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Today's students are tomorrow's researchers, and so intentionally integrating reproducibility into the curriculum will be crucial in promoting reproducible practices in the long term. Teaching students to work reproducibly starts with providing them with the skills to document their work so that any other researcher can reproduce their results with the materials provided by the student.
Incorporating Reproducible Practices
One way to accomplish this is to design an assignment that minimally requires students to submit their final report, the data files they used in their analysis, and the code files (e.g. R or Python) that they used to generate the results contained in their report.
Contains examples of syllabi from a variety of social science disciplines that have assignments incorporating reproducibility concepts
LMU Open Science Center's Resources for Teaching
Curriculum materials, complete examples of online courses, and tools to implement in an Open Science workshop
Open Science Training Handbook
Includes a specific section on reproducibility, as well as general guidance on teaching and training best practices, event planning strategies, and links to examples.
Tufts University Consolidating Teaching Resources
A fantastic collection of resources for teaching reproducibility, with links to podcasts, journal articles, blog posts, and online courses and activities.
Reproducing Others' Work
Another popular method for teaching reproducible research is to have an assignment that requires students to reproduce or replicate some or all of the results of a published journal article.
A number of educators are using GitHub to share their course materials, which allows others to easily remix and adapt the resources in their own teaching.
Reproducible Science Curriculum
Materials from a series of hackathons focused on creating courses using Jupyter Notebooks as a facilitator of reproducible research.
From the University of Glasgow, materials for teaching reproducible research using R across all undergraduate and postgraduate levels. Includes links to repos on two courses: Data Skills for Reproducible Science and Teaching Reproducible Data Analysis in R.