Goal
- Guide synthesis teams through setting up a reproducible repository that consumes curated HRL datasets.
Before you start
- Confirm hypothesis scope, decision context, and approvals for using sensitive data.
- Review the Style & Development Guide, reproducibility commitments, and applicable science plans.
Workflow outline
- Create a GitHub repository from the HRL analysis template with Quarto scaffolding and CI.
- Acquire datasets through the HRL catalog/SDKs; log input DOIs, versions, and access constraints.
- Develop models/analyses in R or Python with parameterized scripts, diagnostics, and metadata-rich outputs.
- Generate synthesis products (datasets, indicators, figures) and Quarto reports for stakeholders.
- Package outputs for reintegration (metadata, release tags) and notify the Central Data Team and reporting leads.
Deliverables
- Reproducible codebase, published synthesis dataset/model, documentation for reporting and catalog updates.
- Recommendations or decision-support materials for adaptive management discussions.
Support and review
- Peer review expectations, Central Data Team consults, and access to compute resources or specialized tooling.
- Link to Getting Help guidance for troubleshooting analytical workflows.