This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This dataset contains counts and rates (per 1,000,000 residents) of asthma deaths among Californians statewide and by county. The data are stratified by age group (all ages, 0-17, 18+) and reported for 3-year periods. The data are derived from the California Death Statistical Master Files, which contain information collected from death certificates. All deaths with asthma coded as the underlying cause of death (ICD-10 CM J45 or J46) are included.

A. SUMMARY This dataset includes data on a variety of substance use services funded by the San Francisco Department of Public Health (SFDPH). This dataset only includes Drug MediCal-certified residential treatment, withdrawal management, and methadone treatment. Other private non-Drug Medi-Cal treatment providers may operate in the city. Withdrawal management discharges are inclusive of anyone who left withdrawal management after admission and may include someone who left before completing withdrawal management.

This dataset also includes naloxone distribution from the SFDPH Behavioral Health Services Naloxone Clearinghouse and the SFDPH-funded Drug Overdose Prevention and Education program. Both programs distribute naloxone to various community-based organizations who then distribute naloxone to their program participants. Programs may also receive naloxone from other sources. Data from these other sources is not included in this dataset.

Finally, this dataset includes the number of clients on medications for opioid use disorder (MOUD).

The number of people who were treated with methadone at a Drug Medi-Cal certified Opioid Treatment Program (OTP) by year is populated by the San Francisco Department of Public Health (SFDPH) Behavioral Health Services Quality Management (BHSQM) program. OTPs in San Francisco are required to submit patient billing data in an electronic medical record system called Avatar. BHSQM calculates the number of people who received methadone annually based on Avatar data. Data only from Drug MediCal certified OTPs were included in this dataset.

The number of people who receive buprenorphine by year is populated from the Controlled Substance Utilization Review and Evaluation System (CURES), administered by the California Department of Justice. All licensed prescribers in California are required to document controlled substance prescriptions in CURES. The Center on Substance Use and Health calculates the total number of people who received a buprenorphine prescription annually based on CURES data. Formulations of buprenorphine that are prescribed only for pain management are excluded.

People may receive buprenorphine and methadone in the same year, so you cannot add the Buprenorphine Clients by Year, and Methadone Clients by Year data together to get the total number of unique people receiving medications for opioid use disorder.

For more information on where to find treatment in San Francisco, visit findtreatment-sf.org. 

B. HOW THE DATASET IS CREATED This dataset is created by copying the data into this dataset from the SFDPH Behavioral Health Services Quality Management Program, the California Controlled Substance Utilization Review and Evaluation System (CURES), and the Office of Overdose Prevention.

C. UPDATE PROCESS Residential Substance Use Treatment, Withdrawal Management, Methadone, and Naloxone data are updated quarterly with a 45-day delay. Buprenorphine data are updated quarterly and when the state makes this data available, usually at a 5-month delay.

D. HOW TO USE THIS DATASET Throughout the year this dataset may include partial year data for methadone and buprenorphine treatment. As both methadone and buprenorphine are used as long-term treatments for opioid use disorder, many people on treatment at the end of one calendar year will continue into the next. For this reason, doubling (methadone), or quadrupling (buprenorphine) partial year data will not accurately project year-end totals.

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Unintentional Overdose Death Rates by Race/Ethnicity Preliminary Unintentional Drug Overdose Deaths

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

This table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

High-Occupancy Vehicle (HOV) lane, also known as the carpool or diamond lane, is a traffic management strategy to promote and encourage ridesharing; thereby alleviating congestion and maximizing the people-carrying capacity of California highways.HOV lane is usually located on the inside (left) lane and is identified by signs along the freeway and white diamond symbols painted on the pavement. In Northern California, HOV lanes are only operational on Monday thru Friday during posted peak congestion hours, for example: between 6 a.m. - 10 a.m. and 3 p.m. - 7 p.m. All other vehicles may use the lanes during off-peak hours. This is referred to as "part-time" operation. In Southern California, HOV lanes are generally separated from other lanes by a buffer zone. The HOV lanes are in effect 24-hours a day, 7-days a week, referred to as "full-time" operation.The locations of the HOV system are based on postmiles derived from an excel spreadsheet maintained by Caltrans, Division of Traffic Operations, Office of System Management Operations.

The dataset titled "Overview of affordable housing indicators" is a comprehensive resource that provides insights into the affordability of housing across OECD member countries. The data spans from 2010 to 2020 and is updated annually. The dataset, published by the Organisation for Economic Co-operation and Development (OECD) on May 27, 2021, is available in PDF format and can be accessed openly. However, the OECD restricts the posting of its material on the internet, although linking, sharing, and embedding are permitted. The dataset does not contain data about individuals or identifiable individuals. The metadata for this dataset was created on November 15, 2023, and last modified on April 8, 2025. The dataset provides a range of economic indicators related to housing affordability, including house-price-to-income and housing-expenditure-to-income ratio measures. It also includes more data-intensive indicators such as residual income measures, which focus on the income households have left after paying for housing. The dataset is tagged with keywords such as Affordability, Affordable Housing, Economic Indicators, Expenditure, Housing Potential, Income, and Indicator. The dataset is owned by the OECD, and they can be contacted via telephone or fax for any queries. The dataset is available in English and the description of the dataset is provided. The dataset's source and location are provided, but the license is not specified.

These datasets focus on patients leaving California hospitals in 2019-2020 against medical advice (AMA), which is defined as choosing to leave the hospital before the treating physician recommends discharge. Patients leaving AMA are exposed to higher risks due to inadequately treated medical issues, which may result in the need for readmission.

Temporary residents who are in Canada on a study permit in the observed calendar year. Datasets include study permit holders by year in which permit(s) became effective or with a valid permit in a calendar year or on December 31st. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

Demographics (2006 and 2011 Census Data) This dataset contains three worksheets. The full description for each column of data is available in the first worksheet called "IndicatorMetaData". The data came from the 2006 and 2011 Census. Some of the data from the 2011 Census was not available at the time of publishing. Refer to the descriptions in worksheet 1 for more information. Users should note that the data for each neighbourhood are based on the mathematical aggregation of smaller sub-areas (in this case Census Tracts) that when combined, define the entire neighbourhood. Since smaller areas may have their values rounded or suppressed (to abide by Statistics Canada privacy standards), the overall total may be undercounted. Population Total (2016 Census Data) The data refers to Total Population from the 2016 Census, aggregated by the City of Toronto to the City's 140 Neighbourhood Planning Areas. Although Statistics Canada makes a great effort to count every person, in each Census a notable number of people are left out for a variety of reasons. For Census 2016: Population and Dwellings example, people may be travelling, some dwellings are hard to find, and some people simply refuse to participate. Statistics Canada takes this into account and for each Census estimates a net 'undercoverage' rate for the urban region, the Toronto Census Metropolitan Area (CMA), but not for the city. The 2011 rate for the Toronto CMA was 3.72% plus or minus 0.53%. The 2016 rate is not yet available

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

This table provides quarterly estimates of the number of non-permanent residents by type for Canada, provinces and territories.

Under the direction of University College London (UCL), this international, multidisciplinary project assessed the feasibility of using non-destructive digital imaging technology to make texts visible in images of papyrus in Ancient Egyptian mummy case cartonnages for open research and analysis. Our pilot project has led to an understanding of which imaging modalities are worth pursuing in future research projects. The massive finding of papyri in Egypt between the end of the 19th and the beginning of the 20th century has dramatically increased our knowledge of the ancient world. The recovering of new texts has brought to light classical and biblical literature, and everyday writing of people that have changed the way we interpret antiquity. Papyri were and still are found in two main ways: in situ, i.e. where they were left by the ancients, or recycled for fabricating other objects such as mummy masks and coverings, book binding and other kinds of what scholars broadly define as 'cartonnage.' Papyri were also used sometimes to stuffing animal mummies. In the past, the awareness that such ancient objects could be filled with manuscripts has led papyrologists to destroy cartonnage, mummy masks and other material for retrieving their contents. With the passing of decades, specialists' recognition of the problems connected with such practice has increased, and new, less invasive techniques have been developed in order to avoid the destruction of important historical evidence. The decision to eventually dismount cartonnage involves careful evaluations of the pros and cons and of the methods to be followed. Besides papyrologists, conservators and other specialists, the practice of dissolving cartonnage in order to retrieve papyri has been employed by dealers and collectors seeing the opportunity to multiply their earnings or simply looking for manuscripts without recognizing the issues involved with the destruction of ancient artefacts. In these cases, the damage produced to our cultural heritage is even greater since little if any attention to the methods employed and to the recording of the process is paid. The application of advanced imaging techniques has the potential to dramatically improve our study of papyri encapsulated in ancient artefacts and will potentially solve the problem of invasive, destructive approaches to the remains of our ancient past. This exploratory, pilot project, working with a range of international partners and collections between November 2015 and December 2017, tested the feasibility of non-destructive imaging of multi-layered Papyrus found in Egyptian mummy cartonnages. Our research has shown that no current single imaging technique can identify both iron and carbon based inks at depths within cartonnage. If we are to detect and ultimately read text within cartonnage, a multimodal imaging approach is required, but this will necessarily be limited by cost, access to imaging systems, and the portability of both the system and the cartonnage. We are currently in the process of publishing lessons-learned on findings and imaging methodologies for further research, including on affordances and limitations of specific imaging approaches, and how they can be used in tandem to recover extant text within layers of cartonnage. This data is hosted by UCL Research Data Repository for public access and use. All images are licensed for use under Creative Commons 0 1.0 Universal License.

This data set comprises a core content set of digital images, analytical data and technical reports on the imaging and analysis of mummy mask cartonnage and modern surrogates. These are intended for access by researchers, scholars, students and the general public. The data set contains the following folders organized by imaging method:

Documentation.7z contains documentation, metadata, photographs and reports for each modality (151MB). Data_FiberOpticReflectanceSpectroscopy.7z is Fiber Optic Reflectance Spectroscopy Data from testing conducted by Equipoise Imaging (30MB) Data_OpticalCoherenceTomography.7z is Optical Coherence Tomography Data from imaging conducted in the Duke University Eye Center and Department of Biomedical Engineering. (619MB) Data_Terahertz.7z is Terahertz Data from experimental imaging at the University of Western Australia (1MB) Data_Xray.7z contains XRF data from the SLAC Stanford Synchrotron Radiation Lightsource in California and "Micro-CT ALS Berkeley" data from the Lawrence Livermore National Laboratory Advanced Light Source in California. (21.3GB). ImageData_RBT.7z - Multispectral imaging data from RB Toth Associates at Duke University and University of California at Berkeley, with processed images of US and UCL images. (31 GB.) UCBsn_LC.7z - Data from multispectral imaging at the University of California at Berkeley s.n. cartonnage fragment by the Library of Congress before and after x-ray of the fragment for damage assessment (2.1GB) UCL_Digital_Humanities.7z - Data from multispectral imaging of the UCL Phantom surrogates and Petrie Museum cartonnage UC806037i in the UCL Centre for Digital Humanities, London. (22.6GB) UManchester_JohnRylands: Data from multispectral imaging of both sides of cartonnage Greek P458 P458 at the University of Manchester John Rylands Library. (5.5GB)

README files with more specific information are included with the data set from each imaging modality. This data was first shared online in July 2017. It was moved to its current location and assigned a doi in November 2022.

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter

https://doi.org/10.5061/dryad.qz612jmjt

Description of the data and file structure

Data description:

Annual spatial estimates of above ground live, standing dead, litter, and below ground biomass (g/m2) for 2001-2023 for southern California.

These raster layers were created by modeling field plot biomass to covariates, including precipitation, remotely sensed NDVI, and geophysical (slope, aspect, elevation) data.

For a more complete description, visit https://doi.org/10.5061/dryad.qz612jmjt

The biomass raster layers are packaged in zip files for each year using the following naming structure:

WWETAC_UCD_SoCal_Biomass_XXXX.zip

Where XXXX is the year of the biomass estimates. Within each zip file are the following files:

WWETAC_UCD_

Where

Data includes public and Catholic schools and school authorities. Private schools and publicly funded hospital and provincial schools and care, treatment and correctional facilities are not included.

Includes:

• board number
• board name
• school name
• school number
• school level
• school type
• school language
• enrolment

Source: As reported by schools in Ontario School Information System (OnSIS), October Submissions.

Boards update and maintain "/dataset/ontario-public-school-contact-information">contact information related to schools and boards of education.

Small cells have been suppressed:

• where fewer than 10 students are in a given category, the data is depicted with (10)
• the report may not be used in any way that could lead to the identification of an individual.

Note:

• Starting 2018-2019, enrolment numbers have been rounded to the nearest five.
• Where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals.
• Includes Temporary Remote Learning Schools (TRLS)

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

Demographics (2006 and 2011 Census Data) This dataset contains three worksheets. The full description for each column of data is available in the first worksheet called "IndicatorMetaData". The data came from the 2006 and 2011 Census. Some of the data from the 2011 Census was not available at the time of publishing. Refer to the descriptions in worksheet 1 for more information. Users should note that the data for each neighbourhood are based on the mathematical aggregation of smaller sub-areas (in this case Census Tracts) that when combined, define the entire neighbourhood. Since smaller areas may have their values rounded or suppressed (to abide by Statistics Canada privacy standards), the overall total may be undercounted. Population Total (2016 Census Data) The data refers to Total Population from the 2016 Census, aggregated by the City of Toronto to the City's 140 Neighbourhood Planning Areas. Although Statistics Canada makes a great effort to count every person, in each Census a notable number of people are left out for a variety of reasons. For Census 2016: Population and Dwellings example, people may be travelling, some dwellings are hard to find, and some people simply refuse to participate. Statistics Canada takes this into account and for each Census estimates a net 'undercoverage' rate for the urban region, the Toronto Census Metropolitan Area (CMA), but not for the city. The 2011 rate for the Toronto CMA was 3.72% plus or minus 0.53%. The 2016 rate is not yet available