ArtData™ Standard
OOF™ Origin Open Foundation™
Independent Methodological Authority
OriginID: OOF-OID-AI-ARTD-2026-02-22-0012
Category: AI & Data Integrity Standards (AI)
Subcategory: Data Integrity Classification
Version: 1.0
Status: Canonical · Binding
Effective Date: 22 February 2026
Compatibility:
MTVF™ · Methodology OS™ · AI Governance Frameworks
Authority: OOF™ Origin Open Foundation™
Protection: MIP™ — Methodological Intellectual Property
Canonical Language: English (UCL™)
A. Standard Abstract
The ArtData™ Standard defines a minimum structural integrityclassification for datasets used in artificial intelligence training, testing, and
validation environments.
The standard establishes baseline conditions for dataset traceability,
structural identity, modification transparency, and declared responsibility.
ArtData™ does not define dataset quality or ethical evaluation.
It defines the minimum structural accountability required for reliable AI
data pipelines.
The standard is designed to be technology-neutral and immediately
applicable across research, commercial, and governance environments.
B. Canonical Definition
ArtData™ is a structural integrity classification for digital datasets thatmeet defined minimum requirements of traceability, identity anchoring,
modification transparency, and responsible entity declaration.
ArtData™ defines the minimum structural accountability conditions
required for datasets used in AI systems.
It is independent of dataset type, format, domain, or size.
C. Purpose
The purpose of the ArtData™ Standard is to:
- establish traceable dataset origin structures
- enable transparent dataset lifecycle tracking
- reduce operational and regulatory risk in AI data pipelines
- support reproducible training and evaluation environments
- introduce structural accountability in AI dataset management
quality guarantees.
D. Structural Scope
The ArtData™ Standard applies to datasets used in:
- AI training environments
- machine learning testing systems
- AI validation datasets
- simulation and model evaluation datasets
- AI research datasets
- automated decision-system training pipelines
institutional environments.
E. Minimum Structural
Requirements
A dataset may be designated ArtData™ Compliant only when the following conditions are satisfied.
1. Origin Disclosure
The dataset must have a documented origin source.Minimum requirement:
- identifiable source category
or - documented acquisition method.
2. Identity Anchor
The dataset must possess a unique structural identifier.Minimum requirement:
- dataset identifier
- dataset version reference
- cryptographic hash or equivalent integrity reference.
3. Time Continuity
Dataset creation and modification history must be recorded.Minimum requirement:
- initial creation timestamp
- modification timestamp records.
4. Modification Transparency
All transformation, filtering, labeling, or preprocessing operations must bedocumented.
Minimum requirement:
- transformation description
- responsible entity or system reference.
5. Responsible Entity Declaration
A legally identifiable entity must declare dataset responsibility.Minimum requirement:
- organization or responsible entity name
- contact reference
- declaration of responsibility.
G. What ArtData™ Is Not
ArtData™ does not guarantee:
- absence of dataset bias
- ethical neutrality
- legal compliance across jurisdictions
- dataset quality superiority.
dataset perfection.
H. Governance Position
ArtData™ functions as a foundational data integrity classification layerwithin AI data pipelines.
It may operate independently or in compatibility with broader validation
frameworks including MTVF™.
The standard establishes a structural baseline for trustworthy AI dataset
management.