cannlytics/cannabis_tests

Cannabis Tests, Curated by Cannlytics

Dataset Summary

This dataset is a collection of public cannabis lab test results parsed by CoADoc, a certificate of analysis (COA) parsing tool.

Dataset Structure

The dataset is partitioned into the various sources of lab results.

Subset	Source	Observations
rawgarden	Raw Gardens	2,667
mcrlabs	MCR Labs	Coming soon!
psilabs	PSI Labs	Coming soon!
sclabs	SC Labs	Coming soon!
washington	Washington State	Coming soon!

Data Instances

You can load the details for each of the dataset files. For example:

from datasets import load_dataset

# Download Raw Garden lab result details.
dataset = load_dataset('cannlytics/cannabis_tests', 'rawgarden')
details = dataset['details']
assert len(details) > 0
print('Downloaded %i observations.' % len(details))

Note: Configurations for results and values are planned. For now, you can create these data with CoADoc().save(details, out_file).

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
analyses	["cannabinoids"]	A list of analyses performed on a given sample.
{analysis}_method	"HPLC"	The method used for each analysis.
{analysis}_status	"pass"	The pass, fail, or N/A status for pass / fail analyses.
coa_urls	[{"url": "", "filename": ""}]	A list of certificate of analysis (CoA) URLs.
date_collected	2022-04-20T04:20	An ISO-formatted time when the sample was collected.
date_tested	2022-04-20T16:20	An ISO-formatted time when the sample was tested.
date_received	2022-04-20T12:20	An ISO-formatted time when the sample was received.
distributor	"Your Favorite Dispo"	The name of the product distributor, if applicable.
distributor_address	"Under the Bridge, SF, CA 55555"	The distributor address, if applicable.
distributor_street	"Under the Bridge"	The distributor street, if applicable.
distributor_city	"SF"	The distributor city, if applicable.
distributor_state	"CA"	The distributor state, if applicable.
distributor_zipcode	"55555"	The distributor zip code, if applicable.
distributor_license_number	"L2Stat"	The distributor license number, if applicable.
images	[{"url": "", "filename": ""}]	A list of image URLs for the sample.
lab_results_url	"https://cannlytics.com/results"	A URL to the sample results online.
producer	"Grow Tent"	The producer of the sampled product.
producer_address	"3rd & Army, SF, CA 55555"	The producer's address.
producer_street	"3rd & Army"	The producer's street.
producer_city	"SF"	The producer's city.
producer_state	"CA"	The producer's state.
producer_zipcode	"55555"	The producer's zipcode.
producer_license_number	"L2Calc"	The producer's license number.
product_name	"Blue Rhino Pre-Roll"	The name of the product.
lab_id	"Sample-0001"	A lab-specific ID for the sample.
product_type	"flower"	The type of product.
batch_number	"Order-0001"	A batch number for the sample or product.
metrc_ids	["1A4060300002199000003445"]	A list of relevant Metrc IDs.
metrc_lab_id	"1A4060300002199000003445"	The Metrc ID associated with the lab sample.
metrc_source_id	"1A4060300002199000003445"	The Metrc ID associated with the sampled product.
product_size	2000	The size of the product in milligrams.
serving_size	1000	An estimated serving size in milligrams.
servings_per_package	2	The number of servings per package.
sample_weight	1	The weight of the product sample in grams.
results	[{...},...]	A list of results, see below for result-specific fields.
status	"pass"	The overall pass / fail status for all contaminant screening analyses.
total_cannabinoids	14.20	The analytical total of all cannabinoids measured.
total_thc	14.00	The analytical total of THC and THCA.
total_cbd	0.20	The analytical total of CBD and CBDA.
total_terpenes	0.42	The sum of all terpenes measured.
results_hash	"{sha256-hash}"	An HMAC of the sample's results JSON signed with Cannlytics' public key, "cannlytics.eth".
sample_id	"{sha256-hash}"	A generated ID to uniquely identify the producer, product_name, and results.
sample_hash	"{sha256-hash}"	An HMAC of the entire sample JSON signed with Cannlytics' public key, "cannlytics.eth".

Each result can contain the following fields.

Field	Example	Description
analysis	"pesticides"	The analysis used to obtain the result.
key	"pyrethrins"	A standardized key for the result analyte.
name	"Pyrethrins"	The lab's internal name for the result analyte
value	0.42	The value of the result.
mg_g	0.00000042	The value of the result in milligrams per gram.
units	"ug/g"	The units for the result value, limit, lod, and loq.
limit	0.5	A pass / fail threshold for contaminant screening analyses.
lod	0.01	The limit of detection for the result analyte. Values below the lod are typically reported as ND.
loq	0.1	The limit of quantification for the result analyte. Values above the lod but below the loq are typically reported as <LOQ.
status	"pass"	The pass / fail status for contaminant screening analyses.

Data Splits

The data is split into details, results, and values data. Configurations for results and values are planned. For now, you can create these data with:

from cannlytics.data.coas import CoADoc
from datasets import load_dataset
import pandas as pd

# Download Raw Garden lab result details.
repo = 'cannlytics/cannabis_tests'
dataset = load_dataset(repo, 'rawgarden')
details = dataset['details']

# Save the data locally with "Details", "Results", and "Values" worksheets.
outfile = 'details.xlsx'
parser = CoADoc()
parser.save(details.to_pandas(), outfile)

# Read the values.
values = pd.read_excel(outfile, sheet_name='Values')

# Read the results.
results = pd.read_excel(outfile, sheet_name='Results')

Dataset Creation

Curation Rationale

Certificates of analysis (CoAs) are abundant for cannabis cultivators, processors, retailers, and consumers too, but the data is often locked away. Rich, valuable laboratory data so close, yet so far away! CoADoc puts these vital data points in your hands by parsing PDFs and URLs, finding all the data, standardizing the data, and cleanly returning the data to you.

Source Data

Data Source	URL
MCR Labs Test Results	https://reports.mcrlabs.com
PSI Labs Test Results	https://results.psilabs.org/test-results/
Raw Garden Test Results	https://rawgarden.farm/lab-results/
SC Labs Test Results	https://client.sclabs.com/
Washington State Lab Test Results	https://lcb.app.box.com/s/e89t59s0yb558tjoncjsid710oirqbgd

Data Collection and Normalization

You can recreate the dataset using the open source algorithms in the repository. First clone the repository:

git clone https://huggingface.co/datasets/cannlytics/cannabis_tests

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

cd cannabis_tests
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_results_mcrlabs.py

In the algorithms directory, you can find the data collection scripts described in the table below.

Algorithm	Organization	Description
get_results_mcrlabs.py	MCR Labs	Get lab results published by MCR Labs.
get_results_psilabs.py	PSI Labs	Get historic lab results published by MCR Labs.
get_results_rawgarden.py	Raw Garden	Get lab results Raw Garden publishes for their products.
get_results_sclabs.py	SC Labs	Get lab results published by SC Labs.
get_results_washington.py	Washington State	Get historic lab results obtained through a FOIA request in Washington State.

Personal and Sensitive Information

The dataset includes public addresses and contact information for related 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 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 represents only a subset of the population of cannabis lab results. Non-standard values are coded as follows.

Actual	Coding
'ND'	0.000000001
'No detection in 1 gram'	0.000000001
'Negative/1g'	0.000000001
'PASS'	0.000000001
'<LOD'	0.00000001
'< LOD'	0.00000001
'<LOQ'	0.0000001
'< LOQ'	0.0000001
'<LLOQ'	0.0000001
'≥ LOD'	10001
'NR'	None
'N/A'	None
'na'	None
'NT'	None

Additional Information

Dataset Curators

Curated by 🔥Cannlytics
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:

@misc{cannlytics2022,
  title={Cannabis Data Science},
  author={Skeate, Keegan and O'Sullivan-Sutherland, Candace},
  journal={https://github.com/cannlytics/cannabis-data-science},
  year={2022}
}

Contributions

Thanks to 🔥Cannlytics, @candy-o, @hcadeaux, @keeganskeate, The CESC, and the entire Cannabis Data Science Team for their contributions.