Realistic and Robust Reproducible Research for Biostatistics - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2020

Realistic and Robust Reproducible Research for Biostatistics

Abstract

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.
Fichier principal
Vignette du fichier
main.pdf (2.16 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03100421 , version 1 (07-01-2021)

Identifiers

Cite

Boris P. Hejblum, Kevin Kunzmann, Ennio Lavagnini, Anna Hutchinson, David Robertson, et al.. Realistic and Robust Reproducible Research for Biostatistics. 2020. ⟨hal-03100421⟩
71 View
400 Download

Altmetric

Share

Gmail Facebook X LinkedIn More