5.9. Bringing It All Together: Enhancing Reproducibility

To go from basic version control to full reproducibility, you need:

1. Documentation: Include thorough documentation into your repository using a README.md file or a dedicated docs/ directory. This ensures that users can easily understand your project.

2. Data Availability: Publish your data! Use LFS for effective versioning of large datasets.

3. Workflow Documentation: Leverage submodules and automation scripts to comprehensively document the full analysis workflow, providing clarity on how to execute your project.

4. Dependencies: Clearly specify direct dependencies in your project. This helps users install the necessary libraries or tools to run your analysis.

5. Transitive Dependencies: Define isolated execution environments to manage transitive dependencies effectively, e.g., to ensure that all required packages are available without conflicts.

6. Environment tracking: Use isolation tools like Docker or ✨NixOS✨ to track the execution environment, guaranteeing consistency across different systems.

7. Configuration Settings: Declare and load configuration settings to manage parameters used in your analysis, making it easier for others to reproduce your work.