How to Contribute · Guide

Help Build the Directory

Every edit makes this dataset more accurate for researchers, policymakers, and the cannabis industry. No coding required — just a GitHub account and a browser.

1

Sign in with GitHub

Click Login with GitHub on the directory page. We only read your username and avatar — nothing else.

2

Click any cell to edit

Once logged in, every cell becomes clickable. One cell at a time — keeps edits clean and reviewable.

3

Submit your edit

Click Submit Edit or press Enter. Your GitHub username, timestamp, and the change are recorded automatically.

4

View the ledger

Every edit is logged in the Edit Ledger with full transparency — who, what, when, and before/after values.

Dropdown Fields

Some columns have predefined options:

FieldOptions
Business_CategorySingle Location · Multi-Location · MSO · Vertically Integrated · Boutique · Franchise
Is_MedicalTRUE · FALSE
Is_Adult_UseTRUE · FALSE
Social_Equity_StatusTRUE · FALSE
Home_DeliveryTRUE · FALSE

For multi-value fields like Product_Focus, Payment_Methods, and Ownership_Type, use pipe-separated values:

Flower|Edibles|Concentrates

What to Contribute

The most valuable contributions right now are filling in the empty community columns:

You can also correct existing data — wrong addresses, outdated phone numbers, closed businesses.

📌 Guidelines One cell at a time. Cite your source in Notes if adding data from a website. Don't delete data — replace it. Be accurate — this dataset has a DOI and is used for research.

Architecture

The edit system runs entirely on AWS free tier:

Browser → API Gateway → Lambda → DynamoDB (HTTP API) (Python) (Ledger)

GitHub OAuth authenticates contributors. DynamoDB stores every edit as an immutable ledger entry. Full infrastructure docs in aws-setup.md.

Credits

Shannon Goddard — Research, data collection, legal analysis, manual verification, and the vision that turned a fragmented regulatory landscape into a single open dataset. Every record in this directory exists because she tracked it down, cleaned it, and verified it by hand.

Amazon Q (AWS AI Assistant) — Pipeline architecture, data processing scripts, AWS infrastructure (DynamoDB, Lambda, API Gateway), frontend development, community edit system, and documentation. Proud to have my name on the DOI.

Built with grit in Riverside, CA. Chaos Preferred. Integrity Required.