A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers need pre-tested names with data on perceptions of these attributes. Such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

Anti-Black or African American attacks were the most common form of racist hate crime in the United States in 2023, with 3,027 cases. Anti-White hate crimes were the next most common form of race-based hate crime in that year, with 831 incidents.

Prepared by Vetle Torvik 2018-04-15 The dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed. • How was the dataset created? First and last names of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including Ethnea+Genni as described in: Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA. http://hdl.handle.net/2142/88927 Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720 EthnicSeer: http://singularity.ist.psu.edu/ethnicity Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada SexMachine 0.1.1: https://pypi.org/project/SexMachine First names, for some Author-ity records lacking them, were harvested from outside bibliographic databases. • The code and back-end data is periodically updated and made available for query at Torvik Research Group • What is the format of the dataset? The dataset contains 9,300,182 rows and 10 columns 1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition) 2. name: full name used as input to EthnicSeer) 3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX 4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction 5. lastname: used as input for Ethnea+Genni 6. firstname: used as input for Ethnea+Genni 7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short) 8. Genni: predicted gender; 'F', 'M', or '-' 9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male) 10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'

Race Demographics in the 2010 CensusThis feature layer, utilizing data from the U.S. Census Bureau (USCB), contains demographics about race and ethnicity in the 2010 U.S. Census. The data is provided for state, county, tract, and block group geographies. These attributes cover topics such as the count of population, householder information, and family type by race/ethnicity.Per the Census, "Also known as the Population and Housing Census, the Decennial U.S. Census is designed to count every resident in the United States. It is mandated by Article I, Section 2 of the Constitution and takes place every 10 years. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute hundreds of billions of dollars in federal funds to local communities."There are four layers: state, county, census tract, and census block group. Each layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map has increasing detail as you zoom in to smaller areas. Only one geography is in view at any time.Race Demographics in the 2010 CensusData currency: 2010For more information: Race and Ethnicity in the United States: 2010 Census and 2020 CensusFor feedback please contact: ArcGIScomNationalMaps@esri.comData Processing notes:State and county boundaries are simplified representations offered from the Census Bureau's 2010 MAF/TIGER databaseTract and block group boundaries are 2010 TIGER boundaries with select water area boundaries erased (coastlines and major water bodies)Field names and aliases are processed by Esri as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.U.S. Census BureauPer USCB, "the Census Bureau is the federal government’s largest statistical agency. We are dedicated to providing current facts and figures about America’s people, places, and economy. Federal law protects the confidentiality of all the information the Census Bureau collects."

An emerging body of research has robustly found a link between immigration and preferences for redistribution. In particular, immigration has been proved to undermine native support for the welfare state (Alesina et al., 2022; Dahlberg et al., 2012; Eger, 2010; Ford, 2006; Stichnoth, 2012). This link can be interpreted within a rational resource-competition framework (since increasing the number of potential net recipients has welfare consequences). Yet different social identity theories (Hornsey, 2008; Tajfel & Turner, 1986; Turner et al., 1987) have also been invoked to stress that natives’ attitudes towards redistribution and the welfare state are grounded on ethnic/national identity as well as on related conceptions of cultural distance (Brandt et al. 2014; Chambers et al. 2013). Perceptions of cultural distance, in turn, make the overall effect of immigration on attitudes towards redistribution dependent on the characteristics of the immigrant pool —because some immigrant groups are perceived as more (or less) disserving than others (Verkuyten et al. 1996). In the European context, immigrants’ coming from Middle East and North African countries of majoritarian Muslim faith (MENAM) and their European-born descendants are known to be particularly at risk of discrimination and prejudice ( Strabac & Listhaug 2008; Strabac et al. 2014; Di Stasio et al. 2021; Polavieja et al. 2023). An important gap in the literature on redistribution and welfare nativism, however, concerns the potential role of immigrant characteristics other than cultural-religious or socioeconomic background, specifically, the role of phenotype (i.e. color or racial appearance). The importance of racial appearance as an additional source of prejudice and discrimination has been long neglected in the European context and, to our knowledge, to date no study on the impact of immigration on attitudes towards redistribution has tested whether immigrants’ physical (“racial”) appearance can influence European natives’ attitudes toward welfare deservedness. Recent field experimental research on racial discrimination in hiring has brought the question of racial discrimination to the fore by showing European employers are less likely to hire immigrant descendants with non-white phenotypes and, hence, that having “visible” phenotypes constitutes a serious barrier for the socio-economic integration of the second-generation in Europe (Polavieja et al. 2023). Building on this research, we propose an experiment to address the distinctive role of ethnicity (treatment 1) and phenotype (treatment 2) on native’s attitudes regarding welfare deservedness chauvinism (research question 1). Additionally, we test for two mediating mechanisms, welfare competition (research question 2) and disgust sensitivity (research question 3), which allows us to also contribute to the expanding literature on the neurocognitive basis of prejudice and the role of visceral emotions. To this end we draw on recent developments in cognitive psychology, political psychology and behavioral science. Our main research questions can thus be summarized as follows: Research Question 1: What is the distinctive role of immigrant-descendants’ ethnicity and phenotype as potentially different drivers of welfare chauvinism? Research Question 2: To what extent (rational) concerns about competition might help us explain welfare chauvinist responses amongst natives? Research Question 3: To what extent (irrational) disgust sensitivity can help us explain welfare chauvinism and, in particular, chauvinist responses triggered by phenotypic racism?

This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

In 2016, around 15.2 percent of all White families in the United States had a net worth of one million U.S. dollars or more. This compares to only 1.9 percent of Black families.

The LDOES project investigated the dynamics of changing neighbourhood populations over two decades in Scotland. The project has substantive links with two other ESRC projects: AQMeN II Urban Segmentation (PI: Susan McVie, Edinburgh) and Dynamics of Ethnic Identity & Inequality (PI: James Nazroo, Manchester). The project identified a lack of available information on ethnic migration dynamics in inter-census years. The Registers of Scotland (RoS) property transactions data was used to address this deficit. The RoS data captures each and every property transaction in Scotland between 1990 and 2014 as well as the names of buyers and sellers. Additional work was done by the AQMeN team to impute the ethnicity and religion of buyers using the name-classification software Onomap. This deposit contains tables for annual ethnic and religious inflows into an area based on the names of property buyers. The aggregation is at the level of 2001 Scottish Datazones (each unit covers between 500 – 1000 residents). The Applied Quantitative Methods Network (AQMeN) Phase II is a Research Centre that aims to develop a dynamic and pioneering set of projects to improve our understanding of current social issues in the UK and provide policy makers and practitioners with the evidence to build a better future. Three principal cross-cutting research strands will exploit existing high-quality data resources: (1) Education and Social Stratification will focus on social class differences in entry to, progression in and attainment at tertiary education and how they affect individuals' labour market outcomes and their civic participation; (2) Crime and Victimisation will explore the dramatic change in crime rates in Scotland and other jurisdictions and examines the determinants and impact of criminal careers amongst populations of offenders; and (3) Urban Segmentation and Inequality which will create innovative new measures of social segmentation and combine these with cutting-edge longitudinal and sorting-model techniques to explore the causes of neighbourhood segmentation, household location choice and neighbourhood inequalities. Five additional projects will focus on the referendum on Scottish independence, location dynamics and ethnicity and exploiting existing datasets. The research will fed into training activities and knowledge exchange events aimed at boosting capacity in quantitative methods amongst the UK social science community. The original data was collected by Registers of Scotland. Registers of Scotland is the non-ministerial government department responsible for compiling and maintaining 18 public registers. These relate to land, property, and other legal documents. The data is a complete census of housing transactions in Scotland from 1990 - 2014. Additional work was done by the AQMeN II team to impute the ethnicity and religion of buyers based on name using onomap -- a commercial software for name based imputation (http://www.onomap.org/).

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 examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

This statistic displays the percentage of U.S. adults aged between 50 to 75 years who were up to date with colorectal cancer screening as of 2018, by ethnicity. About 71 percent of white survey respondents indicated that they were up to date with colorectal cancer screening. Standard preventative screening includes a fecal occult blood tests within 1 year, sigmoidoscopy within 5 years and fecal occult blood test within 3 years, or colonoscopy within 10 years.

This layer contains a Vermont-only subset of block group level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.*VCGI exported a Vermont-only subset of the nation-wide layer to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator County_Name: County Name State_Name: State Name P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division COUNTY: County (FIPS) COUNTYNS: County (NS) TRACT: Census Tract BLKGRP: Block Group AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLAT: Internal Point (Latitude) INTPTLON: Internal Point (Longitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

The data correspond to the article entitled: "dNTPs and adjuvant reagent solutions in 3’ RACE improve the characterization of noncanonical RNA SARS-CoV-2 genomes"

R1. RACE 3’ Primer Blast Alignment. Contains BLAST alignments against the GenBank database using the consensus nucleotide sequence from the 3’ end of the SARS-CoV-2 genome and the polylinker. In addition, an illustration of the restriction enzyme pattern of the 3' RACE primer RV30AkCOVID19 and its synthesis by MALDI-TOF is included. The red box indicates the nucleotide sequence of the polylinker and the yellow box represents the 3' RACE primer along with the result of primer synthesis and purification.

R2. Reads and assembles SARS-CoV-2 genomes.

The folder "1) Reads - Ion torrent" contains the reads obtained from sequencing via Ion Torrent technology and the reagents used in this study.

The folder named "2) FastQC" contains the results of Ion Torrent sequencing. In the file name, the number indicates the sample, and the letters "RNA" indicate the sequencing according to the IonTorrent protocol. The cDNA synthesis procedures for this study correspond to the following nomenclature: dNTPs-R = dNTPs SARS-CoV-2 solution, DES-R = denaturation reagent, and COM PRO = commercial procedure.

The folders named "3) IRMA" and "4) Bowtie2" contain the assemblies of the genomes.

In addition, an Excel document with the nucleotide ratios of each characterized genome is included from SARS-CoV-2.

R3. BLAST alignment of assembled SARS-CoV-2 genomes. Contains two folders named "BLAST - IRMA" and "BLAST - Bowtie2," which contain plain text documents with the results of the BLAST alignment for the genomes obtained with each of the assemblies.

R4. Pangolin v1.16 and Nextclade v2.9.1 lineages for SARS-CoV-2 genomes. Contains the folders "Pangolin and Nextclade (Bowtie2)" and "Pangolin and Nextclade (IRMA)." Each folder shows the data obtained with the Pangolin v1.16 and Nextclade v2.9.1 software for the classification of the genomes reported in this study, which were assembled with the IRMA and Bowtie2 software.

R5. Reference genome alignment and assembled genomes. Contains the folders "1) IRMA genomes," "2) Bowtie2 genomes," and "3) Genomes 07dN120320 and 27St122620." The files show the sequences and alignments of the examined genomes (the file name indicates the analyzed genome) relative to the SARS-CoV-2 reference genome both in FASTA and Clustal W formats.

R6. Programmed −1 Ribosomal Frameshifting Structure. The folder "1) Gibbs free energy 2D" contains a plain text document indicating the secondary structures of the open reading frame stimulation element in dot-bracket format. The folder "2) modeling Data Modeling 3D" contains the information for generating the structure of folder 1 in 3D.

R7. SARS-CoV-2 Database.

1) GISAID_sequences.zip contains a Zip file that contains a folder named GISAID, which in turn contains plain text documents with the genomes of each variant indicated in the filename of each document.

2) The depuration of sequences_GISAID contains two subfolders. The first subfolder, named "1) SARS-CoV-2 complete genome" contains plain text documents with the genomes downloaded from GISAID without undetermined nucleotides. The file name of each document corresponds to the analyzed variant. The subfolder "2) SARS-CoV-2 eliminate genome" contains the sequences eliminated from subfolder 1 because they differed from the majority of the analyzed sequences.

3) SARS-CoV-2 consensus variants. Contains plain text documents with consensus sequences for each variant, with frequency thresholds of 20 and 100 indicated in the file name of each document.

4) SARS-CoV-2 alignment consensus variants. Contains two subfolders, with the number indicating the alignment frequency threshold. The "Alignment 20_" subfolder contains four documents named "with Ns," which correspond to fasta and Clustal formats with undetermined nucleotides, whereas the files named "without" do not have undetermined nucleotides. The "100_" folder has the same file pattern as the previous folder.

5) SARS-CoV-2 codons alignment consensus variants and nc-sgRNA. Contains a document with the alignment of the genomes characterized in this study with the reference genome of SARS-CoV-2. A subfolder named “SARS-CoV-2 codons nc-sgRNA” shows each of the nc-sgRNA obtained in this study with the reference genome, and the file name corresponds to the nc-sgRNAs. The subfolder “SARS-CoV-2 Geneious Prime” contains 4 documents. Each document includes the graphical representation of the alignment of the nc-sgRNA obtained with each treatment for the synthesis of SARS-CoV-2 cDNA with respect to the reference genome. The following three documents indicated with the numbers 25, 50, and 100 correspond to the percentage of identity with respect to the number of annotations relative to the reference genome, which is indicated in the title of each document.

6) Variant Alignment – Ns. Contains eight documents corresponding to the fasta and clustal formats with SARS-CoV-2 genomes obtained in this study from the reference genome and from genomes containing undetermined nucleotides of the Gamma, Lambda, Mu and Omicron variants.

R8. Phylogeny SARS-CoV-2. Contains two subfolders with the results of the phylogenetic analyses conducted via the maximum likelihood method of the genomes characterized in this study compared to the variants. The subfolder named "Phylogeny with Ns" indicates the analysis of genomes containing undetermined nucleotides, whereas "Phylogeny without Ns" corresponds to the analysis of complete genomes.

In 2011, 87.2 percent of the total population of the United Kingdom were white British. A positive net migration in recent years combined with the resultant international relationships following the wide-reaching former British Empire has contributed to an increasingly diverse population.

Varied ethnic backgrounds

Black British citizens, with African and/or African-Caribbean ancestry, are the largest ethnic minority population, at three percent of the total population. Indian Britons are one of the largest overseas communities of the Indian diaspora and make up 2.3 percent of the total UK population. Pakistani British citizens, who make up almost two percent of the UK population, have one of the highest levels of home ownership in Britain.

Racism in the United Kingdom

Though it has decreased in comparison to the previous century, the UK has seen an increase in racial prejudice during the first decade and a half of this century. Racism and discrimination continues to be part of daily life for Britain’s ethnic minorities, especially in terms of work, housing, and health issues. Moreover, the number of hate crimes motivated by race reported since 2012 has increased, and in 2017/18, there were 3,368 recorded offenses of racially or religiously aggravated assault with injury, almost a thousand more than in 2013/14.

As of July 2024, 70.4 percent of the Malaysian population were classified as Bumiputera, 22.4 percent were classified as ethnic Chinese, and 6.5 percent as ethnic Indians. Those who do not fall under these three main ethnic groups are classified as ‘Other’. Malaysia is a multi-ethnic and multi-religious society with three main ethnicities and language groups. Who are Malaysia’s Bumiputera? Bumiputera, meaning sons of the soil, is a term used to categorize the Malays, as well as the indigenous peoples of Peninsular Malaysia, also known as orang asli, and the indigenous peoples of Sabah and Sarawak. As of July 2023, the Bumiputera share of the population in Sabah was 89 percent, while that in Sarawak was 76.1 percent. Thus, the incorporation of the states of Sabah and Sarawak during the formation of Malaysia ensured that the ethnic Malays were able to maintain a majority share of the Malaysian population. Bumiputera privileges and ethnic-based politics The rights and privileges of the Malays and the natives of Sabah and Sarawak are enshrined in Article 153 of Malaysia’s constitution. This translated, in practice, to a policy of affirmative action to improve the economic situation of this particular group, through the New Economic Policy introduced in 1971. 50 years on, it is questionable whether the policy has achieved its aim. Bumiputeras still lag behind the other ethnic two major groups in terms of monthly household income. However, re-thinking this policy will certainly be met by opposition from those who have benefitted from it.

As of 2021, Akan was the largest ethnic group in Ghana, accounting for 45.7 percent of the country's population. Simultaneously, Akan, as a language, was the most widely spoken in Ghana. Mole-Dagbani and Ewe covered 18.5 percent and 12.8 percent of the groups of ethnicity, respectively. Other ethnic groups include Ga-Dangme, Gurma, Guan, and Grusi.

The prevalence of obesity among adults of all ethnicities in the United States has increased in the last three decades. Roughly half of all non-Hispanic Black and Mexican American adults were obese in 2017-2018. This statistic presents the prevalence of obesity (BMI of 30 kg/m2 and over) among adults in the United States from 1988-1994 to 2017-2018, by ethnicity.

A dataset listing Washington cities by population for 2024.

A dataset listing Iowa cities by population for 2024.