The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

This dataset includes all personal names listed in the Wikipedia category “American people by ethnic or national origin” and all subcategories fitting the pattern “American People of [ ] descent”, in total more than 25,000 individuals. Each individual is represented by a row, with columns indicating binary membership (0/1) in each ethnic/national category.

Ethnicity inference is an essential tool for identifying disparities in public health and social sciences. Existing datasets linking personal names to ethnic or national origin often neglect to recognize multi-ethnic or multi-national identities. Furthermore, existing datasets use coarse classification schemes (e.g. classifying both Indian and Japanese people as “Asian”) that may not be suitable for many research questions. This dataset remedies these problems by including both very fine-grain ethnic/national categories (e.g. Afghan-Jewish) and more broad ones (e.g. European). Users can chose the categories that are relevant to their research. Since many Americans on Wikipedia are associated with multiple overlapping or distinct ethnicities/nationalities, these multi-ethnic associations are also reflected in the data.

Data were obtained from the Wikipedia API and reviewed manually to remove stage names, pen names, mononyms, first initials (when full names are available on Wikipedia), nicknames, honorific titles, and pages that correspond to a group or event rather than an individual.

This dataset was designed for use in training classification algorithms, but may also be independently interesting inasmuch as it is a representative sample of Americans who are famous enough to have their own Wikipedia page, along with detailed information on their ethnic/national origins.

DISCLAIMER: Due to the incomplete nature of Wikipedia, data may not properly reflect all ethnic national associations for any given individual. For example, there is no guarantee that a given Cuban Jewish person will be listed in both the “American People of Cuban descent” and the “American People of Jewish descent” categories.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEthnicityThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.Definition: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.Respondents could choose one out of 19 tick-box response categories, including write-in response options.This dataset includes data relating to Leicester City and England overall.

Example of assigning ethnic class using Ethnicity Estimator.

This data set consists of 17 variables that underpin the analysis of the paper entitled Exploring intergenerational, intra-generational and transnational patterns of family caring in minority ethnic communities: the example of England and Wales published in the International Journal of Care and Caring.

The methodology for the survey is described in the paper.

This data set is no longer being updated and is historical, last update 10/10/2022.Provides the percentage of COVID-19 cases by race/ethnicity in Jefferson County, KY. In addition, percentage of Jefferson county vaccine recipients broken out by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. Fieldname Definition race description of race/ethnicity CensusCountPCT percentage of population make-up of Jefferson county ConfirmedCaseCountPCT percentage of confirmed cases by race/ethnicity (rounded to the whole percent) DeceasedCountPCT percentage of deceased cases by race/ethnicity (rounded to the whole percent) RecoveredCountPCT percentage of recovered cases by race/ethnicity (rounded to the whole percent) VaccinatedCountPCT percentage of Jefferson county vaccine recipients by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. (rounded to the whole percent) Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to change

For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

CalEnviroScreen scores represent a combined measure of pollution and the potential vulnerability of a population to the effects of pollution. Like the previous versions, CalEnviroScreen 4.0 does not include indicators of race/ethnicity or age. However, the distribution of the CalEnviroScreen 4.0 cumulative impact scores by race or ethnicity is important. This information can be used to better understand issues related to environmental justice and racial equity in California. CalEPAs racial equity team has released a StoryMap using CalEnviroScreen 3.0 data that examines the connection between racist land use practices of the 1930s and the persistence of environmental injustice. The CalEPA StoryMap, along with this analysis, are examples of information that can be used to better understand issues related to environmental justice and racial equity in California.

This data set is no longer being updated and is historical, last update 10/10/2022.Summary level data on vaccine distributions given to Jefferson County, KY residents by ethnicity, excluding data provided by WALGREENS and CVS.Fieldname Definition ethnicity self identified ethnicity of vaccinated recipient total_vaccinated Number of vaccinated Jefferson County residents ethnicity_vaccinated Number of vaccinated Jefferson County residents by ethnicity ethnicity_vaccinated_percentage Percentage of Number of vaccinated Jefferson County residents by ethnicity /Number of vaccinated Jefferson County residents BaseLinePct Number of Jefferson County residents by ethnicity/Number of Jefferson County residents Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to changeFor questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

Transportation to Work by Race/Ethnicity

Risk Factors and Inequalities in California

By Health [source]

About this dataset

This table contains important data on the mode of transportation used by California residents aged 16 years and older. This information is sourced from the U.S. Census Bureau Decennial Census and American Community Survey and given as part of a series of indicators as part of the Healthy Communities Data and Indicators Project created by the Office of Health Equity.

Commuting to work makes up a large portion - 19% -of overall travel miles in the United States, with automobiles being overwhelmingly preferred by commuters over other methods like walking or biking. Automobiles show an impressive level of personal mobility, however they are associated with certain hazards such as air pollution, car crashes, pedestrian injuries, sedentary lifestyles linked to stress-related health problems and more. Alternatives such as walking alone or combined with public transport offer physical activity which has been linked to lower rates for diseases like heart disease, stroke, diabetes colon cancer breast cancer dementia depression etc., however these forms do come with their own risks; urban areas especially feature higher collision risks seeking pedestrians due to increased vehicle density while bus/rail passengers face less risk than motorcyclists pedestrians or bicyclists.

But this isn't just any average statistic; certain disadvantaged minority communities bear a disproportionate share when it comes to pedestrian-car fatalities: Native American males have an astonishingly 4 times higher death rate compared to Whites or Asians whereas African-Americans & Latinos face double risk than their respective counterparts; factors like stereotypes regarding race based driving behavior can be partially responsible for this discrepancy further marching for more research into this area our part towards embracing greater equality for all races/ethnicities . As such this data acquired from HealthData & CHHS Open Data is presented in hopes that greater awareness can be generated on current situation leading ultimately towards improving safety & providing better mobility options uniformly across all communities

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How to use the dataset

This dataset contains information on the mode of transportation to work for California residents aged 16 and older by race/ethnicity. It provides an excellent opportunity to compare commute data across different regions, counties, geographies, and ethnicities. This dataset can be used in many ways and can give insights into how different communities utilize different modes of transportation.

To get started using this dataset, begin by filtering the data to narrow down the criteria you are looking for (e.g., region_code or county_fips). Once you have narrowed down your selection of data points, you can use a variety of visualizations to gain insights into population segments who use various means of transport. For example, you could create charts such as bar graphs, line graphs or pie charts that display population patterns across year groups within a given area or particular demographic groupings (race/ethnicity). Additionally, this information could be used for public policy related applications such as informing zones about allocating resources to increase accessibility or safety related concerns with certain modes etc.

By examining this dataset further it is also possible to make comparative analyses between several years which may shed light on social trends over time in regards to commuting behaviors which could potentially reveal potential opportunities when planning infrastructure projects or commuter-friendly services such as ridesharing groups etc., through identifying current commuting gaps in given areas relative two other nearby regions based on mode usage shifts throughout various timespans within the years included in this dataset's range (2000-2010).

In conclusion; whether studying historical trends or analyzing present activity –this Transportation To Work 2000-2006-2010 Dataset holds invaluable insight on travel trends among California’s populous providing great potential for expansive research endeavors as well as guiding decision makers from city councils toward more effective policies & projects delivering positive community impact & productivity benefits

Research Ideas

• Investigating the relationship between mode of transportation and health among different racial/ethnic groups in California and also comparisons across regions.
• ...

Dataset contains ethnic group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the ethnic group population count between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

The ethnic groups are:

• European
• Māori
• Pacific peoples
• Asian
• Middle Eastern/Latin American/African
• Other ethnicity.

Map shows percentage change in the census usually resident population count for ethnic groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Ethnicity concept quality rating

Ethnicity is rated as high quality.

Ethnicity – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-998 Not applicable

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEthnicityThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.Definition: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.Respondents could choose one out of 19 tick-box response categories, including write-in response options.This dataset includes data relating to the Medium Super Output Areas (MSOAs) of Leicester City.

Modifications since Superintendent's Informational memorandum number 182, dated December 4, 1998: Dinwiddie County -- redistribution of Ungraded students. (Dec. 10, 1998) Background: Each year, the Virginia Department of Education collects statistics on the number of students enrolled in public school on September 30th. This report is submitted by each school in Virginia which officially enrolls students (i.e. student records are maintained on a Virginia Teacher's Register or automated system). Student counts are reported by grade assignment and ethnicity. Excluded from the September 30 count are Special education preschool pupils, pupils in hospitals, clinics or detention homes, and local programs such as vocational and alternative education centers (i.e. centers or schools which receive, but do not officially enroll students). Definitions: The five Federal racial/ethnic categories are as follows: American Indian or Alaskan native - a person having origins (ancestry) in any of the original peoples of North America, who maintains cultural identification through tribal affiliation or community recognition. Asian or Pacific Islander - a person having origins (ancestry) in any of the original peoples of the Far East, Southeast Asian, the Pacific Islands, or the Indian subcontinent. Included, for example, are peoples of China, Korea, the Philippine Islands, Samoa, and India.

Black, not of Hispanic origin - a person having origins (ancestry) in any of the black racial groups of Africa.

Hispanic- a person of Mexican, Puerto Rican, Cuban, Central or South American, or other Spanish culture or origin (ancestry), regardless of race.

White, not of Hispanic origin - a person have origins (ancestry) in any of the original peoples of Europe, North Africa, or the Middle East.

The following abbreviations are used in the 1998 Fall Membership reports: PK - Pre-Kindergarten, KG - Kindergarten, PG - Post Graduate, UG - Ungraded.

Description

Title:

Facial Attributes Dataset of Bangladeshi Tribes for Ethnic Classification Research

Description:

This dataset comprises facial attribute data collected from individuals belonging to two indigenous tribal communities in Bangladesh GARO and CHAKMA. It aims to facilitate research in ethnicity classification using machine learning (ML) and deep learning (DL) techniques. The dataset is presented in CSV format and includes detailed facial attribute annotations to support diverse AI-driven tasks, including classification, clustering, and recognition.

Key Features:

1. Data Content: Facial attribute information (For example: skin tone, facial shape, eye features, etc.) encoded numerically and categorically for ML and DL applications.
2. Ethnic Diversity: Includes samples from multiple Bangladeshi tribal groups to ensure ethnic diversity and representation.
3. Purpose: Designed for applications in ethnicity classification, social anthropology research, and AI ethics.
   Format: CSV file (.csv)

Institutions

Daffodil International University

Categories

Machine Learning, Ethnicity, Deep Learning, Binary Classification

The data is prepared using AmeriCorps members who began service on any day in fiscal year (FY) 2017. The members may have served 1 to 365 days during their term. Members who are in never served, disqualified, pre-service, or deferred statuses were excluded from this analysis. AmeriCorps VISTA and AmeriCorps NCCC race and ethnicity data come from the member application to serve. The code to extract the data between the two programs is the same. The ASN race and ethnicity data comes from the enrollment form. The enrollment form may exist multiple times if the member enrolled in more than one term. It is not uncommon for each enrollment form to have conflicting information about the member’s race and ethnicity. The member may have enrollment form data for terms served outside of the timeframe of the dataset. For example, if we are reporting on members who began service in FY17, then a member who also served in FY16 may have race and ethnicity information in the FY16 enrollment form and no race or ethnicity information or conflicting information in the FY17 enrollment form. In the case of conflicting information, this analysis assumes each instance of race designation is correct. If a member reports themselves as “Asian or Asian American” in one enrollment form and “White” in another enrollment form, then the analysis categorizes this person as someone who identifies with multiple race selections vs. one or the other. In the case of ethnicity, if a member indicates that they are not Hispanic or Latino/a in one form, but that they are in another, this analysis assumes the affirmative—and they will be categorized as Hispanic or Latino/a. Lastly, the totals include the total results from the query plus the difference between the query and the raw count of members who started service in that fiscal year. The members who did not have a record in the invite table and enrollment table were added to the non-response category. Senior Corps Figures come from the Annual Progress Report Supplement as of April 11, 2018. Percentages are calculated from totals of the subcategories, excluding the non-response categories.

OSPI school year 2024 (2023-24) detailed student ethnicity data.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Background: Each year, the Virginia Department of Education collects statistics on the number of students enrolled in public school on September 30th. This report is submitted by each school in Virginia which officially enrolls students (i.e. student records are maintained on a Virginia Teacher's Register or automated system). Student counts are reported by grade assignment and ethnicity. Excluded from the September 30th count are Special education preschool pupils, Head Start pupils, and pupils in hospitals, clinics or detention homes, and local programs such as vocational and alternative education centers (i.e. centers or schools which receive, but do not officially enroll students).

Definitions: The five Federal racial/ethnic categories are as follows: American Indian or Alaskan native - a person having origins (ancestry) in any of the original peoples of North America, who maintains cultural identification through tribal affiliation or community recognition. Asian or Pacific Islander - a person having origins (ancestry) in any of the original peoples of the Far East, Southeast Asian, the Pacific Islands, or the Indian subcontinent. Included, for example, are peoples of China, Korea, the Philippine Islands, Samoa, and India. Black, not of Hispanic origin - a person having origins (ancestry) in any of the black racial groups of Africa. Hispanic - a person of Mexican, Puerto Rican, Cuban, Central or South American, or other Spanish culture or origin (ancestry), regardless of race. White, not of Hispanic origin - a person have origins (ancestry) in any of the original peoples of Europe, North Africa, or the Middle East. The following abbreviations are used in the 1995 Fall Membership reports: PK - Pre-Kindergarten (non Federally funded), KG - Kindergarten, PG - Post Graduate, UG - Ungraded.

These data are modelled using the OMOP Common Data Model v5.3.Correlated Data SourceNG tube vocabulariesGeneration RulesThe patient’s age should be between 18 and 100 at the moment of the visit.Ethnicity data is using 2021 census data in England and Wales (Census in England and Wales 2021) .Gender is equally distributed between Male and Female (50% each).Every person in the record has a link in procedure_occurrence with the concept “Checking the position of nasogastric tube using X-ray”2% of person records have a link in procedure_occurrence with the concept of “Plain chest X-ray”60% of visit_occurrence has visit concept “Inpatient Visit”, while 40% have “Emergency Room Visit”NotesVersion 0Generated by man-made rule/story generatorStructural correct, all tables linked with the relationshipWe used national ethnicity data to generate a realistic distribution (see below)2011 Race Census figure in England and WalesEthnic Group : Population(%)Asian or Asian British: Bangladeshi - 1.1Asian or Asian British: Chinese - 0.7Asian or Asian British: Indian - 3.1Asian or Asian British: Pakistani - 2.7Asian or Asian British: any other Asian background -1.6Black or African or Caribbean or Black British: African - 2.5Black or African or Caribbean or Black British: Caribbean - 1Black or African or Caribbean or Black British: other Black or African or Caribbean background - 0.5Mixed multiple ethnic groups: White and Asian - 0.8Mixed multiple ethnic groups: White and Black African - 0.4Mixed multiple ethnic groups: White and Black Caribbean - 0.9Mixed multiple ethnic groups: any other Mixed or multiple ethnic background - 0.8White: English or Welsh or Scottish or Northern Irish or British - 74.4White: Irish - 0.9White: Gypsy or Irish Traveller - 0.1White: any other White background - 6.4Other ethnic group: any other ethnic group - 1.6Other ethnic group: Arab - 0.6