The Semantic Data Charter (SDC) is an open specification for creating self-describing, semantically rich data models that work seamlessly across systems, languages, and autonomous agents.

This organization and all repositories within it are controlled and maintained by Axius SDC, Inc.

Data infrastructure engineered for fully automated machine processing.

We saw where technology was heading 25 years ago: machines would become the primary consumers and producers of structured data. When that happened, every system built for human interpretation would break. SDC was built for that world.

• Open - Built on international standards, not proprietary lock-in
• Accessible - Language-agnostic, enabling global collaboration
• Governed - Built-in lineage and provenance for accountability
• Permanent - CUID-based immutability for long-term data integrity

1. Enforce Governance - Built-in lineage and provenance tracking
2. Embed Meaning - Semantic interoperability via RDF/OWL
3. Mandate Quality - Validation rules and quality constraints

Think of it as: "Protocol Buffers with semantics and governance"

SDC combines:

• Structure (like Protocol Buffers) - XML Schema 1.1 for data types and constraints
• Semantics (like RDF/OWL) - Meaning and relationships
• Validation (like SHACL) - Quality rules and business logic
• Governance - Lineage, provenance, and audit trails
• Packaging - Self-contained, queryable data packages

Result: Data that is self-describing, semantically rich, and audit-ready - readable by machines without human interpretation.

• Language Agnostic - Model in Portuguese, French, Japanese, Spanish, etc.
• Standards-Based - Built on 24 international standards from W3C, OASIS, OMG, ISO, IETF, and IANA
• AI Governance - Runtime enforcement via OASIS XACML decisions with tamper-evident receipts
• Namespace Versioning - CUIDs for immutable, evolvable components
• Multi-Format Export - RDF, OWL, SHACL, FHIR, GraphQL, JSON, GQL

• Enterprise Data Integration - Canonical models for multi-system environments
• AI Governance - Data packages with automatic lineage for trustworthy AI
• Cross-Border Exchange - Semantic interoperability without translation
• Regulatory Compliance - Built-in audit trails and provenance
• Legacy Modernization - Bridge between old and new systems
• Agentic Infrastructure - Deterministic data substrate for autonomous agents

• SDCRM v4.0.0 - Reference model, schemas, and specification
• sdc-xml2graph v4.0.0 - Transform SDC4 XML to knowledge graphs (Q1-2026)

• Form2SDCTemplate v4.4.0 - Convert PDF, DOCX, and image forms into SDC templates using Gemini AI. Available as a Python package, Google Colab notebook, or LLM instruction file.
• SDCObsidianTemplate v4.3.0 - Interactive Obsidian Templater plugin for building SDC markdown templates with guided prompts, domain-aware defaults, and SDC participation model support.

• sdcvalidator (Python) v4.1.0 - SDC4 structural validator with two-tier error classification (PyPI)
• sdcgovernance (Python) v4.0.0 - Governance advisory engine implementing 24 standards from W3C, OASIS, OMG, ISO, IETF, IANA. Returns OASIS XACML decisions (PERMIT/DENY/INDETERMINATE/NOT_APPLICABLE) with SHA-256 hash-chained tamper-evident receipts. MCP server included. (PyPI)

• SDCStudio v4.0.0 - Web application for AI-powered SDC model generation, schema export, and semantic enrichment

All SDC projects use 4.x.x versioning where the MAJOR version (4) represents the current SDC generation.

SDC was not built in response to the AI era. It was built in anticipation of it.

Evolution Timeline:

• 2000-2009: FreePM/TORCH (open source healthcare applications)
• 2009-2017: MLHIM (430 commits, 165+ citations, healthcare focus)
• 2012-2025: S3Model (1,586 commits, domain-agnostic generalization)
• 2025-present: SDC4 (commercial production platform)

Academic Validation:

• 12+ peer-reviewed papers (AMIA, JCI, JAMA, IEEE/ACM)
• 165+ citations on Google Scholar
• Proven in healthcare, research, and enterprise contexts

Verification: Historical Archive - 4.2GB, 76,313 files, full Git history (2013-2025)

SDC is built on international standards:

W3C Standards:

• XML Schema 1.1 (structure)
• RDF 1.1 (semantics)
• OWL 2 (ontologies)
• SPARQL 1.1 (queries)
• SHACL (validation)

ISO Standards:

• ISO 11179 (metadata registries)
• ISO 20022 (financial messaging)
• ISO/IEC 21838 (top-level ontologies)
• ISO 21090 (healthcare data types)

IETF Standards:

• RFC 3986 (URIs)
• RFC 8259 (JSON)
• And more...

OASIS Standards:

• XACML 3.0 (governance decisions)

OMG Standards:

• DMN (decision tables)

Total: 24 international standards from 6 standards bodies

The fastest path from existing form to SDC model:

1. Open the Form2SDCTemplate Colab notebook
2. Enter your free Google AI API key
3. Upload a PDF, DOCX, or image form
4. Download the generated SDC markdown template
5. Upload to SDCStudio for processing

Or use the Python package:

pip install "form2sdc[gemini]"

from form2sdc.analyzer import GeminiAnalyzer
from form2sdc.core import FormToTemplatePipeline
from pathlib import Path

analyzer = GeminiAnalyzer(api_key="YOUR_KEY")
pipeline = FormToTemplatePipeline(analyzer)
result = pipeline.process(Path("your_form.pdf"))

print(result.template)          # SDC markdown
print(result.validation.valid)  # True if valid

pip install sdcvalidator

from sdcvalidator import SDC4Validator

validator = SDC4Validator('schema.xsd')
result = validator.validate('data.xml')

if result.is_valid:
    print("Valid!")
else:
    print(f"Structural errors: {len(result.structural_errors)}")
    print(f"Semantic errors: {len(result.semantic_errors)}")

pip install sdcgovernance

from sdcgovernance import GovernanceEngine

engine = GovernanceEngine(model)
result = engine.evaluate_transition(
    current_state="draft",
    target_state="review",
    workflow_tree=tree,
)
print(result.decision)       # Decision.PERMIT
print(result.receipt.hash)   # SHA-256 tamper-evident receipt

git clone https://github.com/SemanticDataCharter/SDCRM.git
cd SDCRM
# See sdc4/schemas/sdc4.xsd (source of truth)
# See sdc4/specification/sdc4-specification.md

We welcome contributions to all SDC projects!

• Code - Implement features, fix bugs, improve performance
• Documentation - Write guides, improve examples, translate content
• Testing - Write tests, report bugs, verify fixes
• Use Cases - Share how you're using SDC
• Feedback - Suggest improvements, discuss architecture

1. Read the Contributing Guidelines
2. Check the Code of Conduct
3. Review the Security Policy
4. Find a project that interests you
5. Open an issue or PR

Each repository has its own CONTRIBUTING.md and CLAUDE.md files with specific guidance for that project.

We take security seriously. See our Security Policy for:

• Vulnerability reporting
• Security best practices
• Response timeline

Report vulnerabilities: security@axius-sdc.com

• Website: https://semanticdatacharter.com
• Specification: sdc4-specification.md
• Practitioners: https://axius-sdc.com/practitioners/
• PyPI Packages: sdcvalidator | sdcgovernance | form2sdc
• Historical Archive: GitHub
• AI Instructions: ai.txt

Controlled and maintained by: Axius SDC, Inc. Contact: contact@axius-sdc.com License: Apache 2.0 (specification and open source tools)

Founders:

• Timothy W. Cook - Founder & CEO
• Dr. Luciana Tricai Cavalini, PhD - Co-Founder & Partner
• Dr. Nikki Shaw, PhD - Co-Founder & Partner

Team: International (US, Canada, Brazil), 40+ years combined experience

• Website: semanticdatacharter.com
• Company: axius-sdc.com
• LinkedIn: Axius SDC
• Email: contact@axius-sdc.com

• SDC Specification: Apache 2.0
• Open Source Libraries: Apache 2.0
• Trademarks: "Semantic Data Charter" and "SDC" are trademarks of Axius SDC, Inc.

The autonomous era needs deterministic data. The substrate is ready.

Last Updated: April 2026