Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research.

Area of Science:

• Computational Biology
• Data Science Education

Background:

• Existing Software Carpentry curricula can be overwhelming for short workshops.
• Learners struggle to integrate independent programming lessons into their research.

Purpose of the Study:

• To develop a concise, two-day R programming workshop curriculum.
• To teach novice researchers reproducible research principles using the tidyverse, Unix shell, and Git.
• To create an accessible teaching resource for instructors.

Main Methods:

• Curriculum development inspired by The Carpentries.
• Integration of live coding, individual, and group practice exercises.
• Iterative refinement based on participant and instructor feedback.

Main Results:

• A two-day virtual workshop was successfully delivered to nearly 40 participants.
• The curriculum effectively teaches data management, analysis, plotting, and reporting with R.
• The developed material serves as a succinct, integrated resource for learners and instructors.

Conclusions:

• The new curriculum successfully addresses the need for a condensed and integrated approach to teaching R programming for reproducible research.
• This resource lowers the barrier for new instructors and enhances learning for novices.
• The curriculum is a valuable tool for workshops focused on practical data analysis skills.