Teaching reproducible research for medical students and postgraduate pharmaceutical scientists

BMC Research Notes

Andreas D. Meid¹ 

[1] Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg;

关键词: Reproducible research;    Reproducibility;    Heterogeneous treatment effects;    Machine learning;    Medical education;   

DOI  :  10.1186/s13104-021-05862-8

来源: DOAJ

【 摘 要 】

Abstract In medicine and other academic settings, (doctoral) students often work in interdisciplinary teams together with researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. They should be fundamentally taught good research practices, especially in terms of statistical analysis. This includes reproducibility as a central aspect. Acknowledging that even experienced researchers and supervisors might be unfamiliar with necessary aspects of a perfectly reproducible workflow, a lecture series on reproducible research (RR) was developed for young scientists in clinical pharmacology. The pilot series highlighted definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, several practical obstacles arose. In this article, reproduction of a working example is commented to emphasize the manifold facets of RR, to provide possible explanations for difficulties and solutions, and to argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. These experiences should raise awareness among educators and students, supervisors and young scientists. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.

【 授权许可】

Unknown