Purpose
- Describe how synthesis teams transform curated datasets into indicators, models, and reports that address HRL hypotheses.
- Emphasize reproducibility, documentation, and reintegration of derived products.
Workflow expectations
- Repository conventions per the Style & Development Guide (Quarto scaffolding, dependency management, folder structure).
- Use of scripted R/Python workflows, parameterized notebooks, and automation for reruns.
- Integration of curated datasets via the HRL catalog, APIs, or SDKs.
Documentation requirements
- READMEs with scope/methods, metadata for derived datasets, and references to input DOIs/versions.
- Tracking of assumptions, modeling decisions, and analytical diagnostics.
Quality assurance
- Continuous integration for linting, tests, and reproducibility checks; peer/code review expectations.
- Storage of validation artifacts (model fit summaries, residual analyses, etc.).
Outputs and reintegration
- Derived datasets, models, indicators, and decision-support tools returned to the Central Data Team.
- Semantic versioning and DOI assignment for synthesis products plus release communications to reporting teams.