version-control-research

To make research reproducible with Git and DVC, you structure stable paper-linked code in `main` branches and track large datasets or metrics changes using `dvc.yaml` and `params.yaml` configurations. This ensures deterministic code and data lineage across collaborators.

The best way to track ML experiments with DVC pipelines is defining multi-stage workflows in `dvc.yaml` and capturing parameter changes in `params.yaml`. Using `dvc exp` workflows allows you to isolate feature branches and version data lineage alongside Git history.

Yes, you can run end-to-end `dvc repro` checks in GitHub Actions CI to verify pipeline reproducibility. This automated CI verification enforces quality with pre-commit hooks while keeping configurations secret-safe using `.env` files managed by `python-dotenv`.

Yes, pre-commit and python-dotenv are required to standardize research version control. Pre-commit hooks enforce code quality and linting, while python-dotenv manages `.env` files to keep secrets out of Git, ensuring deterministic and secret-safe configurations across collaborators.

Paper-aligned version tagging applies semantic version tags at submission and revision milestones. This maps published results to exact code and data states, ensuring that analysis experiments remain perfectly reproducible and verifiable during peer review.

Using Git for data versioning without DVC limits your ability to track large datasets and metrics efficiently. Git handles code and configuration well, but DVC is required to manage multi-stage experiment pipelines, data lineage, and parameter changes without bloating repository history.