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README.md

Reproducible Research in R for Ocean Biosciences: Open-science Training Seminar (RRROBOTS)

Welcome! (Yes, we have an awesome acronym!)

Usage

This repo is intended to be a self-contained set of guides and materials for the course. If you would like to use any content, including modifying it for your own purposes, please fork this on GitHub. Detailed information about running the course can be found here.

Course Objectives

There are 2 main goals of this course:

  1. Learn and implement practices in research computing including: data management, version control, code documentation, and replicability. (see Wilson et al. "Good enough practices in scientific computing")
  2. Reproduce the figures and results from published work. In other words, test-drive the skills in objective 1. while also going in-depth into the methodology of research that piques your interest.

By the end of this course, students will have first-hand experience of the entire data analysis workflow, including: processing raw datasets, generating figures, and interpreting results in a scientific context suitable for publication.

The long-term goals of this project are outlined in the Roadmap

Code of Conduct

All participants will be expected to follow the SIO Open Data Science Code of Conduct: https://open-data-science-at-sio.github.io/mission.html

Note that this applies both to the physical space for classes, as well as online interactions.

Target Audience

This course was originally aimed at students in the Ocean Biosciences program at Scripps Institution of Oceanography at ~3rd year and above, but should be generally suitable for PhD students getting ready to analyze data for their dissertations. Space is limited to 10 students.

Pre-requisites

Students will be expected to have taken at least 1 programming course and 1 statistics course. Students who are interested in the course, but unsure about their background should contact the instructor.

Participating students should install Git (https://git-scm.com/) and create a Github account (https://github.com/).

Students are also encouraged to install and use R (https://cran.r-project.org/), RStudio (https://www.rstudio.com/products/rstudio/download/) to ensure a standard user interface. While students may use Python and Jupyter notebooks, I am not able to assist with set-up and troubleshooting of these tools.

Logistics

Meetings will take place on Tuesdays, from 3pm to 5pm in Vaughan Hall 328. The first session will be April 4, 2017 with the last one on June 6, 2017.

In addition, lab/office hours will be offered every Friday (April 7 to June 2), from 3pm to 5pm in Vaughan Hall 328. The goal of these sessions is to provide time for catching up on work for the week and to resolve any coding issues.

Class Schedule

See here.

Contributors / Course Participants

See here.

List of Papers

See here.

Acknowledgments

This course is partly inspired by previous examples here:

And additionally is an Open Project sponsored by Mozilla Science

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Reproducible Research in R for Ocean Biosciences: Open-science Training Seminar

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