Among the 81 largest metropolitan areas (by population) in the United States, Knoxville, Tennessee was ranked first with **** percent of residents reporting as white, non-Hispanic in 2023.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Country Life Acres, the largest racial group is White alone with a population of 74 (96.10% 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 Country Life Acres
• Population: The population of the racial category (for Non-Hispanic) in the Country Life Acres is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Country Life Acres 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 Country Life Acres Population by Race & Ethnicity. You can refer the same here

In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Country Club Heights, the largest racial group is White alone with a population of 169 (95.48% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/country-club-heights-in-population-by-race-and-ethnicity.jpeg" alt="Country Club Heights Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Country Club Heights
• Population: The population of the racial category (for Non-Hispanic) in the Country Club Heights is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Country Club Heights 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 Country Club Heights Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Town And Country, the largest racial group is White alone with a population of 9,318 (83.18% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/town-and-country-mo-population-by-race-and-ethnicity.jpeg" alt="Town And Country Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Town And Country
• Population: The population of the racial category (for Non-Hispanic) in the Town And Country is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Town And Country 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 Town And Country Population by Race & Ethnicity. You can refer the same here

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

A special analysis of the Eurobarometer 2000 opinion poll on behalf of the European Monitoring Centre on Racism and Xenophobia. By SORA, Vienna, Austria, www.sora.at General recommendations and conclusions: These recommendations are based on findings hinted at in the data-analysis which do not permit the development of a complete set of policy recommendations. Policy recommendations should be based on a knowledge of causal relationships and the strength of effects which is beyond the scope of this project. Thus, the recommendations are linked and clearly connected to the evidence within the data. Political leadership: A quarter of all Europeans can be categorised as ‘ambivalent’ – meaning that they harbour positive and negative attitudes towards minorities at the same time. Data show that party affiliation is a part of the causal system producing attitudes towards minorities. Ambivalent people should be considered those who react most political leadership – awareness of this fact can help politicians to make their decisions. Unemployment: Experience with unemployment and the expectation of higher unemployment rates lead to an increase in hostile attitudes towards minorities. Sinking unemployment rates and information about a decrease in unemployment might reduce concerns about migration and minorities. Welfare: Since a large part of xenophobic concerns is about loss of welfare standards, policies which lend large majorities the feeling that they can participate in the increase of wealth within a growing economy will contribute significantly to reducing xenophobic concerns. Demographic developments and their impact have to be considered and researched. Particular attention should be paid to the number of retired people and the increasing number of old people with lower income and with low expectations within that group. An increase in hostility towards minorities might well get stronger in this group. Education: Higher education clearly correlates with positive attitudes towards minorities. More research should be carried out to determine the nature of this effect and establish whether the increase of higher education – which is a stable trend – will result in a more tolerant attitude within Europe in the coming decades. Personal relations: Supporting personal relationships between people of different religions, nations or with different skin colour increases tolerance. In the countries of Southern European, attitudes towards minorities seem to be influenced by other factors than in the rest of Europe. There is not enough evidence about causal relationships within this analysis to confirm that the conclusions mentioned above are meaningful for the southern part of Europe.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Country Club, the largest racial group is White alone with a population of 2,662 (89.48% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/country-club-mo-population-by-race-and-ethnicity.jpeg" alt="Country Club Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Country Club
• Population: The population of the racial category (for Non-Hispanic) in the Country Club is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Country Club 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 Country Club Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Hill Country Village, the largest racial group is White alone with a population of 565 (76.87% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/hill-country-village-tx-population-by-race-and-ethnicity.jpeg" alt="Hill Country Village Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Hill Country Village
• Population: The population of the racial category (for Non-Hispanic) in the Hill Country Village is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Hill Country Village 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 Hill Country Village Population by Race & Ethnicity. You can refer the same here

Know Your Customer Dataset, Face Detection and Re-identification

The dataset is created on the basis of Selfies and ID Dataset

80,000+ photos including 10,600+ document photos from 5,300 people from 28 countries. The dataset includes 2 photos of a person from his documents and 13 selfies. All people presented in the dataset are caucasian. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups.

Photo documents contains only a photo of a person. All personal information from the document is hidden

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9ad166a8728e7299087a69793e420918%2FFrame%2015%20(1).png?generation=1712143714014867&alt=media" alt="">

Documents in the dataset

• Passports
• International passport
• Driver licenses
• Student cards
• Health certificate
• Pensioner's ID
• Pass to work
• Other documents

The dataset can be utilized for a wide range of tasks, including face recognition, emotion detection, age estimation, gender classification, or any problem related to human image analysis.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

Statistics for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5a5be7a59953aa5e05014dbc88c7740b%2FFrame%2093.png?generation=1712832246364646&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset consists of: - files - includes 7 folders corresponding to each person and including 15 images (2 id photos and 13 selfies), - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• id: id of the person,
• age - age of the person,
• gender - gender of the person,
• country - country of the person,
• id_1, id_2: link to access id photos,
• selfie_1, selfie_2, ..., selfie_13: link to access each of the 13 selfies of the person

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset, machine learning, image-to-image, re-identification, id photos, selfies and paired id, photos, id verification models, passport, id card image, digital photo-identification, caucasian people, caucasian dataset

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

The data consist of transcripts of interviews with 19 individuals from Brazil and 5 individuals from Colombia, who are all involved in Black and Indigenous activist organisations or in state agencies that are charged with promoting anti-racism and/or human rights.

Each transcript begins with a paragraph giving contextual information

Latin America has often been held up as a region where racism is less of a problem than in regions such as the United States or Europe. Because most people are 'mestizos' (mixed race) and mixture is often seen as the essence of national identity, clear racial boundaries are blurred, resulting in comparatively low levels of racial segregation and a traditionally low public profile for issues of race. In Europe and the United States, the racial mixture and interaction across racial boundaries, which are typical of Latin America and are becoming more visible elsewhere, are heralded by some observers as leading towards a 'post-racial' reality, where anti-racism and multiculturalism - seen in this view as divisive policies that accentuate social differences - become unnecessary. Critics point out that mixture is not an antidote to racial inequality and racism in Latin America: they all coexist. This severely qualifies claims that mixture can lead to a 'post-racial' era.

This project will investigate anti-racist practices and ideologies in Bolivia, Brazil, Colombia and Mexico. The project will contribute to conceptualising and addressing problems of racism, racial inequality and anti-racism in the region. We also propose that Latin America presents new opportunities for thinking about racism and anti-racism in a 'post-racial' world. Understanding how racism and anti-racism are conceived and practised in Latin America - in contexts in which mixture is pervasive - can help us to understand how to think about racism and anti-racism in other regions of the world, where notions of race have been changing in some respects towards Latin American patterns. It is also crucial to show the variety of ways in which mixture operates and co-exists with racism in Latin America - a region that is far from homogeneous.

Research teams in each country, working with a range of organisations concerned with racism and discrimination, will explore how the organisations conceptualise and address key problems, which are becoming more salient in other regions, which confront similar scenarios. First, how to practice anti-racism when most people are mixed and when they may deny the importance of race and racism and themselves be both victims and the perpetrators of racism. Second, how to conceptualise and practice anti-racism when 'culture' seems to be the dominant discourse for talking about difference, but when physical difference (skin colour, hair type, etc.) remain powerful but often unacknowledged signs that move people to discriminate. Third, how to understand racism and combat it when race and class coincide to a great extent and make it easy to deny that race and racism are important factors. Fourth, how to make sure anti-racism addresses gender difference effectively, in a context in which mixture between white men and non-white women has been seen as the founding act of the nation. Fifth, how to pursue anti-racism when it is often claimed that there is little overt racist violence and that this is evidence of racial tolerance. We will explore how these elements structure - and may constrain - ideas about (anti-)racism within institutions, organisations and everyday practice.

Our project will work with organisations in Bolivia, Brazil, Colombia and Mexico - countries that capture a good range of the region's diversity - to explore how racism and anti-racism are conceptualised and addressed in state and non-state circles, in legislation and the media, and in a variety of campaigns and projects. We aim to strengthen anti-racist practice in Latin America by feeding back our findings and by helping build networks; and to provide useful insights for understanding racism and anti-racism within and outside the region.

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.

Ethnic-racial classification criteria are widely recognized to vary according to historical, cultural and political contexts. In Brazil, the strong influence of individual socio-economic factors on race/colour self-classification is well known. With the expansion of genomic technologies, the use of genomic ancestry has been suggested as a substitute for classification procedures such as self-declaring race, as if they represented the same concept. We investigated the association between genomic ancestry, the racial composition of census tracts and individual socioeconomic factors and self-declared race/colour in a cohort of 15,105 Brazilians. Results show that the probability of self-declaring as black or brown increases according to the proportion of African ancestry and varies widely among cities. In Porto Alegre, where most of the population is white, with every 10% increase in the proportion of African ancestry, the odds of self-declaring as black increased 14 times (95%CI 6.08–32.81). In Salvador, where most of the population is black or brown, that increase was of 3.98 times (95%CI 2.96–5.35). The racial composition of the area of residence was also associated with the probability of self-declaring as black or brown. Every 10% increase in the proportion of black and brown inhabitants in the residential census tract increased the odds of self-declaring as black by 1.33 times (95%CI 1.24–1.42). Ancestry alone does not explain self-declared race/colour. An emphasis on multiple situational contexts (both individual and collective) provides a more comprehensive framework for the study of the predictors of self-declared race/colour, a highly relevant construct in many different scenarios, such as public policy, sociology and medicine.

Why can some Americans acknowledge the deeply rooted racism in the U.S. while others cannot? Past research suggests that the more people want to avoid feeling negative (“avoided negative affect; ANA”), the less likely they focus on and even perceive someone’s suffering. Because acknowledging racism is one specific instance of noticing and acknowledging that people are suffering, the present research investigates whether ANA might also affect the degree to which people acknowledge racism. We predicted that the more people want to avoid feeling negative, the less they will acknowledge systemic racism and the more they will deny negative aspects of their country’s history and current policies, that is, the more blindly patriotic they will be. In Study 1, 104 undergraduates reported their ANA, patriotism, and rated how much racism they perceived in certain situations. As predicted, the more participants wanted to avoid feeling negative, the less they acknowledged systemic racism. These findings held even after controlling for political ideology, ethnicity, moral foundations, and how people actually feel. However, ANA did not predict blind patriotism. In Study 2, we randomly assigned 116 participants to either an increase ANA, decrease ANA, or control condition. As predicted, participants in the increase ANA condition acknowledged systemic racism less than those in the decrease ANA and control conditions. Wanting to avoid feeling negative might be one barrier to dismantling racial inequalities. Given the high degree of ANA in the U.S., we discuss implications of this work.

Explore various maps to learn more about the population in the US based on how people respond to the American Community Survey (ACS). Based on how people responded, we can learn more about where different race and ethnicity groups live throughout the country. The pattern for each map portrays the most current 5-year ACS estimates, and is offered for states, counties, and tracts. Zoom and explore the map to see the patterns in your area.In this collection, you'll find various different topics:The predominant race in each area (which one has the largest count)Race by dot densityPeople of color (non-white population)Percent of the population by each raceWhere is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.

The dataset contains general information about world countries as well as information about their flags, economy, and geographical location.

File description:

world_flags_2024.csv - dataset data_description.txt - full description of each column.

Source:

The dataset contains 41 columns: 8 of them are numeric-valued, others are either Boolean or nominal-valued. In the CSV file fields are separated by commas.

Note: Possible errors or inaccuracies in the interpretation of blazon images or other symbols on flags are not intentional, but arise from a lack of awareness on the part of the author.

Data fields:

Country - Names of all sovereign states as of 2024.

FlagUrl - Link to country's flag on Flagpedia.net.

AspectRatio - Aspect ration of the flag. Format: Height:Width.

LatestAdoption - Year of the last changes in the flag design.

White - 1 if white color present in the flag, 0 otherwise.

Red - 1 if red color present in the flag, 0 otherwise.

Blue - 1 if blue color present in the flag, 0 otherwise.

Black - 1 if black color present in the flag, 0 otherwise.

Yellow - 1 if yellow color present in the flag, 0 otherwise.

Green - 1 if green color present in the flag, 0 otherwise.

Orange - 1 if orange color present in the flag, 0 otherwise.

OtherColor - 1 if any other color present in the flag, 0 otherwise.

StripesEqual - 1 if all the stripes that make up the flag have equal width, 0 otherwise.

StripesVertical - 1 if stripes are arranged vertically, 0 otherwise.

StripesHorizontal - 1 if stripes are arranged horizontally, 0 otherwise.

StripesDiagonal - 1 if stripes are arranged diagonally, 0 otherwise.

StripesOther - 1 if the direction of stripes is mixed, 0 otherwise.

SingleColor - 1 if the flag is single color, i.e. there is no stripes, 0 otherwise.

LeftTriangle - 1 if there is a triangle on the left hand side of the flag, 0 otherwise.

Canton - 1 if there is an insert with an image in the top-left corner of the flag, 0 otherwise.

Cross - 1 if the flag contains a cross, 0 otherwise.

Crescent - 1 if the flag contains a crescent, 0 otherwise.

Sun - 1 if the flag contains the sun, 0 otherwise.

Bird - 1 if the flag contains a bird, 0 otherwise.

Stars - Number of stars on the flag.

Circle - 1 if the flag contains a circle, 0 otherwise.

BlazonOrOther - 1 if the flag contains a blazon or any other symbol, 0 otherwise.

Continent - Continent where the country is located. Note: Some countries have their parts located on multiple continents. For those countries the continent where the majority of its territory is located is chosen. Example: Russian Federation and Turkey.

Landlocked - 1 if the country has no direct access to an ocean, 0 otherwise.

TotalArea - Area of the country in km^2.

Population - Population of the country as of 2024.

Capital - Name of the capital of the country.

CapitalPopulation - Population of the capital.

HighestPoint - The highest point of the country.

LowestPoint - The lowest point of the country.

Religion - Dominant religion. If multiple, the most popular is chosen.

Currency - Name of the currency of the country.

CallingCode - Calling code of the country.

GDPPerCapita - GDP per capita in USD as of 2022. Zero if unknown.

HDI - Human Development Index as of 2022.

Gini - Income inequality: Gini coefficient as of 2023.

References:

1. https://www.un.org/dgacm/sites/www.un.org.dgacm/files/Documents_Protocol/officialnamesofcountries.pdf
2. https://flagpedia.net
3. https://www.worldometers.info
4. https://www.britannica.com
5. https://ourworldindata.org
6. https://www.jetpunk.com/info/capital-cities-by-population
7. https://wisevoter.com/country-rankings/religion-by-country
8. https://assets.pewresearch.org/wp-content/uploads/sites/11/2012/12/globalReligion-tables.pdf
9. Header image generated by leonardo.ai

Inspired by:

https://www.kaggle.com/datasets/edoardoba/world-flags https://www.kaggle.com/code/mscgeorges/country-flags-analysis

BackgroundMigrant and ethnic minority groups are often assumed to have poor health relative to the majority population. Few countries have the capacity to study a key indicator, mortality, by ethnicity and country of birth. We hypothesized at least 10% differences in mortality by ethnic group in Scotland that would not be wholly attenuated by adjustment for socio-economic factors or country of birth.Methods and findingsWe linked the Scottish 2001 Census to mortality data (2001–2013) in 4.62 million people (91% of estimated population), calculating age-adjusted mortality rate ratios (RRs; multiplied by 100 as percentages) with 95% confidence intervals (CIs) for 13 ethnic groups, with the White Scottish group as reference (ethnic group classification follows the Scottish 2001 Census). The Scottish Index of Multiple Deprivation, education status, and household tenure were socio-economic status (SES) confounding variables and born in the UK or Republic of Ireland (UK/RoI) an interacting and confounding variable. Smoking and diabetes data were from a primary care sub-sample (about 53,000 people). Males and females in most minority groups had lower age-adjusted mortality RRs than the White Scottish group. The 95% CIs provided good evidence that the RR was more than 10% lower in the following ethnic groups: Other White British (72.3 [95% CI 64.2, 81.3] in males and 75.2 [68.0, 83.2] in females); Other White (80.8 [72.8, 89.8] in males and 76.2 [68.6, 84.7] in females); Indian (62.6 [51.6, 76.0] in males and 60.7 [50.4, 73.1] in females); Pakistani (66.1 [57.4, 76.2] in males and 73.8 [63.7, 85.5] in females); Bangladeshi males (50.7 [32.5, 79.1]); Caribbean females (57.5 [38.5, 85.9]); and Chinese (52.2 [43.7, 62.5] in males and 65.8 [55.3, 78.2] in females). The differences were diminished but not eliminated after adjusting for UK/RoI birth and SES variables. A mortality advantage was evident in all 12 minority groups for those born abroad, but in only 6/12 male groups and 5/12 female groups of those born in the UK/RoI. In the primary care sub-sample, after adjustment for age, UK/RoI born, SES, smoking, and diabetes, the RR was not lower in Indian males (114.7 [95% CI 78.3, 167.9]) and Pakistani females (103.9 [73.9, 145.9]) than in White Scottish males and females, respectively. The main limitations were the inability to include deaths abroad and the small number of deaths in some ethnic minority groups, especially for people born in the UK/RoI.ConclusionsThere was relatively low mortality for many ethnic minority groups compared to the White Scottish majority. The mortality advantage was less clear in UK/RoI-born minority group offspring than in immigrants. These differences need explaining, and health-related behaviours seem important. Similar analyses are required internationally to fulfil agreed goals for monitoring, understanding, and improving health in ethnically diverse societies and to apply to health policy, especially on health inequalities and inequities.

This statistic shows the population of the United States in the final census year before the American Civil War, shown by race and gender. From the data we can see that there were almost 27 million white people, 4.5 million black people, and eighty thousand classed as 'other'. The proportions of men to women were different for each category, with roughly 700 thousand more white men than women, over 100 thousand more black women than men, and almost three times as many men than women in the 'other' category. The reason for the higher male numbers in the white and other categories is because men migrated to the US at a higher rate than women, while there is no concrete explanation for the statistic regarding black people.