The course is aimed at graduate students, postdocs, and other researchers who perform computational analysis or work. The material on automation uses basic R for teaching and illustrating the key concepts. Advanced knowledge of R is not needed, but some familiarity with R will aid in absorbing the material.
This document provides basic information about Reproducible Science workshops for instructors:
As of September 2015, a ReprSci in R workshop has six modules:
Goals: Students will be able to recognize problems that reproducible research helps address. They will know how to identify pain points in getting analyses to be reproducible. The will understand role of documentation, sharing, automation, and organization in making research more reproducible. Students will be introduced to selected tools to solve these problems, specifically R /RStudio / RMarkdown.
Instructor’s skills: Good understanding of the concept of reproducibility. Ability to list common problems emerging if research cannot be reproduced. Knowledge of, at least selected, tools, processes and approaches to making research reproducible.
Sample slides: deck 1 from Duke workshop and deck 2 from Duke workshop
Goals: Students will learn the benefits of project and folder organization, and how these enable reproducibility and reusability.
Instructor’s skills: Good understanding of file organisation in research projects. Understanding of file structure on major operating systems (Windows, Linux/Unix, Mac OS) and the interface/commands for managing files and folders. Understanding of basic file types (binary vs. text). At least a basic overview of how files are stored (and deleted) in different operating systems. Understanding of file and folder naming conventions (names, extensions etc.).
Sample slides: file organization and file naming
Goals: Students will work through activities highlighting the motivation for and value of literate programming as a concept, and as its implementation in Rmarkdown. Through this, students will get introduced to the concepts of executable documentation and automation. Students will also learn about best practices for structuring spreadsheet-type data files, and the importance of documenting all changes one makes to data. Finally, students will be introduced to combining all these ideas to create automated, executable, and self-documenting data quality ensurance and control reports.
Instructor’s skills: Good understanding of literate programming as a concept. Good knowledge of Markdown syntax, R programming basics, Rmarkdown syntax and how to control the behavior of
knitr on Rmarkdown code chunks. Good understanding of best practices for data organization and manipulation for tabular observational data.
Sample slides: literate programming
The initial version of the workshop didn’t include version control, but there were many requests for this module during the first workshop at Duke, so we added it for the iDigBio workshop.
Goals: Students will understand the concept of version control and gain skills to apply this tool (using
Git). In particular, they will know how to set up and use local repository (initiate it, add and commit files, browse history). Students will learn how to setup remote repository on GitHub and use it (push, pull, clone). They will be able to collaborate using Git (create a pull request to someone else’s remote repository), they will also be introduced to concepts such as branching, resolving conflicts and so on.
Instructor’s skills: Very good understanding of the concept of version control. Solid practical knowledge of Git and GitHub. Recommended: knowledge of other version control (SVN, Mercurial); ability to highlight the advantages of version control over tools such as Dropbox or tracking changes in binary documents (MS Word).
Sample slides: slides!
Goals: Students will learn how to restructure their scripts (typically written in
R) so that the code is modularised. They will learn to define and use functions. Students will be able to write code to automate code build and write tests for their code. They will be briefly introduced to the continuous integration tools.
Instructor’s skills: Good understanding of programming concepts, in particular code modularisation, writing and using functions, code reusability and so on. Good understanding of selected software engineering concepts such as project build and automation, code testing, continuous integration and so on. Solid knowledge of R, RStudio and relevant packages (consult the materials for details). Understanding of basic statistical concepts (consult the materials for details).
Sample slides: none (all hands-on in R)
Goals: Students will learn why and how to publish research materials. They will be know what the advantages and arguments for open access publishing and public domain. They will understand which research outputs should and should not (e.g. confidential data) be published. They will be able to select relevant repositories for their research outputs. Students will gain knowledge about choosing licenses for software and data produced during their research.
Instructor’s skills: Understanding of requirements for reproducible publication. Understanding of differences between publication, sharing and archiving. Understanding the difference between open and restricted access publication. Overview of tools and repositories for publishing research outputs. Knowledge of different licensing models and ability to discuss major differences between the most commonly used licenses in research.
Sample slides: slides!
These materials are being revised as we get more feedback from participants and instructors. The list of GitHub issues for the Reproducible-Science-Curriculum gives a pretty good idea of what is happening and what needs to be done.