Practical guidance for data lifecycle, metadata, curation, and FAIR principles in research.
Comprehensive, FAIR-aligned DMP template with clear section prompts, examples, and a short checklist to help teams document dataset inventories, metadata standards, storage & security, sharing/licensing, retention, and roles.
A practical, fillable template to record data assets, owners, formats, metadata requirements, storage and backup, access controls, versioning, legal/ethical constraints, retention, and a scored FAIR maturity checklist to guide project-level data management and reuse.
An actionable, interactive DMP template that guides teams through data description, metadata, storage, access, provenance, archiving and an embedded FAIR alignment checklist. Saveable responses support reproducibility, clearer responsibilities, and easier handoff to repositories or compliance reviewers.
A practical, fillable DMP template that guides project teams through data types, storage architecture, backup schedules, metadata standards and identifiers, access and sharing policies, retention and archiving, roles and responsibilities, cost estimates, and compliance. Each section includes concrete prompts, example answers, and recommended FAIR-focused practices to support reuse and reproducibility.
Timeboxed, actionable huddle template to surface pipeline health, data quality issues, analytic requests, decisions, and clear action items. Collects meeting metadata, issue details, owners, SLAs, and follow-up so problems are resolved quickly and analysis stays trustworthy.
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.
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.
An interactive experiment registry form and minimal schema to capture consistent experiment metadata, ownership, protocols, datasets, analysis scripts, outcomes, and reproducibility evidence.
An interactive, structured audit that helps research teams assess data completeness, lineage, schema validation, access and retention controls, stewardship, and remediation priorities — with saved responses to track findings and follow-up.