Comprehensive database of marijuana prices across legal U.S. states, including both recreational and medical cannabis markets.

BackgroundThe recent exponential increase in legalized medical and recreational cannabis, development of medical cannabis programs, and production of unregulated over-the-counter products (e.g., cannabidiol (CBD) oil, and delta-8-tetrahydrocannabinol (delta-8-THC)), has the potential to create unintended health consequences. The major cannabinoids (delta-9-tetrahydrocannabinol and cannabidiol) are metabolized by the same cytochrome P450 (CYP) enzymes that metabolize most prescription medications and xenobiotics (CYP3A4, CYP2C9, CYP2C19). As a result, we predict that there will be instances of drug-drug interactions and the potential for adverse outcomes, especially for prescription medications with a narrow therapeutic index.MethodsWe conducted a systematic review of all years to 2023 to identify real world reports of documented cannabinoid interactions with prescription medications. We limited our search to a set list of medications with predicted narrow therapeutic indices that may produce unintended adverse drug reactions (ADRs). Our team screened 4,600 reports and selected 151 full-text articles to assess for inclusion and exclusion criteria.ResultsOur investigation revealed 31 reports for which cannabinoids altered pharmacokinetics and/or produced adverse events. These reports involved 16 different Narrow Therapeutic Index (NTI) medications, under six drug classes, 889 individual subjects and 603 cannabis/cannabinoid users. Interactions between cannabis/cannabinoids and warfarin, valproate, tacrolimus, and sirolimus were the most widely reported and may pose the greatest risk to patients. Common ADRs included bleeding risk, altered mental status, difficulty inducing anesthesia, and gastrointestinal distress. Additionally, we identified 18 instances (58%) in which clinicians uncovered an unexpected serum level of the prescribed drug. The quality of pharmacokinetic evidence for each report was assessed using an internally developed ten-point scale.ConclusionDrug-drug interactions with cannabinoids are likely amongst prescription medications that use common CYP450 systems. Our findings highlight the need for healthcare providers and patients/care-givers to openly communicate about cannabis/cannabinoid use to prevent unintended adverse events. To that end, we have developed a free online tool (www.CANN-DIR.psu.edu) to help identify potential cannabinoid drug-drug interactions with prescription medications.

The spot price for wholesale cannabis in the United States has fluctuated over the past three years. In January 2024, the spot price of wholesale cannabis was ***** U.S. dollars per pound.

Current prices and price indexes of cannabis, by medical purposes and non-medical purposes, Canada, provinces and territories, annual.

The District of Columbia holds the record for the highest price per ounce of high quality marijuana in the United States, as of January 2025, with an average price of some 590 U.S. dollars per ounce. Recreational cannabis use Recreational cannabis is not legal in all U.S. states; however, many consumers use the drug illicitly. A recent survey indicated that over half of U.S. adults think that cannabis should be legalized and taxed like alcohol and tobacco. Recreational cannabis is often used by consumers for relaxation, stress relief, and creativity, to name a few. Surveys have shown that a majority of recreation users use marijuana for relaxation. Legal marijuana market potential It is projected that by 2025 the sales of legal cannabis in the U.S. will generate approximately 25 billion dollars in revenue. The regulation of marijuana includes the taxation of all sales. There is potential for the U.S. cannabis market to generate significant taxes and boost the economy. It is estimated that the taxes from legal marijuana sales would exceed the taxes earned from U.S. sales taxes by a significant amount.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IMCC IM Cannabis Corp. Common Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Canada Consumer Price Index (CPI): AT: Recreational Cannabis data was reported at 71.100 Dec2018=100 in Mar 2025. This records an increase from the previous number of 71.000 Dec2018=100 for Feb 2025. Canada Consumer Price Index (CPI): AT: Recreational Cannabis data is updated monthly, averaging 76.150 Dec2018=100 from Dec 2018 (Median) to Mar 2025, with 76 observations. The data reached an all-time high of 100.800 Dec2018=100 in May 2019 and a record low of 70.900 Dec2018=100 in Sep 2023. Canada Consumer Price Index (CPI): AT: Recreational Cannabis data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.I002: Consumer Price Index: 2002=100.

Context

Cannabis is a genus of flowering plants in the family Cannabaceae.

Source: https://en.wikipedia.org/wiki/Cannabis

Content

In October 2016, Phylos Bioscience released a genomic open dataset of approximately 850 strains of Cannabis via the Open Cannabis Project. In combination with other genomics datasets made available by Courtagen Life Sciences, Michigan State University, NCBI, Sunrise Medicinal, University of Calgary, University of Toronto, and Yunnan Academy of Agricultural Sciences, the total amount of publicly available data exceeds 1,000 samples taken from nearly as many unique strains.

https://medium.com/google-cloud/dna-sequencing-of-1000-cannabis-strains-publicly-available-in-google-bigquery-a33430d63998

These data were retrieved from the National Center for Biotechnology Information’s Sequence Read Archive (NCBI SRA), processed using the BWA aligner and FreeBayes variant caller, indexed with the Google Genomics API, and exported to BigQuery for analysis. Data are available directly from Google Cloud Storage at gs://gcs-public-data--genomics/cannabis, as well as via the Google Genomics API as dataset ID 918853309083001239, and an additional duplicated subset of only transcriptome data as dataset ID 94241232795910911, as well as in the BigQuery dataset bigquery-public-data:genomics_cannabis.

All tables in the Cannabis Genomes Project dataset have a suffix like _201703. The suffix is referred to as [BUILD_DATE] in the descriptions below. The dataset is updated frequently as new releases become available.

The following tables are included in the Cannabis Genomes Project dataset:

Sample_info contains fields extracted for each SRA sample, including the SRA sample ID and other data that give indications about the type of sample. Sample types include: strain, library prep methods, and sequencing technology. See SRP008673 for an example of upstream sample data. SRP008673 is the University of Toronto sequencing of Cannabis Sativa subspecies Purple Kush.

MNPR01_reference_[BUILD_DATE] contains reference sequence names and lengths for the draft assembly of Cannabis Sativa subspecies Cannatonic produced by Phylos Bioscience. This table contains contig identifiers and their lengths.

MNPR01_[BUILD_DATE] contains variant calls for all included samples and types (genomic, transcriptomic) aligned to the MNPR01_reference_[BUILD_DATE] table. Samples can be found in the sample_info table. The MNPR01_[BUILD_DATE] table is exported using the Google Genomics BigQuery variants schema. This table is useful for general analysis of the Cannabis genome.

MNPR01_transcriptome_[BUILD_DATE] is similar to the MNPR01_[BUILD_DATE] table, but it includes only the subset transcriptomic samples. This table is useful for transcribed gene-level analysis of the Cannabis genome.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: http://opencannabisproject.org/ Category: Genomics Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://www.ncbi.nlm.nih.gov/home/about/policies.shtml - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. Update frequency: As additional data are released to GenBank View in BigQuery: https://bigquery.cloud.google.com/dataset/bigquery-public-data:genomics_cannabis View in Google Cloud Storage: gs://gcs-public-data--genomics/cannabis

Banner Photo by Rick Proctor from Unplash.

Inspiration

Which Cannabis samples are included in the variants table?

Which contigs in the MNPR01_reference_[BUILD_DATE] table have the highest density of variants?

How many variants does each sample have at the THC Synthase gene (THCA1) locus?

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SNDL: Stemming the Tide of the Cannabis Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Zip codes identified by the Arizona Department of Health Services to promote ownership of marijuana establishments in communities disproportionately affected by the enforcement of Arizona’s previous marijuana laws under Prop 207 (passed by Arizona voters in Nov. 2020).In December 2021, the Arizona Department of Health Services will issue 26 adult-use Marijuana Establishment licenses to applicants who qualify under the social equity ownership program. More information about the State of Arizona's Medical Marijuana Social Equity Ownership Program can be found here: https://azdhs.gov/licensing/marijuana/social-equity/index.php To see if you quality go to the map on this page: https://www.azdhs.gov/licensing/marijuana/social-equity/index.php#qualification The source data for this data layer was the Census 2020 Zip Code Tabulation Areas (ZCTAs). Zip codes for the Medical Marijuana Social Equity Ownership Program were selected by the Arizona Department of Health Services, which were then identified in the ZCTA 2020 GIS layer.

Housing Prices and Access to Cannabis

A comparison of legal and non-legal states

About this dataset

The legal cannabis industry is booming, and it's having a major impact on housing prices across the country. In states where recreational cannabis sales are legal, prices have skyrocketed. But what about in states where it is not?

This dataset compare housing prices in legal and non-legal states, in order to determine whether or not there is a correlation between the two. Are houses in legal states really worth more? Or is something else at play?

Download the dataset and take a closer look to find out!

• State: The state in which the housing data was collected
• HPI_AT_BDL_national: The national average home price index (HPI) value for that state
• HPI_AT_BDL_state: The statewide home price index (HPI) value for that state
• legal: A binary value indicating whether or not recreational cannabis sales are legal in that state

Research Ideas

• Research whether or not there is a correlation between housing prices and states where recreational cannabis sales are legal.
• Compare housing prices in states where recreational cannabis sales are legal and those where they are not, in order to determine if there is a difference between the two groups.
• Examine if there is a relationship between changes in housing prices and changes in the legal status of recreational cannabis sales in a state

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Leafy Holdings (LFLY): Will Cannabis Flower Sales Continue to Grow?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China Trade Index: YoY: Unit Value: Import HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data was reported at 128.600 Prev Year=100 in Mar 2025. This records an increase from the previous number of 63.800 Prev Year=100 for Feb 2025. China Trade Index: YoY: Unit Value: Import HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data is updated monthly, averaging 106.482 Prev Year=100 from Apr 2019 (Median) to Mar 2025, with 44 observations. The data reached an all-time high of 178.700 Prev Year=100 in Feb 2024 and a record low of 50.000 Prev Year=100 in May 2022. China Trade Index: YoY: Unit Value: Import HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

The spot price for wholesale indoor cannabis in the United States rose from 2019 to 2021 before declining in 2022 and again in 2023. The price of indoor cannabis stood at ***** U.S. dollars per pound in January 2023.

China Trade Index: MoM: Unit Value: Export HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data was reported at 104.200 Average 12 Mths PY=100 in Feb 2025. This records a decrease from the previous number of 117.400 Average 12 Mths PY=100 for Jan 2025. China Trade Index: MoM: Unit Value: Export HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data is updated monthly, averaging 100.300 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 71 observations. The data reached an all-time high of 179.000 Average 12 Mths PY=100 in Mar 2022 and a record low of 61.900 Average 12 Mths PY=100 in Jun 2020. China Trade Index: MoM: Unit Value: Export HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: MoM: HS4 Classification.

China EQI: YoY: HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data was reported at 7.200 Prev Year=100 in Feb 2025. This records an increase from the previous number of 1.000 Prev Year=100 for Jan 2025. China EQI: YoY: HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data is updated monthly, averaging 150.798 Prev Year=100 from Mar 2018 (Median) to Feb 2025, with 64 observations. The data reached an all-time high of 11,421.300 Prev Year=100 in Jun 2021 and a record low of 1.000 Prev Year=100 in Jan 2025. China EQI: YoY: HS4: True Hemp (Cannabis Sativa L.), Raw or Processed but not Spun; Tow and Waste of True Hemp (Including Yarn Waste and Garnetted Stock). data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: YoY: HS4 Classification.

This statistic presents select cities in the U.S. based on the price of medical/recreational marijuana, reported between April and December 2024, in U.S. dollars per ounce. In New Orleans, Louisiana, the price per ounce of high-quality marijuana stood at *** dollars, as of July 28, 2024. Medical Marijuana Market Growth The medical marijuana industry in the U.S. is experiencing rapid expansion, with retail sales projected to reach *** billion by 2028. This growth is driven by increasing legalization, with ** states and the District of Columbia now permitting medical marijuana use. Florida leads in patient numbers, with approximately ******* registered users as of mid-2023. The market's expansion is also evident in the variety of consumption methods, with flowers and edibles being the most popular among U.S. adults. Regulatory Landscape and Consumer Access Despite the growing acceptance of medical marijuana, federal law still classifies cannabis with over ***% THC as illegal. This contradiction between state and federal laws creates a complex regulatory environment. However, public support for marijuana legalization continues to rise, with most Americans believing in its valid medical uses. States like Arizona and Illinois are forecasted to have the largest growth potential within the U.S. medical marijuana market between 2021 and 2026.

BackgroundCannabis is the third most commonly used drug worldwide, with studies suggesting a deleterious effect on some aspects of performance monitoring. It is unknown, however, whether diminished error awareness influences adaptive behaviour in cannabis users. Therefore, this study examined the effect of error awareness on learning from errors in cannabis users.MethodsThirty-six chronic cannabis users (Mage = 23.81 years; female, 36%) and 34 controls (Mage = 21.53 years; female, 76%) completed a Go/No-Go task that allowed participants to learn from errors and adapt their behaviour. Multilevel models were specified to determine whether the effect of error awareness on learning from errors differs between cannabis users and controls, and whether cannabis-use measures predict error correction while accounting for error awareness.ResultsWhile error awareness and correction rates did not differ between the groups, there was a significant effect of age of use onset on error correction in cannabis users. Further, the effect of error awareness was dependent on age of onset, and cannabis use-related frequency and harm. That is, cannabis users reporting an earlier age of regular use or scoring higher on the cannabis use index were less likely to perform correctly following an aware error.ConclusionIt appears overall cannabis use might not be tightly coupled to behavioural indices of performance monitoring. There is evidence, however, that aspects of cannabis use predict impairments in learning from errors that may be associated with treatment outcomes.

Includes; analysis of correlation between indices, The dry weight of all ecotypes, which includes male and female plants In two replicates which includes water deficit stress and control treatment.

Cannabis (hemp and marijuana) is an iconic yet controversial crop. On the one hand, it represents a growing market for pharmaceutical and agricultural sectors. On the other hand, plants synthesizing the psychoactive THC produce the most widespread illicit drug in the world. Yet, the difficulty to reliably distinguish between Cannabis varieties based on morphological or biochemical criteria impedes the development of promising industrial programs and hinders the fight against narcotrafficking. Genetics offers an appropriate alternative to characterize drug vs. non-drug Cannabis. However, forensic applications require rapid and affordable genotyping of informative and reliable molecular markers for which a broad-scale reference database, representing both intra- and inter-variety variation, is available. Here we provide such a resource for Cannabis, by genotyping 13 microsatellite loci (STRs) in 1 324 samples selected specifically for fibre (24 hemp varieties) and drug (15 marijuana varieties) production. We showed that these loci are sufficient to capture most of the genome-wide diversity patterns recently revealed by NGS data. We recovered strong genetic structure between marijuana and hemp and demonstrated that anonymous samples can be confidently assigned to either plant types. Fibres appear genetically homogeneous whereas drugs show low (often clonal) diversity within varieties, but very high genetic differentiation between them, likely resulting from breeding practices. Based on an additional test dataset including samples from 41 local police seizures, we showed that the genetic signature of marijuana cultivars could be used to trace crime scene evidence. To date, our study provides the most comprehensive genetic resource for Cannabis forensics worldwide.