Research has shed light on the employment barriers faced by individuals with disabilities, and by racialized people. The challenges faced by people belonging to both marginalized groups are less well-understood. The purpose of this scoping review was to examine existing research on labour market and workplace experiences of racialized people with disabilities, and to identify how ableism and racism intersect to shape employment experiences and outcomes. Seven international databases were searched, covering the period from 2000 to April 2022. Four reviewers independently conducted the screening, and data extraction and analysis were performed on 44 articles that met our inclusion criteria. The findings highlighted rates of workplace ableism and racism (including discrimination allegations and perceived discrimination); types and forms of experiences arising from the intersection of ableism and racism (including unique individual stereotyping and systemic and institutional discrimination); and the role of other demographic variables. The intersection of ableism and racism impacted labour market outcomes, well-being in the workplace, and career/professional advancement. Our review highlights the need for greater in-depth research focusing explicitly on the intersection of ableism and racism (and of other forms of discrimination), to better understand and address the barriers that racialized people with disabilities face in employment.IMPLICATIONS FOR REHABILITATIONThe experiences of racialized people with disabilities have been under explored, and clinicians and rehabilitation specialists should consider incorporating intersectionality into their practices to better understand and serve these populations.Ableism and racism do not operate in isolation, and clinicians and other professionals need to be aware that racialized people with disabilities may face unique challenges and barriers as a result.Service providers should aim to address gaps and inequities in services faced by racialized people with disabilities which may prevent them from finding and/or maintaining meaningful employment. The experiences of racialized people with disabilities have been under explored, and clinicians and rehabilitation specialists should consider incorporating intersectionality into their practices to better understand and serve these populations. Ableism and racism do not operate in isolation, and clinicians and other professionals need to be aware that racialized people with disabilities may face unique challenges and barriers as a result. Service providers should aim to address gaps and inequities in services faced by racialized people with disabilities which may prevent them from finding and/or maintaining meaningful employment.

About this dataset

Context

The dataset tabulates the population of Person County by race. It includes the population of Person County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Person County across relevant racial categories.

Key observations

The percent distribution of Person County population by race (across all racial categories recognized by the U.S. Census Bureau): 65.68% are white, 25.47% are Black or African American, 0.59% are American Indian and Alaska Native, 0.43% are Asian, 0.01% are Native Hawaiian and other Pacific Islander, 1.40% are some other race and 6.43% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Person County
• Population: The population of the racial category (excluding ethnicity) in the Person County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Person County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Person County Population by Race & Ethnicity. You can refer the same here

23,110 People Multi-race and Multi-pose Face Images Data. This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks.

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, and gender. This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives. The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity are separate files but are now combined. Information updated as of 7/15/2024.

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

In 2023, around 48 percent of the Black people interviewed in the United States thought transgender people face a great deal of discrimination. In comparison, the share of Hispanic and white people who shared this view was about 45 and 40 percent, respectively.

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Person County by race. It includes the distribution of the Non-Hispanic population of Person County across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Person County across relevant racial categories.

Key observations

Of the Non-Hispanic population in Person County, the largest racial group is White alone with a population of 25,433 (68.79% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Person County
• Population: The population of the racial category (for Non-Hispanic) in the Person County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Person County total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Person County Population by Race & Ethnicity. You can refer the same here

Definition Of Human Race

## Overview

Definition Of Human Race is a dataset for object detection tasks - it contains Human Race annotations for 3,150 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Citywide/6859-spec. COVID-19 vaccinations administered to Chicago residents based on the reported race-ethnicity and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ··People with an original booster dose: Number of people who have a completed vaccine series and have received at least one additional monovalent dose. This includes people who received a monovalent booster dose and immunocompromised people who received an additional primary dose of COVID-19 vaccine. Monovalent doses were created from the original strain of the virus that causes COVID-19. People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group. Note that each age group has a row where race-ethnicity is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2019 1-year estimates. For some of the age groups by which COVID-19 vaccine has been authorized in the United States, race-ethnicity distributions were specifically reported in the ACS estimates. For others, race-ethnicity distributions were estimated by the Chicago Department of Public Health (CDPH) by weighting the available race-ethnicity distributions, using proportions of constituent age groups. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity) who have each vaccination status as of the date, divided by the estimated number of Chicago residents in each subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Data reported in I-CARE only include doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that c

I applied bits of text mining, natural langauge processing, and data science to a pair of annual editions of Race and Ethnic Relations, and below is a summary of what I learned.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco. The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person. The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons: • The person was not asked about their race and ethnicity. • The person was asked, but refused to answer. • The person answered, but the testing provider did not include the person's answers in the reports. • The testing provider reported the person's answers in a format that could not be used by the health department. For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.” B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown." The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.” If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value i

Two parallel processes structure American politics in the current moment: partisan polarization and the increasing linkage between racial attitudes and issue preferences of all sorts. We develop a novel theory that roots these two trends in historical changes in party coalitions. Changing racial compositions of the two major parties led to the formation of racialized images about Democrats and Republicans in people’s minds—and these images now structure Americans’ partisan loyalties and policy preferences. We test this theory in three empirical studies. First, using the American National Election Studies we trace the growing racial gap in party coalitions as well as the increasing overlap between racial and partisan affect. Then, in two original survey studies we directly measure race–party schemas and explore their political consequences. We demonstrate that race–party schemas are linked to partisan affect and issue preferences—with clear implications for the recent developments in U.S. politics.

1,995 People Face Images Data (Asian race). For each subject, more than 20 images per person with frontal face were collected. This data can be used for face recognition and other tasks.

NOTE: As of 2/16/2023 this table is no longer being updated. For information on COVID-19 Updated (Bivalent) Booster Coverage, go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Race/8267-bg4w. Important change as of June 1, 2022 As of June 1, 2022, we will be using 2020 DPH provisional census estimates* to calculate vaccine coverage percentages by age at the state level. 2020 estimates will replace the 2019 estimates that have been used. Caution should be taken when making comparisons of percentages calculated using the 2019 and 2020 census estimates since observed difference may result from the shift in the denominator. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator. DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020 State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT. This table shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 by race / ethnicity and age group. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator. Population size estimates are based on 2019 DPH census estimates until 5/26/2022. From 6/1/2022, 2020 DPH provisional census estimates are used. In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose. A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple races. A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities sug

1. Specifications Product : Biometric Data

Data size : 200,000 ID

Race distribution : black people, Caucasian people, brown(Mexican) people, Indian people and Asian people

Gender distribution : gender balance

Age distribution : young, midlife and senior

Collecting environment : including indoor and outdoor scenes

Data diversity : different face poses, races, ages, light conditions and scenes Device : cellphone

Data format : .jpg/png

Accuracy : the accuracy of labels of face pose, race, gender and age are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go Biometric Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

4,484 people multi-race – infrared face recognition data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The race distribution includes Asian, Black, Caucasian and Brown people. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is DV-DH4,044S305AD. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.