Number and percentage of people reporting cannabis use in the past three months by quarter, geography, gender, age, household population aged 15 years or older, Canada.

Context

Cannabis Strains

Content

strain name: Given name of strain

type of strain: indica, sativa, hybrid

rating: user ratings averaged

effects: different effects optained

taste: taste of smoke

description: backround, etc

Acknowledgements

leafly.com

Inspiration

Marijuana may get a bad rep in the media as far as the decriminalization debate goes, but its health benefits can no longer go unnoticed. With various studies linking long-term marijuana use to positive, health-related effects, there are more than just a few reasons to smoke some weed every day.

A study done by the Boston Medical Center and the Boston University of Medicine, examined 589 drug users—more than 8 out of 10 of whom were pot smokers. It determined that “weed aficionados” were no more likely to visit the doctor than non-drug users. If an increased risk of contracting ailments is what’s preventing you from smoking more weed, it looks like you’re in the clear!

One of the greatest medicinal benefits of marijuana is its pain relieving qualities, which make it especially effective for treating chronic pain. From menstruation cramps to nerve pain, as little as three puffs of bud a day can help provide the same relief as synthetic painkillers. Marijuana relieves pain by “changing the way the nerves function,” says Mark Ware, MD and assistant professor of anesthesia and family medicine at McGill University.

Studies have found that patients suffering from arthritis could benefit from marijuana use. This is because naturally occurring chemicals in cannabis work to activate pathways in the body that help fight off joint inflammation.

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

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). This indicator is based on self-report and includes adults who used any form of marijuana at least one time in the past month.Among federally prohibited drugs and substances, marijuana is the most commonly used. In early 2018, marijuana became legal for recreational sale and consumption in California. Using marijuana at any age can lead to negative health consequences, which include psychological conditions such as depression or anxiety; brain damage affecting memory, attention, and learning ability; lung and cardiovascular system damage; harm to developing fetuses or infants; and increased risk for motor vehicle crashes. Marijuana use has long been associated with the use of other substances, including alcohol, tobacco, and prescription and illicit narcotics. Cities and communities should take an active role in educating residents, particularly youth, pregnant persons, and other vulnerable groups, about the potential risks of marijuana use and adopt policies that regulate and ensure safe marijuana retail activity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This paper presents estimates of a dynamic individual-level model of cannabis consumption, using data from a 1998 survey of young people in Britain. The econometric model is a split-population generalization of the non-stationary Poisson process, allowing for separate dynamic process for initiation into cannabis use and subsequent consumption. The model allows for heterogeneity in consumption levels and behavioural shifts induced by leaving education and the parental home.

1. Model Purpose & Description

The model aims to recognize and categorize common weeds found in Maryland lawns It is made up of eight classes consisting of four species of Native Maryland Lawn Weeds: Red Deadnettle (Lamium purpureum), Dandelion (Taraxacum officinale), Hairy Bittercress (Cardamine hirsuta), and Shepherd's Purse (Capsella bursa-pastoris). These weeds were chosen due to their prevalence and their known medicinal and edible properties. The samples, which were collected from iNaturalist (a social media platform that uses machine learning to identify wildlife through use of community scientists and verification by professionals and other users), consist primarily of images of the adult stages of the weeds and their leaves (Loarie, 2023).

While the primary objective of the model is to identify common weeds found in Maryland lawns, the model’s capabilities will be extended to provide information about the medical and edible properties of the weeds based on credible sources utilizing this overview page. The model is intended to serve as a resource for individuals experiencing food scarcity or those seeking to adopt more sustainable practices such as foragers or people interested in herbal medicine. Also, it could be particularly beneficial for low-income individuals who may struggle to afford medicines or natural foods. Given that many weeds are rich in vitamins, possess medicinal properties, and can be used in a variety of dishes, this model could help mitigate the scarcity of healthy food and medical resources (Gardiner, 2021; Salehi et al., 2019). Also, by collecting weeds, individuals are participating in gleaning, a process involving collecting surplus fresh foods from various sources to help those in need, reducing food waste and enhancing food security, which benefits both the community by providing nutritious food options and the environment by minimizing waste (USDA, 2009).

While the model can be used to find information about the properties of these weeds this information is suggested and should NOT be taken into account over the opinion of professionals. Also, individuals should make sure NOT to pick weeds from locations that use herbicides, pesticides, or other harmful chemicals (USDA, 2009). Therefore, this model is a tool for plant identification and a platform for promoting sustainability and self-sufficiency. It aims to educate users about the untapped potential of local plants and encourage a more holistic view of the environment. Additionally, This model was created for a class assignment in AI and Natural History at St. Mary’s College of Maryland.

1. Description of Plant Properties

• Red Deadnettle (Lamium purpureum)

Deadnettle is a versatile plant with a plethora of medicinal properties, including being astringent, purgative, diuretic, diaphoretic, anti-inflammatory, anti-fungal, and anti-bacterial (LovelyPinesFarm, n.d.). It’s rich in vitamins C, A, and K, iron, calcium, magnesium, manganese, and fiber, making it beneficial for treating colds and allergies, and as a detox aid (Gardiner, 2021). The leaves can be used topically as a poultice to dress wounds or to stop bleeding due to their antifungal and anti-bacterial nature (Gardiner, 2021). In terms of edibility, dead nettle leaves are not only nutritious but can also be consumed in various forms—raw, cooked, or as tea, which can have a laxative effect if taken in large quantities (Gardiner, 2021; LovelyPinesFarm, n.d.). They can also be transformed into pesto or dried for tea blends, offering a healthy addition similar to spinach or kale (Gardiner, 2021).

• Dandelion (Taraxacum officinale)

Dandelion, a plant known for its medicinal and edible properties, serves as a natural diuretic and is used to treat various conditions, including infections and digestive issues (NCCIH, n.d). The leaves and flowers are highly nutritious, offering a rich source of vitamins A, B complex, C, and D, as well as minerals like iron, potassium, and zinc (Bellarmine, n.d.; Jarvoe, 2015). While the stem is inedible due to its bitter substance, the leaves can be enjoyed fresh in salads, cooked like spinach, or used as a flavorful herb (NCCIH, n.d; SLC, n.d). The flowers, sweeter than the leaves, can be added to pancakes, fritters, or even used to make dandelion wine (Bellermain, n.d.). The roasted roots are a popular coffee substitute, and the entire plant is known to stimulate digestion and act as a mild laxative, potentially aiding those with poor liver function (Bellermain, n.d.). Dandelion’s versatility extends to a variety of culinary uses, from beer and butter to salad and jelly, making it a valuable addition to both the medicine cabinet and the kitchen (Bellermain, n.d.).

• Hairy Bittercress (Cardamine hirsuta)

Hairy bittercress, a wild mustard relative, is a versatile and nutritious edible weed. It’s a natural source of vitamin C, calcium, magnesium, and beta-carotene, making it a healthy substitute for pars

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

Background:

The numbers of days that people consume alcohol and other drugs over a fixed time interval, such as 28 days, are often collected in surveys for research in the addictions field. The presence of an upper bound on these variables can result in response distributions with "ceiling effects". Also, if some peoples’ substance use behaviors are characterized by various weekly patterns of use, summaries of substance days-of-use over longer periods can exhibit multiple modes. Multiple modes can also result from "heaping" of responses when respondents are unsure about the precise value. These characteristics of substance days-of-use data mean that models assuming common parametric response distributions will not always provide a good fit.

Repository contents: Simulate longitudinal cannabis days-of-use over 28-day intervals intended to reproduce characteristics of data reported by respondents to an Australian survey of illicit drug users run over 4 waves during the COVID-19 pandemic in Australia in 2020–21. The dataset includes generated subject_id and survey_wave and iso explanatory variables, where iso is a dummy variable indicating subjects that were in quarantine or isolation at the time of the 28-day interval. R-code to fit proportional-odds and continuation-ratio ordinal models as well as binomial, beta-binomial, negative binomial and hurdle negative binomial models to these data are available at a linked companion website. Methods We fitted a Bayesian multinomial model to reported cannabis days-of-use over four 28-day intervals (four survey waves) during the COVID-19 pandemic in Australia. Cannabis days-of-use was modeled as a nominal categorical variable with 29 levels, one for each possible response (0 days, 1 day, ..., 28 days). The model, fitted to responses by 443 illicit drug users across four survey waves, included only survey wave and isolation status (in isolation or quarantine yes/no) as explanatory variables with subject_id as a random intercept. A simulated sample of 600 participants was generated by twice subsampling 300 subject_ids without replacement from the full set of 443. Most participants will have been selected in both subsamples. A single cannabis days-of-use was simulated for 2 subsamples x 300 subject_ids x 4 survey waves = 2400 28-day intervals. The cannabis days of use simulated response was generated by a single draw from the posterior predictive distribution for each subsample. The survey wave and isolation explanatory variables and subject_id are included in the supplied dataset. Survey participants are not identifiable.

OpenSprayer.com

Open Sprayer will hopefully be an open sourced autonomous land drone that will propel itself across the fields spraying weeds it can see with its mounted cameras. The project should involve a mix of mechanical engineering, classical software design and machine learning to achieve its goal. The project is meant to be a DIY effort to compete with the big companies like John Deere currently developing similar tech. The benefit of an open design is cheaper capital and maintenance cost. The ability to fix, update and repair your own sprayer would offer a great alternative to the potential high running costs of branded machines.

The data set includes pictures of broad leaved docks and picture of the land without broad leaved docks. I plan to update the images to better reflect the images that the sprayer drone will produce when operating. Give me feedback and I can take more pictures to improve the dataset.

Contrary to popular belief, people can become addicted to cannabis. Continued, frequent and heavy cannabis use can cause physical dependency and addiction.

This series measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions include age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including psychotherapeutics. Respondents were also asked about substance abuse treatment history, illegal activities, problems resulting from the use of drugs, personal and family income sources and amounts, need for treatment for drug or alcohol use, criminal record, and needle-sharing. Questions on mental health and access to care, which were introduced in the 1994-B questionnaire (see NATIONAL HOUSEHOLD SURVEY ON DRUG ABUSE, 1994), were retained in this administration of the survey. In 1996, the section on risk/availability of drugs was reintroduced, and sections on driving behavior and personal behavior were added (see NATIONAL HOUSEHOLD SURVEY ON DRUG ABUSE, 1996). The 1997 questionnaire continued the risk/availability section along with new items about the use of cigars, people present when respondents used marijuana or cocaine for the first time (if applicable), reasons for using these two drugs the first time, reasons for using these two drugs in the past year, reasons for discontinuing use of these two drugs (for lifetime but not past-year users), and reasons respondents never used these two drugs. In addition, a new series of questions asked only of respondents aged 12 to 17 was introduced. These items covered a variety of topics that may be associated with substance use and related behaviors, such as exposure to substance abuse prevention and education programs, gang involvement, relationship with parents, and substance use by friends. Demographic data include gender, race, age, ethnicity, marital status, educational level, job status, income level, veteran status, and current household composition.This study has 1 Data Set.

The aim of the CCS is to obtain detailed information about the habits of people who use cannabis and behaviours relative to cannabis use.

Dataset of all licensees as well as applicants who have obtained proximity protection for their proposed adult-use retail and medical dispensaries.

The aim of the CCS is to obtain detailed information about the habits of people who use cannabis and behaviours relative to cannabis use.

Response NHS Unlicenced Drugs We are not able to release the information requested regarding the prescribing of NHS Unlicenced Cannabis. This is due to the risk of patients being identified, if this information was used in combination with other information that may be in the public domain or reasonably available. This includes media coverage about cannabis prescribing and the small number of people receiving these drugs. This information falls under the exemption in section 40 subsections 2 and 3A (a) of the Freedom of Information Act. This is because it would breach the first data protection principle as: a) it is not fair to disclose patients’ personal details to the world and is likely to cause damage or distress. b) these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the patients. Please click the below web link to see the exemption in full. https://www.legislation.gov.uk/ukpga/2000/36/section/40

The Crops Suitability Tool combines soil types, aspect (slope orientation), and percentage of slope to determine the best and least suitable sites in which to grow crops in Loudoun County. It includes different types of Agricultural Soils (Prime Farmland, Secondary Cropland, Grassland Agriculture, Orchard Land, Woodland Use and Wildlife) and its grade of suitability for grapes, tree fruits, hops, vegetables, flowers, herbs, small fruits, field crops, pasture and hay.A spatial model uses existing geographic data to predict an outcome. In this application, we combined soil types, aspect (slope orientation), and percentage of slope to determine the best and least suitable site in which to grow crops in Loudoun County, Virginia. It includes different types of Agricultural Soils (Prime Farmland, Secondary Cropland, Grassland Agriculture, Orchard Land, Woodland Use and Wildlife) and its grade of suitability for grapes, tree fruits, hops, vegetables (ethnic crops), flowers, herbs, and small fruits, field crops, pasture, and hay.This tool does not account for the incidence and prevalence of any type of pests (weed, insects, and diseases -nematodes, fungi, bacteria, or viruses) or weather conditions that can affect crops. The accuracy of the predicted outcomes is not 100% (for example: 17B soils in a concave position are not suitable for growing perennial crops or high cash valued crops); therefore, it is highly recommended to contact VCE Loudoun Commercial Horticulturist Beth Sastre to get a soil map report of the property and/or to have a site evaluation for further recommendation.We encourage farmers, beginner farmers, people interested in farming, and realtors to use this tool to make guided decisions before starting a crop for the first time or buying land. If you see major discrepancies while using this tool, please report them.

This dataset presents a thorough analysis of the cannabis industry in 2025, highlighting key statistics and trends such as market growth rates, sales data, usage patterns, legalization progress, employment statistics, and the overall economic impact of the cannabis industry in the United States and globally.

Background: Existing cannabis treatment programs reach only a very limited proportion of people with cannabis-related problems. The aim of this systematic review and meta-analysis was to assess the effectiveness of digital interventions applied outside the health care system in reducing problematic cannabis use. Methods: We systematically searched the Cochrane Central Register of Controlled Trials (2015), PubMed (2009-2015), Medline (2009-2015), Google Scholar (2015) and article reference lists for potentially eligible studies. Randomized controlled trials examining the effects of internet- or computer-based interventions were assessed. Study effects were estimated by calculating effect sizes (ESs) using Cohen's d and Hedges' g bias-corrected ES. The primary outcome assessed was self-reported cannabis use, measured by a questionnaire. Results: Fifty-two studies were identified. Four studies (including 1,928 participants) met inclusion criteria. They combined brief motivational interventions and cognitive behavioral therapy delivered online. All studies were of good quality. The pooled mean difference (Δ = 4.07) and overall ES (0.11) give evidence of small effects at 3-month follow-up in favor of digital interventions. Conclusions: Digital interventions can help to successfully reduce problematic cannabis use outside clinical settings. They have some potential to overcome treatment barriers and increase accessibility for at-risk cannabis users.

BackgroundLittle is known about whether people who use both tobacco and cannabis (co-use) are more or less likely to have mental health disorders than single substance users or non-users. We aimed to examine associations between use of tobacco and/or cannabis with anxiety and depression.MethodsWe analyzed data from the COVID-19 Citizen Science Study, a digital cohort study, collected via online surveys during 2020–2022 from a convenience sample of 53,843 US adults (≥ 18 years old) nationwide. Past 30-day use of tobacco and cannabis was self-reported at baseline and categorized into four exclusive patterns: tobacco-only use, cannabis-only use, co-use of both substances, and non-use. Anxiety and depression were repeatedly measured in monthly surveys. To account for multiple assessments of mental health outcomes within a participant, we used Generalized Estimating Equations to examine associations between the patterns of tobacco and cannabis use with each outcome.ResultsIn the total sample (mean age 51.0 years old, 67.9% female), 4.9% reported tobacco-only use, 6.9% cannabis-only use, 1.6% co-use, and 86.6% non-use. Proportions of reporting anxiety and depression were highest for the co-use group (26.5% and 28.3%, respectively) and lowest for the non-use group (10.6% and 11.2%, respectively). Compared to non-use, the adjusted odds of mental health disorders were highest for co-use (Anxiety: OR = 1.89, 95%CI = 1.64–2.18; Depression: OR = 1.77, 95%CI = 1.46–2.16), followed by cannabis-only use, and tobacco-only use. Compared to tobacco-only use, co-use (OR = 1.35, 95%CI = 1.08–1.69) and cannabis-only use (OR = 1.17, 95%CI = 1.00–1.37) were associated with higher adjusted odds for anxiety, but not for depression. Daily use (vs. non-daily use) of cigarettes, e-cigarettes, and cannabis were associated with higher adjusted odds for anxiety and depression.ConclusionsUse of tobacco and/or cannabis, particularly co-use of both substances, were associated with poor mental health. Integrating mental health support with tobacco and cannabis cessation may address this co-morbidity.

In light of cannabis use being legalized in Canada for people over 18 years old (or 19 in some provinces and territories), understanding the associated risks of its use is critical to help Canadians make informed decision about their own health.