Overview

Design the best practices for workflows in scientific computing will make your research more efficient and reliable. We aim to design the workflow that is:

1. Reproducible: Future you and other researchers can reproduce your results
2. Automated: Reduces time spent on repetitive tasks
3. Maintainable: Code remains functional and understandable over time

This chapter covers essential tools and practices for reproducible research:

Lectures

• Scientific Computing Workflow: Suggested workflows and project management strategies for scientific computing projects
• Git and GitHub: Version control for tracking changes, collaboration, and best practices in scientific computing
• Make: Automation and documentation of workflows, including dependency management and reproducibility
• Virtual Environment: Python environment and dependency management for isolation and reproducibility
• Reproducible R: R package and environment management using here and renv for consistent file paths and reproducible environments
• Docker: Complete environment containerization for cross-platform compatibility and system-level dependencies