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An Open Science and Reproducible Research Primer for Landscape Ecologists
Citation:
Hollister, Jeff, B. Kreakie, Bryan Milstead, AND S. Chamberlain. An Open Science and Reproducible Research Primer for Landscape Ecologists. International Association for Landscape Ecology World Congress 2015, Portland, OR, July 05 - 10, 2015.
Impact/Purpose:
One of the goals of SSWR 2.3C is to apply the tenets of Open Science to our work. As we do this, we gain knowledge on best practices and useful approaches for increasing openness and reproducibility that, if shared, have an impact far beyond SSWR 2.3C. This workshop will establish our work at the forefront of Open Science at the USEPA, will solidify collaborations within the Open Science community, and initiate new collaborations, via teaching of Open Science tools, that should benefit our future research efforts.
Description:
In recent years many funding agencies, some publishers, and even the United States government have enacted policies that encourage open science and strive for reproducibility; however, the knowledge and skills to implement open science and enable reproducible research are not yet widely taught. The objectives of this workshop are to provide an introduction to the basic tenets of open science and reproducibility, provide hands on-experience with R and RStudio, and provide examples relevant to landscape ecologists. We will combine an introduction to relevant topics with hands-on exercises. The topics to be covered are: Basic tenets of open science and reproducible research; basics of R and R scripting; using spatial data in R; R Markdown; version control with Git and RStudio; and discovering and using data on the web. The topics will be presented by Jeffrey Hollister (USEPA), Bryan Milstead (USEPA), Betty Kreakie (USEPA), and Scott Chamberlain (rOpenSci).The intended audience for this workshop is landscape ecologists interested in expanding the accessibility to their research products. R basics will be covered, so novices are welcome. Additionally, topics unfamiliar to many R users will be covered, so seasoned R users will also benefit.