rely exclusively on established open-source tools and free services. Special emphasis is put on the integration of these tools with best practices from software development and free online services for the biostatistics domain.< Réduire

Mots clés en anglais

Biostatistics

Data management

Reproducibility

Workflow automation

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Realistic and Robust Reproducible Research for Biostatistics

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