Open Science and R

TABLE OF CONTENTS:

03 –A: What is Open Science?

“The process of making the content and process of producing evidence and claims transparent and accessible to others” (Munafo et al., 2017)

READ: https://www.apa.org/science/about/psa/2019/11/better-research-practices

03 –B: Why Open Science?

03– C: The Open Science Lifecycle

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03–E: Other Open Science Practices

In order to increase transparency and improve the journal review process, many high-profile journals have started requiring researchers to make their raw data and analysis code available. Still, many do not but researchers that support the open science framework will often make their data and code available as well. If not included with supplemental materials with the journal article, some scientists will make information available on a lab website or GitHub account.

Another open science method is to publish pre-prints of your work and publish with open-access publishers that do not charge individuals or Universities extra to view the manuscript. This helps build equity in science, and increases citations and access to your work.

For example, a researcher Richard Mann, had to retract a paper only after his friend told him he found an error in his code after requesting it to build on the research. Dr. Mann had run a simulation with an N of 1, not N=100. Typos can happen, and sometimes having another set of eyes look at your work can prevent mistakes from making it into the literature. Mann self-retracted the paper and later re-did the research. But this serves as a tale of caution! (https://www.statnews.com/2017/06/01/shrimp-study-error/)

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03–F: Questionable Research Practices and Barriers to Reproducibility

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03–G: How R Encourages and Contributes to Open Science

Since R is freely available and new developments can be easily added and modified by the greater scientific community, it is inherently open. Additionally, the statistical packages in R are transparent in what calculations they are performing, allowing the user more flexibility and understanding when conducting analyses in comparison to other statistical software programs. Finally, integration of R code into LaTex packages and the inclusion of RMarkdown allows for easy and clear documentation of thought process, code, and analytical output for reporting and sharing. The ability to keep track of all details of your work and analysis in R allows for greater reproducibility in research.