Purpose
- Provide uniform coding, repository, and documentation practices for HRL data publication, ingestion, and analysis projects.
Repository scaffolding
- Required folder layout (data-raw, data, scripts, metadata), configuration files, and dependency management (renv, virtual environments).
- Mandatory files such as README, LICENSE, CONTRIBUTING, CODEOWNERS, and NEWS/changelog.
Coding standards
- Naming conventions, linting/formatting rules, and expectations for logging and error handling.
- R and Python guidance (use of
package::function(), base pipe, modularized scripts, parameterization).
Testing and CI/CD
- Required automated checks (unit tests, schema validation, reproducibility tests) for each repository type.
- Recommended GitHub Actions/workflow snippets and badges for demonstrating compliance.
Versioning and release management
- Semantic versioning rules, tagging strategy, changelog conventions, and linkage to dataset DOIs.
- Procedures for coordinating releases with EDI submissions or catalog updates.
Collaboration practices
- Branching workflows, pull-request reviews, issue templates, and documentation of governance decisions.
- Expectations for code review participation by Central Data Team or synthesis leads.