01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Lightweight packaging and validation of assay exports, reference tables, tabular analysis results, and other file-based data products.
- Limits
- It does not supply biological semantics, full provenance, privacy policy, repository trust, or ML-specific intended-use and bias documentation.
- Best for
- Discovery and Laboratory and Cross-cutting teams working across Harmonize → Exchange → Learn + reuse.
- Maturity
- ScalingUsable now, but adoption or tooling is still developing. Pilot the exact stack first.
02
See it in the workflow
This view shows the input, the change the standard introduces, and the resulting output.
- InputWhat starts
Discovery and Laboratory and Cross-cutting source data, metadata, and local mappings
- Data PackageWhat changes
Use Data Package as a pinned data model / schema across Harmonize → Exchange → Learn + reuse
- OutputWhat becomes possible
A handoff the next system or team can validate against the same release
03
A concrete example
A release includes a v2 datapackage.json with stable identity, version, license, sources, contributors, resources, and Table Schemas for tabular files.
Why it matters: Machine-readable resources, field types, missing-value conventions, and categories improve loading and validation, but labels, splits, cohort meaning, and fitness remain external.
04
What it fits with
Can carry DataCite-aligned contributor roles and identifiers, sit inside an RO-Crate, and provide structural metadata beneath a Croissant ML description.
- StandardInChI
Both support Discovery and Laboratory work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata profileREMBI
Both support Laboratory and Discovery work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata profileExpmeta
Both support Discovery and Laboratory work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Data model / schemaOME Model / OME-TIFF
Both support Laboratory and Discovery work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship
05
Implementation starter
Start with one bounded handoff. Pin, test, and review it before scaling.
Define one handoff, its accountable owner, and the decision Data Package must support.
Pin the exact version and companion artifacts: 2.0 · current.
Map one representative input to the required data model / schema artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.
06
Test the main limitation
It does not supply biological semantics, full provenance, privacy policy, repository trust, or ML-specific intended-use and bias documentation.
Run one representative end-to-end pilot and record exactly where Data Package loses context, needs an extension, or depends on another standard.
Machine-readable output may still be unfit for analysis or ML.
Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Machine-readable resources, field types, missing-value conventions, and categories improve loading and validation, but labels, splits, cohort meaning, and fitness remain external.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
Data Package Standard v2
Official publisher or steward guidance for this data model / schema profile.
- Publisher
- Data Package Working Group