Reproducible Science and Data Workflows for Beginners

How often do you try to rerun an old analysis only to find that the code no longer works or the data has vanished? Ensuring your scientific workflows are reproducible is the key to credible, collaborative, and impactful research. This text-only course guides you through the process of structuring your scientific projects so that anyone can recreate your results with confidence. You will progress from understanding foundational reproducibility concepts to establishing robust, self-contained data environments. What you'll learn: Understand the core principles of reproducibility, transparency, and open science; Organize research project directories and datasets using standardized structures; Track changes and collaborate seamlessly using modern Git version control workflows; Manage software dependencies and virtual environments to prevent code breakage; Document data pipelines, code, and computational environments clearly for other researchers; Apply automation basics to run analyses consistently from raw data to final output. The course begins with essential terminology and the philosophy of open science, then moves step-by-step through setting up a project repository, managing dependencies, and documenting your entire workflow. Designed entirely for beginners, this course requires no prior background in programming or research administration, starting with the absolute basics. Start building reliable, shareable, and verifiable scientific workflows today.