Practical design patterns, checklists, and templates to combine AI recommendations with human review while preserving traceability and reproducibility in research workflows.
An actionable, recordable checklist to evaluate AI model readiness for research use. Prompts reviewers to capture intended use, failure tolerance, data provenance, bias and robustness checks, validation results, interpretability and uncertainty practices, documentation and reproducibility steps, monitoring and retraining plans, approvals, and residual risk.
Practical patterns for combining AI recommendations with human expertise, plus concrete implementation and audit-log guidance to preserve oversight, traceability, and reproducibility.
A practical, safety-focused library of reusable prompt patterns for literature review, experimental planning, code assistance, and data exploration — with expected outputs, validation checks, traceability practices, and a short governance checklist for when to require human review.
A practical, step-by-step framework to decide where AI adds value in research, validate model outputs, document model use in reproducible workflows, and manage risk throughout an AI-enabled research lifecycle.