README.md

---
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
license:
  - cc-by-4.0
pretty_name: cannabis_licenses
size_categories:
  - 10K<n<100K
source_datasets:
  - original
tags:
  - cannabis
  - licenses
  - licensees
---

# Cannabis Licenses, Curated by Cannlytics

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Data Collection and Normalization](#data-collection-and-normalization)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [License](#license)
  - [Citation](#citation)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** <https://github.com/cannlytics/cannlytics>
- **Repository:** <https://huggingface.co/datasets/cannlytics/cannabis_licenses>
- **Point of Contact:** <dev@cannlytics.com>

### Dataset Summary

This dataset is a collection of cannabis license data for the licensees that have been permitted in the United States.

## Dataset Structure

The dataset is partitioned into subsets for each state.


| State | Licenses |
|-------|----------|
|  [Alaska](#) |  |
|  [Arizona](#) |  |
|  [California](#) | ✅ |
|  [Colorado](#) |  |
|  [Connecticut](#) |  |
|  [District of Columbia](#) |  |
|  [Illinois](#) |  |
|  [Maine](#) |  |
|  [Massachusetts](#) |  |
|  [Michigan](#) |  |
|  [Montana](#) |  |
|  [Nevada](#) |  |
|  [New Hampshire](#) |  |
|  [New Jersey](#) |  |
|  [New Mexico](#) |  |
|  [New York](#) |  |
|  [Oregon](#) | ✅  |
|  [Rhode Island](#) |  |
|  [Vermont](#) |  |
|  [Washington](#) |  |


### Data Instances

You can load the licenses for each state. For example:

```py
from datasets import load_dataset

# Get the licenses for a specific state.
dataset = load_dataset('cannlytics/cannabis_licenses', 'ca')
data = dataset['data']
assert len(data) > 0
print('%i licenses.' % len(data))
```

### Data Fields

Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect encounter in the parsed COA data.

| Field | Example | Description |
|-------|-----|-------------|
| `id` | `"1046"` |  |
| `license_number` | `"C10-0000423-LIC"` |  |
| `license_status` | `"Active"` |  |
| `license_status_date` | `""` |  |
| `license_term` | `"Provisional"` |  |
| `license_type` | `"Commercial -  Retailer"` |  |
| `license_designation` | `"Adult-Use and Medicinal"` |  |
| `issue_date` | `"2019-07-15T00:00:00"` |  |
| `expiration_date` | `"2023-07-14T00:00:00"` |  |
| `licensing_authority_id` | `"BCC"` |  |
| `licensing_authority` | `"Bureau of Cannabis Control (BCC)"` |  |
| `business_legal_name` | `"Movocan"` |  |
| `business_dba_name` | `"Movocan"` |  |
| `business_owner_name` | `"redacted"` |  |
| `business_structure` | `"Corporation"` |  |
| `activity` | `""` |  |
| `premise_street_address` | `"1632 Gateway Rd"` |  |
| `premise_city` | `"Calexico"` |  |
| `premise_state` | `"CA"` |  |
| `premise_county` | `"Imperial"` |  |
| `premise_zip_code` | `"92231"` |  |
| `business_email` | `"redacted@gmail.com"` |  |
| `business_phone` | `"(555) 555-5555"` |  |
| `parcel_number` | `""` |  |
| `premise_latitude` | `32.69035693` |  |
| `premise_longitude` | `-115.38987552` |  |
| `data_refreshed_date` | `"2022-09-21T12:16:33.3866667"` |  |

### Data Splits

The data is split into subsets by state. You can retrieve all licenses by requesting the `all` subset.

```py
from datasets import load_dataset

# Get all cannabis licenses.
repo = 'cannlytics/cannabis_licenses'
dataset = load_dataset(repo, 'all')
data = dataset['data']
```

## Dataset Creation

### Curation Rationale

Data about organizations operating in the cannabis industry for each state is valuable for research.

### Source Data

| State | Data Source URL |
|-------|-----------------|
|  [Alaska](#) |  |
|  [Arizona](https://azcarecheck.azdhs.gov/s/?licenseType=null) | <https://azcarecheck.azdhs.gov/s/?licenseType=null> |
|  [California](https://search.cannabis.ca.gov/) | <https://search.cannabis.ca.gov/> |
|  [Colorado](#) |  |
|  [Connecticut](#) |  |
|  [District of Columbia](#) |  |
|  [Illinois](#) |  |
|  [Maine](#) |  |
|  [Massachusetts](#) |  |
|  [Michigan](#) |  |
|  [Montana](https://mtrevenue.gov/cannabis/#CannabisLicenses) | <https://mtrevenue.gov/cannabis/#CannabisLicenses> |
|  [Nevada](https://ccb.nv.gov/list-of-licensees/) | <https://ccb.nv.gov/list-of-licensees/> |
|  [New Hampshire](#) |  |
|  [New Jersey](#) |  |
|  [New Mexico](https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US) | <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US> |
|  [New York](#) |  |
|  [Oregon](https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx) | <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx> |
|  [Rhode Island](#) |  |
|  [Vermont](#) |  |
|  [Washington](https://lcb.wa.gov/records/frequently-requested-lists) | <https://lcb.wa.gov/records/frequently-requested-lists> |

#### Data Collection and Normalization

In the `algorithms` directory, you can find the algorithms used for data collection. You can use these algorithms to recreate the dataset. First, you will need to clone the repository:

```
git clone https://huggingface.co/datasets/cannlytics/cannabis_licenses
```

You can then install the algorithm Python (3.9+) requirements:

```
cd cannabis_licenses
pip install -r requirements.txt
```

Then you can run all of the data-collection algorithms:

```
python algorithms/main.py
```

Or you can run each algorithm individually. For example:

```
python algorithms/get_licenses_ca.py
```

### Personal and Sensitive Information

This dataset includes names of individuals, public addresses, and contact information for cannabis licensees. It is important to take care to use these data points in a legal manner.

## Considerations for Using the Data

### Social Impact of Dataset

Arguably, there is substantial social impact that could result from the study of permitted adult-use cannabis, therefore, researchers and data consumers alike should take the utmost care in the use of this dataset.

### Discussion of Biases

Cannlytics is a for-profit data and analytics company that primarily serves cannabis businesses. The data are not randomly collected and thus sampling bias should be taken into consideration.

### Other Known Limitations

The data is for adult-use cannabis licenses. It would be valuable to include medical cannabis licenses too.

## Additional Information

### Dataset Curators

Curated by [🔥Cannlytics](https://cannlytics.com)<br>
<dev@cannlytics.com>

### License

```
Copyright (c) 2022 Cannlytics and the Cannabis Data Science Team

The files associated with this dataset are licensed under a 
Creative Commons Attribution 4.0 International license.

You can share, copy and modify this dataset so long as you give
appropriate credit, provide a link to the CC BY license, and
indicate if changes were made, but you may not do so in a way
that suggests the rights holder has endorsed you or your use of
the dataset. Note that further permission may be required for
any content within the dataset that is identified as belonging
to a third party.
```

### Citation

Please cite the following if you use the code examples in your research:

```bibtex
@misc{cannlytics2022,
  title={Cannabis Data Science},
  author={Skeate, Keegan},
  journal={https://github.com/cannlytics/cannabis-data-science},
  year={2022}
}
```

### Contributions

Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.