Dataset Information

Creating and sharing reproducible research code the workflowr way.

ABSTRACT: Making scientific analyses reproducible, well documented, and easily shareable is crucial to maximizing their impact and ensuring that others can build on them. However, accomplishing these goals is not easy, requiring careful attention to organization, workflow, and familiarity with tools that are not a regular part of every scientist's toolbox. We have developed an R package, workflowr, to help all scientists, regardless of background, overcome these challenges. Workflowr aims to instill a particular "workflow" - a sequence of steps to be repeated and integrated into research practice - that helps make projects more reproducible and accessible.This workflow integrates four key elements: (1) version control (via Git); (2) literate programming (via R Markdown); (3) automatic checks and safeguards that improve code reproducibility; and (4) sharing code and results via a browsable website. These features exploit powerful existing tools, whose mastery would take considerable study. However, the workflowr interface is simple enough that novice users can quickly enjoy its many benefits. By simply following the workflowr "workflow", R users can create projects whose results, figures, and development history are easily accessible on a static website - thereby conveniently shareable with collaborators by sending them a URL - and accompanied by source code and reproducibility safeguards. The workflowr R package is open source and available on CRAN, with full documentation and source code available at https://github.com/jdblischak/workflowr.

SUBMITTER: Blischak JD

PROVIDER: S-EPMC6833990 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Creating and sharing reproducible research code the workflowr way.

Blischak John D JD Carbonetto Peter P Stephens Matthew M

F1000Research 20191014

Making scientific analyses reproducible, well documented, and easily shareable is crucial to maximizing their impact and ensuring that others can build on them. However, accomplishing these goals is not easy, requiring careful attention to organization, workflow, and familiarity with tools that are not a regular part of every scientist's toolbox. We have developed an R package, <b>workflowr</b>, to help all scientists, regardless of background, overcome these challenges. <b>Workflowr</b> aims to ...[more]

PMID: 31723427

Similar Datasets

Project description:BACKGROUND:There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature. METHODS:The online author's instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal's data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal. RESULTS:A total of 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. A total of 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. A total of 9.1% of journals mentioned data sharing indirectly, and only 14.8% addressed protein, proteomic, and/or genomic data sharing. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing criteria. Open access journals were not more likely to require data sharing than subscription journals. DISCUSSION:Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable.

Dataset Information

Creating and sharing reproducible research code the workflowr way.

Publications

Creating and sharing reproducible research code the workflowr way.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets