01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Reproducible and reviewable supply-chain records for an AI release that combines scientific data, preprocessing code, dependencies, models, and licenses.
- Limits
- It is a broad BOM model rather than a scientific metadata profile; generated inventories require verification, and license metadata does not authorize use of sensitive human data.
- Best for
- AI / ML and Cross-cutting teams working across 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
AI / ML and Cross-cutting source data, metadata, and local mappings
- SPDXWhat changes
Use SPDX as a pinned standard across 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 publishes an SPDX 3.0.1 document linking the dataset, model, source code, build inputs, dependencies, integrity evidence, licenses, and their relationships, then validates the JSON-LD representation.
Why it matters: Makes the data-model-software supply chain inspectable by tools and agents, while scientific validity, consent, bias, and model performance need separate evidence.
04
What it fits with
Complements RO-Crate research context, Croissant dataset loading metadata, and PROV-O or OpenLineage process history.
- Data model / schemaCroissant
Both support AI / ML and Cross-cutting work and meet around Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Quality vocabularyDQV
Both support AI / ML and Cross-cutting work and meet around Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Validation standardSHACL
Both support Cross-cutting and AI / ML work and meet around Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata vocabularyDPV
Both support AI / ML and Cross-cutting work and meet around 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 SPDX must support.
Pin the exact version and companion artifacts: 3.0.1.
Map one representative input to the required standard 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 is a broad BOM model rather than a scientific metadata profile; generated inventories require verification, and license metadata does not authorize use of sensitive human data.
Run one representative end-to-end pilot and record exactly where SPDX 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. Makes the data-model-software supply chain inspectable by tools and agents, while scientific validity, consent, bias, and model performance need separate evidence.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
SPDX Specification 3.0.1
Official publisher or steward guidance for this standard profile.
- Publisher
- SPDX Project · Linux Foundation