Data Readiness Knowledge Base

01

Where it fits—and where it doesn’t

Use these four checks before committing implementation time.

Use it when: Cross-system lineage, transformation history, audit evidence, and knowledge-graph provenance.
Do not use it as: Do not treat PROV-O as a complete solution on its own. The model is intentionally generic; useful provenance requires a scoped profile, identifier policy, and capture instrumentation.
Best for: Teams working with Cross-cutting data across Acquire → Harmonize → Exchange → Learn + reuse.
Maturity: EstablishedEstablished enough for serious use; still pin the exact release and any implementation profile.

02

See it in the workflow

A standard creates value by changing a handoff, not by existing in a catalog.

InputWhat starts
Cross-cutting data, metadata, and the local decisions around them
PROV-OWhat changes
PROV-O applies a shared ontology / data model across Acquire → Harmonize → Exchange → Learn + reuse
OutputWhat becomes possible
A more consistent, reviewable handoff for the next system or team

Readiness gateBefore scaling: The model is intentionally generic; useful provenance requires a scoped profile, identifier policy, and capture instrumentation.

03

A concrete example

A derived assay table records the source files, transformation activity, software agent, parameters, and responsible organization.

Why it matters: Supports dataset and feature lineage, reproducibility, and audit, but does not define ML-specific quality or responsible-use metadata by itself.

04

What it fits with

Can enrich DCAT, Croissant, RO-Crate, and domain graphs; lighter profiles often select a practical subset of PROV terms.

05

Implementation starter

Start with one bounded handoff. Pin, test, and review it before scaling.

Name an accountable owner and the decision PROV-O must support.
Pin the exact version and companion artifacts: W3C Recommendation · 2013-04-30.
Map one representative input to the required ontology / data model artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.

06

Limitation to test first—and the tests that catch it

Risk

The model is intentionally generic; useful provenance requires a scoped profile, identifier policy, and capture instrumentation.

Test

Run one representative end-to-end pilot and record exactly where PROV-O loses context, needs an extension, or depends on another standard.

Risk

A structured or machine-readable result can still be unfit for analysis or AI.

Test

Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Supports dataset and feature lineage, reproducibility, and audit, but does not define ML-specific quality or responsible-use metadata by itself.

07

Why we believe this

Checked against the canonical source plus independent operational evidence from an adopter, regulator, or implementation report.

Evidence notation: E1 + E2. The code is shorthand; the plain-language statement above is the claim.

Formal status: W3C Recommendation
Confidence: High
Review state: Source-checked
Reviewed by: Provenance architecture reviewer
Last verified: 13 July 2026
Review again when: Erratum, successor, or adopted profile update

How the evidence method works

08

Source shelf

Official diagrams, examples, specifications, and explainers. Nothing external loads until you choose to open it.

Primary sourceW3C Recommendation · 2013-04-30
W3C PROV-O Recommendation
The canonical publisher or steward source used to verify this ontology / data model profile.
Publisher
W3C
Rights
Rights remain with the publisher; this knowledge base links to the source rather than copying it.
Access
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Verified
2026-07-13
Open at source