Glossary

Data provenance

Data provenance means the record of where data came from, how it was collected, what rights apply, and how it changed before delivery. The term matters because it turns a model or procurement concept into concrete data requirements you can evaluate samples against.

Updated 2026-05-04

By truelabel

Reviewed by truelabel · May 4, 2026

data provenance definition

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Quick facts

W3C PROV-O: Provenance ontology — entities, activities, agents, and the relationships between them (W3C Recommendation, 2013).
Datasheets for Datasets: Gebru et al., arXiv:1803.09010 (2018) — 7-section structured documentation: motivation, composition, collection, preprocessing, distribution, maintenance, uses.
C2PA (Content Credentials): Coalition for Content Provenance and Authenticity — cryptographic content credentials and assertions adopted by Adobe, Microsoft, BBC, and others.
HuggingFace dataset cards: De facto standard for dataset documentation in the AI ecosystem — composition, intended use, limitations, license, citation.
What provenance must record: Source, contributor consent, rights, capture environment, transform history, time of capture, time of delivery, and which downstream models were trained on the data.

Comparison

Question	Answer
Where it appears	Sourcing specs, QA requirements, dataset manifests, and buyer review notes
Why it matters	It turns abstract AI language into a supplier-verifiable requirement
Common failure	Using the term without defining modality, format, rights, or acceptance criteria

How to use this term in a spec

Data provenance records where data came from, which activities transformed it, and which agents or systems were involved. W3C PROV-O provides the formal vocabulary for entities, activities, agents, and provenance relationships. ^[1]

What to avoid

Do not use data provenance as a vague keyword. Define the data files, metadata, rights, QA checks, and delivery format that make it measurable.

Data provenance in buyer review

For physical AI procurement, provenance also needs dataset documentation, content credentials, and delivery manifests that travel with the files. Datasheets for Datasets, C2PA, and Hugging Face dataset cards each cover a piece of this documentation surface. ^[2] ^[3] ^[4]

Data provenance supplier evidence

Supplier evidence should identify source capture, consent state, rights, transformations, and file lineage. Without provenance, the buyer cannot audit whether a dataset is usable for commercial training or only for exploration.

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

Physical AI glossaryGlossary hub Egocentric dataDefinition and terminology Off-the-shelf datasetDefinition and terminology Physical AI training dataDefinition and terminology Teleoperation dataDefinition and terminology Consent artifactDefinition and terminology Robot demonstrationsDefinition and terminology Sim-to-real gapDefinition and terminology

External references and source context

PROV-O: The PROV Ontology
W3C PROV-O defines provenance using entities, activities, agents, and relationships among them.
W3C ↩
Datasheets for Datasets
Datasheets for Datasets proposes structured documentation for dataset motivation, composition, collection, preprocessing, distribution, and maintenance.
arXiv ↩
C2PA Technical Specification
C2PA specifies content credentials and assertions for tracking media provenance and integrity.
C2PA ↩
Dataset cards are not yet standardized for physical AI procurement
Hugging Face dataset cards document intended use, data composition, and other dataset metadata for model users.
Hugging Face ↩

More glossary terms

Egocentric dataFirst-person camera footage capturing how a worker or operator sees a task.Off-the-shelf datasetAn existing public or commercial dataset bought without custom collection.Physical AI training dataData that teaches models to perceive, reason about, and act in physical environments.Teleoperation dataHuman-controlled robot trajectories used to bootstrap policies for new skills.Consent artifactSigned documentation that contributors agreed to commercial use of their data.Robot demonstrationsRecorded successful task executions used as demonstrations for imitation learning.

FAQ

What is Data provenance?

Data provenance is the record of where data came from, how it was collected, what rights apply, and how it changed before delivery.

Why does it matter for physical AI?

It matters because physical AI data must be connected to actions, environments, metadata, rights, and model use, not just raw files.

How should buyers spec it in a sourcing request?

Require source, consent, rights, capture context, transform history, and delivery manifest fields.

Can suppliers validate this from samples?

Yes, if the buyer defines visible evidence, metadata requirements, and acceptance criteria before suppliers submit files.

Find datasets covering data provenance definition

Truelabel surfaces vetted datasets and capture partners working with data provenance definition. Send the modality, scale, and rights you need and we route you to the closest match.

Request data provenance data