AI tools & agents for research

Practical guidance, design patterns, and templates to pilot AI assistants, agents, and automation workflows that save time while preserving reproducibility in research.

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Machine learning for research — workflow template

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.

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ML Model Card & Validation Checklist

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.

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