--- annotations_creators: - expert-generated language_creators: - expert-generated license: - cc-by-4.0 pretty_name: cannabis_licenses size_categories: - 10K - **Repository:** - **Point of Contact:** ### 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) | | | [California](https://search.cannabis.ca.gov/) | | | [Colorado](#) | | | [Connecticut](#) | | | [District of Columbia](#) | | | [Illinois](#) | | | [Maine](#) | | | [Massachusetts](#) | | | [Michigan](#) | | | [Montana](https://mtrevenue.gov/cannabis/#CannabisLicenses) | | | [Nevada](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) | | | [New York](#) | | | [Oregon](https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx) | | | [Rhode Island](#) | | | [Vermont](#) | | | [Washington](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)
### 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.