Preserve the source
Original labels and values remain available for traceability instead of being silently overwritten.
Public data · demonstration audit · not a customer engagement
Massachusetts public testing data
A buyer-facing example of how WeedDAO identifies structural interoperability problems before they become downstream data problems.
5,000public rows sampled
100%structural extraction rate
0 → 85.7%mapping coverage after structural adapter
0new scientific mappings required for the adapter gain
CSV + JSONnative state formats represented
Human QAbefore a commercial deliverable is released
Executive summary
Structure and terminology are different problems.
WeedDAO ran its interoperability engine against a 5,000-row sample from the Massachusetts Cannabis Control Commission's 2025 public testing-results data. The initial source structure prevented the analyte mapper from recognizing composite labels. After applying a source-specific structural adapter, mapping coverage increased from 0.000 to 0.857143 while structural extraction reached 1.0 - without adding new scientific mappings for that improvement.
What the demonstration shows
A structurally unusual export can make a terminology dictionary appear ineffective even when the scientific mapping logic is not the underlying problem. WeedDAO separates structural interpretation from semantic normalization while preserving source-native labels, values, and provenance.
Separate failure types
Structural parse issues, terminology gaps, ambiguous labels, and ordinary unmapped items are not treated as the same problem.
Abstain when needed
Unsupported canonicalization can be withheld and routed to human review rather than guessed.
What a WeedDAO audit reports
Audit categories
- Analyte mapping coverage
- Unit parse coverage
- Status parse coverage
- Metadata field presence
- Ambiguous and unmapped labels
- Human-review items
- Structural exceptions
Deliverables
- Executive summary
- Source / schema inventory
- Interoperability findings
- Recommended export improvements
- Technical appendix
- Machine-readable JSON
- Exception CSV
Scope note
This audit evaluates data structure and interoperability characteristics of the supplied export. It does not determine laboratory analytical accuracy, regulatory compliance, accreditation status, or product safety.
How a real engagement starts
01
Review the public demo
No private data is required to understand the format, limitations, and level of detail.
02
Agree on scope and data handling
Confirm supported format, file count, schema complexity, sanitization, and review expectations before accepting an export.
03
Audit + human QA
Preserve the source, apply the structural path, review exceptions, and deliver human-readable plus machine-readable outputs.
Public source: Massachusetts Cannabis Control Commission Open Data, Testing Results 2025. The Commission states that Open Data information is self-reported by licensees and notes a March 19, 2026 correction to individual test-status labeling. View the Commission data catalog.
Next step
Want to see whether this is useful for your export?
Start with a conversation about the data structure and use case. No LIMS credentials or live-system access are required for the current audit product.