Data management & FAIR practices

Practical guidance for data lifecycle, metadata, curation, and FAIR principles in research.

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Reference

Data pipelines & reproducible compute pattern library

A practical pattern library: reproducible ETL and compute recipes, orchestration recommendations, provenance capture examples, and short worked examples teams can copy to make data pipelines repeatable, debuggable, and re-runnable.

Reference

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Guide

Reproducible Compute Starter Kit — containers, workflows, and provenance

A practical starter playbook for making compute runs reproducible and cost-aware. Includes container recipes, workflow patterns (Nextflow/CWL), reproducibility checkpoints, provenance capture best practices, a release checklist you can use immediately, and next steps for automation and team adoption.

Guide

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