The generation and analysis of environmental data are often complex, multi-step processes that may involve collaboration among many people. Increasingly, tools that document and help to organize workflows are being used to ensure reproducibility, shareability, and transparency of the results. This course will introduce students to the conceptual organization of workflows (including code, documents, and data) as a way to conduct reproducible analyses. These concepts will be combined with the practice of various software tools and collaborative coding techniques to develop and manage multi-step analytical workflows as a team.

EDS 214 - Analytical Workflows and Scientific Reproducibility aims to expose incoming MEDS students to “good enough” practices of scientific programming and develop skills in environmental data science to produce reproducible research. By the end of the course, students should be able to:

• Develop knowledge in scientific analytical workflows. To learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will rely on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as R and Python, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows

• Learn how to code in a collaborative manner by practicing techniques such as code review and pair programming. Become comfortable asking for and conducting code reviews using git and GitHub to create pull requests, ask for feedback from peers, and merge changes into the main repository. Practice pair programming to cement the collaborative development of reproducible analytical workflows

• Practice documenting code and data in a systematic way that will enable your collaborators, including your future self, to understand and reuse your work

For more information: https://eds-214.github.io/EDS-214-analytical-workflows/