Advanced Course on Reproducible Research in R

Advanced Course on Reproducible Research in R

Creating Collaborative and Automated Analysis Pipelines

Reproducibility and open scientific practices are increasingly in demand and needed by scientists and researchers in modern research environments. Our work often require or involve a high level of hands-on collaboration on scientific projects throughout all stages of the research lifecycle. We are faced with obstacles that we have no training for nor knowledge on how to address. Obstacles, that can be as simple as not having a shared way of writing code or managing files, can impede effective collaboration. Or they can be complex, like documenting software dependencies or steps in an analysis pipeline in a way that makes it easy to resolve issues and get the most recent set of results after collaborators have worked on a project. Aside from the impact on collaboration, these barriers can even affect projects with just one primary researcher. Ultimately, this can have consequences on the reliability and reproducibility of scientific results, especially considering that the measures taken to address these barriers are often not explicitly shared in classical science output (like a publication).

With this course, we aim to begin addressing this gap. By using a highly practical, hands-on approach that revolves around code-along sessions (instructor and learner coding together), reading activities, and hands-on exercises, our overarching learning outcome is that at the end of the course, participants will be able to: Describe what an open, collaborator-friendly, and nearly-automated reproducible data analysis pipeline and workflow looks like, and then create a project that follows these concepts by using R.

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