Practical guidance, design patterns, and templates to pilot AI assistants, agents, and automation workflows that save time while preserving reproducibility in research.
A practical, research-focused design checklist that explains scope, guardrails, provenance, validation, escalation, and security for AI agents. Includes concrete acceptance criteria, validation tests, common mistakes, and a concise design template you can copy into a project or an interactive checklist.
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.
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.
An expanded, practical end-to-end ML workflow for research teams: data contracts and versioning, feature engineering and feature-store practices, training & validation with baselines, interpretability and subgroup checks, model registry, deployment constraints, monitoring, and governance artifacts for reproducibility.
An expanded, practical brief that maps promising AI/automation capabilities for research, lists concrete candidate pilot experiments with objectives and quick success signals, identifies common blockers with mitigations, and provides a pilot-readiness checklist and prioritization guidance to help teams select feasible first bets.
A practical, stage-based playbook to assess, run, validate, and scale AI pilots in research settings. Includes milestone criteria, a complete pilot planning template (metrics, sample-size guidance), a risk & validation checklist, a roles & responsibilities matrix, and example retrospective and scale checklists. Recommended interactive forms and data-capture points are noted for easier reuse and reproducibility.
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.
A practical, fillable model card template with a detailed validation checklist covering dataset provenance, training and evaluation procedures, subgroup performance, robustness checks, intended use, limitations, reproducibility requirements, versioning, and monitoring.