Hejblum, B.P.; Kunzmann, K.; Lavagnini, E.; Hutchinson, A.; Robertson, D.S.; Jones, S.C.; Eckes-Shephard, A.H. Realistic and Robust Reproducible Research for Biostatistics. Preprints2020, 2020060002. https://doi.org/10.20944/preprints202006.0002.v1
Hejblum, B.P., Kunzmann, K., Lavagnini, E., Hutchinson, A., Robertson, D.S., Jones, S.C., & Eckes-Shephard, A.H. (2020). Realistic and Robust Reproducible Research for Biostatistics. Preprints. https://doi.org/10.20944/preprints202006.0002.v1
Hejblum, B.P., Sacha C. Jones and Annemarie H. Eckes-Shephard. 2020 "Realistic and Robust Reproducible Research for Biostatistics" Preprints. https://doi.org/10.20944/preprints202006.0002.v1
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.
Biostatistics; Data management; Reproducibility; Workflow automation
Computer Science and Mathematics, Software
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