Preserve the source
Original labels, values, and provenance remain intact. Normalized fields are added as a separate analytical layer rather than replacing source-native data.
Research · Data · Interoperability
Better infrastructure for cannabis laboratory data.
WeedDAO builds research and interoperability tools that make cannabis lab data easier to standardize, audit, exchange, and analyze while preserving the meaning of the original source records.
Research and data cooperative in formation. No token. No lab rankings. No medical claims.
WeedDAO Interoperability Audit
Find the structural problems that make lab data difficult to reuse.
The audit examines supported laboratory exports for analyte naming, units, result and status semantics, metadata completeness, ambiguity, and schema-level issues that complicate exchange and downstream analysis.
Surface interoperability issues
Flag unmapped or ambiguous analytes, unit and status parsing issues, missing metadata, and structural exceptions that may require review.
Deliver usable outputs
Receive an executive summary, technical appendix, machine-readable JSON, and exception output designed for QA, data, and laboratory-systems teams.
What the audit reports
- Analyte mapping coverage
- Unit parse coverage
- Status parse coverage
- Metadata field presence
- Ambiguous and unmapped labels
- Human-review items
- Structural exceptions
- Recommended export-structure improvements
Current scope
Designed for supported CSV and JSON exports, including ordinary long-format data, Massachusetts-style composite labels, and compatible wide-format files.
PDF/image ingestion, direct LIMS access, and API integrations are not part of the current product scope.
View the public demonstration →
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 it works
See the problem before sharing private data.
Start with the public demonstration audit. If an engagement makes sense, scope and data handling are agreed before any private export is accepted. WeedDAO does not require LIMS credentials or live-system access for the current audit product.
Review the demo
See the format, findings, limitations, and level of technical detail using a public-data demonstration before committing internal data.
Agree on scope
Define the supported export, file count, schema complexity, review needs, and data-handling terms before an audit engagement begins.
Audit + human QA
WeedDAO preserves the source, runs the interoperability audit, reviews exceptions, and delivers both human-readable and machine-readable outputs.
Built for
Laboratory QA & data teams
Teams responsible for export consistency, terminology, and downstream reuse.
Built for
LIMS & laboratory-systems teams
Teams working across customer exports, integrations, and heterogeneous schemas.
Built for
Multi-site operators & researchers
Teams combining laboratory results across facilities, vendors, or reporting systems.
No private data required
Start with the public Massachusetts demonstration audit.
Review the deliverable first. If it looks useful, then we can discuss a scoped audit of a supported sanitized export.
Public-data research
Research informs the product.
Our research program studies how public cannabis laboratory records differ across reporting systems and how those differences can be handled without silently changing the meaning of source data.
Preserve source-native data
Original labels and values remain intact. Normalized values are treated as a separate analytical layer rather than replacements for source data.
Audit normalization
Analyte mappings, unit-like tokens, and schema assumptions are reviewed for collisions, ambiguity, and unsupported interpretation.
Validate across formats
We use public laboratory and state testing datasets to test schema adapters, provenance handling, deterministic behavior, and terminology coverage.
What we're working on
Infrastructure before hype.
01
Cannabis laboratory data standards and interoperability
02
Analyte, unit, and result-status normalization
03
Measurement metadata such as LOD, LOQ, methods, and identifiers
04
Public-data research and reproducible methodology
05
Collaboration with laboratories, LIMS teams, QA groups, researchers, and other lawful industry participants
Principles
Useful infrastructure requires restraint.
WeedDAO is designed around transparent assumptions, clear limits, and preserving the distinction between what data structure can establish and what it cannot.
What we do
Study interoperability, document normalization assumptions, preserve source-native records, flag ambiguity, and use human review where automated interpretation should stop.
What we don't claim
No laboratory rankings, no blanket claims about lab accuracy, no consumer-health conclusions, no compliance certification, and no endorsement by cited data providers or agencies.
Get in touch
Working with cannabis laboratory data?
We welcome conversations with laboratory QA, data, LIMS, compliance, measurement-science, and research teams interested in interoperability or in reviewing the demonstration audit.