Extra effort is required by data analysts who are committed to reproducibility in science. This extra work is not always fully rewarded. However, attention to reproducibility is important not only for the integrity of the study at hand, but for moving entire fields forward by allowing others to adapt and continue the research. This new section is intended to showcase and reward outstanding efforts to share reproducible, adaptable workflows.
Source Code for Biology and Medicine is accepting submissions to its new section R/Bioconductor Workflows. Authors should indicate in their covering letter that they are submitting to R/Bioconductor Section. Manuscripts submitted to this section will be handled by Section Editor Dr. Levi Waldron at City University of New York in USA. For more information on the article type Workflows, please check the Instructions for Authors.
Workflows do not need to provide novel software, but should:
be of generalizable utility for a problem of broad interest.
be easily adaptable by other analysts to new data. The workflow may reproduce a previously published paper.
adhere to high standards of reproducibility and documentation for both data and code. Implementation can be as a workflow on the Bioconductor website, an Amazon or Docker image, or other approaches that allows straightforward cross-platform reproducibility of the analysis.