About this dataset

Context

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

Key observations

With a zero Hispanic population, White Earth is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 76 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 White Earth
• Population: The population of the racial category (for Non-Hispanic) in the White Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of White Earth 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 White Earth Population by Race & Ethnicity. You can refer the same here

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.

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.

Data from https://github.com/rfordatascience/tidytuesday/edit/master/data/2021/ released under an open license: https://github.com/rfordatascience/tidytuesday/blob/master/LICENSE

College Enrollment

The data this week comes from Data.World and Data.World and was originally from the NCES.

High school completion and bachelor's degree attainment among persons age 25 and over by race/ethnicity & sex 1910-2016

Fall enrollment in degree-granting historically Black colleges and universities (HBCU)

Consider donating to HBCUs, to help fund student's financial assistance programs.

Donation link: https://thehbcufoundation.org/donate/

There's other additional HBCU datasets at Data.World as well.

HBCU Donations Article

... Donation will be placed in an endowment for students to fund need-based scholarships. President Reynold Verret believes the donation will provide an opportunity for students who don’t have the same financial support as others.

“Xavier has roughly more than half of our students who are Pell-eligible. Which means they are in the lowest fifth of the socioeconomic ladder in the country. The lowest quintile. So these students really have significant family needs,” said Verret. “They’re often the first generation in their families to attend college, and meeting the gap between what Pell and the small loans provide and making it affordable is where that need-based is, which is not just based on merit, on your highest ACT or GPA, but basically to qualify students who are able who have the talent and the ability to succeed at Xavier.”

I've left the datasets relatively "untidy" this week so you can practice some of the pivot_longer() functions from tidyr. Note that all of the individual CSVs that are duplicates of the raw Excel files.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-02-02')
tuesdata <- tidytuesdayR::tt_load(2021, week = 6)

hbcu_all <- tuesdata$hbcu_all

# Or read in the data manually

hbcu_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-02/hbcu_all.csv')

Data Dictionary

hbcu.csv

hs_students.csv

• The percentage of students broken down by race/ethnicity, aged 25 and over who have graduated HS.

bach_students, female_bach_students, female_hs_students, male_bach_students, male_hs_students:

• Same as above, but for specific gender and education combination.

Face Images Dataset

Description

10,109 people - face images dataset includes people collected from many countries. Multiple photos of each person’s daily life are collected, and the gender, race, age, etc. of the person being collected are marked.This Dataset provides a rich resource for artificial intelligence applications. It has been validated by multiple AI companies and proves beneficial for achieving outstanding performance in real-world applications. Throughout the process of Dataset collection, storage, and usage, we have consistently adhered to Dataset protection and privacy regulations to ensure the preservation of user privacy and legal rights. All Dataset comply with regulations such as GDPR, CCPA, PIPL, and other applicable laws. For more details, please refer to the link: https://www.nexdata.ai/datasets/computervision/1402?source=Kaggle

Data size

10,109 people, no less than 30 images per person

Race distribution

3,504 black people, 3,559 Indian people and 3,046 Asian people

Gender distribution

4,930 males, 5,179 females

Age distribution

most people are young aged, the middle-aged and the elderly cover a small portion

Collecting environment

including indoor and outdoor scenes

Data diversity

different face poses, races, accessories, ages, light conditions and scenes

Data format

.jpg, .png, .jpeg

Licensing Information

Commercial License

This large-scale face image dataset features 10,109 individuals from various countries and ethnic backgrounds. Each subject has been captured in multiple real-world scenarios, resulting in diverse facial images under varying angles, lighting conditions, and expressions. Detailed annotations include gender, race, and age, making the dataset suitable for tasks such as facial recognition, face clustering, demographic analysis, and machine learning model training.The dataset has been validated by multiple AI companies and proven to deliver strong performance in real-world applications. All data collection, storage, and processing strictly adhere to global data protection regulations, including GDPR, CCPA, and PIPL, ensuring legal compliance and privacy preservation.

Data size 10,109 people, no less than 30 images per person

Race distribution 3,504 black people, 3,559 Indian people and 3,046 Asian people

Gender distribution 4,930 males, 5,179 females

Age distribution most people are young aged, the middle-aged and the elderly cover a small portion

Collecting environment including indoor and outdoor scenes

Data diversity different face poses, races, accessories, ages, light conditions and scenes

Data format .jpg, .png, .jpeg

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.

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

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. With some classification methods (particuarly template-based methods, such as SVM and K-nearest neighbors),

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

Abstract The process of attribution values to some groups can be used as a resource for determining differences between ingroup and outgroup, what may lead to discriminatory behavior against the outgroup. In this sense, the present study sought to determine whether individuals perceive dissimilarities between the values attibuted to themselves, to white and to black people, and if these dissimilarities can follow a prejudice-based logic, expressing subtle racial prejudice. Study 1 (n = 220) aimed to rank the values in terms of socio-economic progress, identifying values that are representative of developed and underdeveloped countries. Study 2 (n = 420) evaluated whether the values attibuted to themselves, to the black and to the white are different and this difference follows a prejudice-based. Overall, results showed a tendency towards the association of third world values such as collectivism to blacks, and first world values such as individualism to whites.

The graph illustrates the number of victims of race-based hate crimes in the United States in 2025. The x-axis lists various ethnic groups, while the y-axis represents the corresponding number of victims. The data reveals that Anti-Black hate crimes were the most prevalent, with 1,743 victims, followed by Anti-Hispanic and Anti-Asian crimes with 629 and 201 victims respectively. Other categories include Anti-Other Race (308), Anti-American Indian (74), Anti-Arab (73), and Anti-Native Pacific (25). The data indicates a significant disparity in the number of victims across different ethnic groups, with Anti-Black hate crimes being the most prominent.

Overview

The Mask Wearing dataset is an object detection dataset of individuals wearing various types of masks and those without masks. One could use this dataset to build a system for detecting if an individual is wearing a mask in a given photo.

Content

Each photo in the data set is a 416x416-black-padding image either with people wearing masks or not.

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

This data file contains details of various nations and their flags. In this file the fields are separated by spaces (not commas). With this data you can try things like predicting the religion of a country from its size and the colours in its flag.

10 attributes are numeric-valued. The remainder are either Boolean- or nominal-valued.

Attribute Information:

1. name: Name of the country concerned
2. landmass: 1=N.America, 2=S.America, 3=Europe, 4=Africa, 4=Asia, 6=Oceania
3. zone: Geographic quadrant, based on Greenwich and the Equator; 1=NE, 2=SE, 3=SW, 4=NW
4. area: in thousands of square km
5. population: in round millions
6. language: 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others
7. religion: 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others
8. bars: Number of vertical bars in the flag
9. stripes: Number of horizontal stripes in the flag
10. colours: Number of different colours in the flag
11. red: 0 if red absent, 1 if red present in the flag
12. green: same for green
13. blue: same for blue
14. gold: same for gold (also yellow)
15. white: same for white
16. black: same for black
17. orange: same for orange (also brown)
18. mainhue: predominant colour in the flag (tie-breaks decided by taking the topmost hue, if that fails then the most central hue, and if that fails the leftmost hue)
19. circles: Number of circles in the flag
20. crosses: Number of (upright) crosses
21. saltires: Number of diagonal crosses
22. quarters: Number of quartered sections
23. sunstars: Number of sun or star symbols
24. crescent: 1 if a crescent moon symbol present, else 0
25. triangle: 1 if any triangles present, 0 otherwise
26. icon: 1 if an inanimate image present (e.g., a boat), otherwise 0
27. animate: 1 if an animate image (e.g., an eagle, a tree, a human hand) present, 0 otherwise
28. text: 1 if any letters or writing on the flag (e.g., a motto or slogan), 0 otherwise
29. topleft: colour in the top-left corner (moving right to decide tie-breaks)
30. botright: Colour in the bottom-left corner (moving left to decide tie-breaks)

A Comprehensive History of Major Disease Outbreaks

Tracing the Past to Prevent the Future

About this dataset

This dataset provides a comprehensive record of major disease outbreaks throughout history. It includes information on the disease, the death toll, the date and location of the outbreak, and the global and regional population lost.

Disease outbreaks are a major public health issue that can have devastating consequences. This dataset can help us better understand how these diseases spread and how to prevent them in the future. By studying this data, we can learn from past mistakes and take steps to avoid repeating them

How to use the dataset

This dataset provides a comprehensive record of major disease outbreaks throughout history. It includes information on the disease, the death toll, the date and location of the outbreak, and the global and regional population lost.

To use this dataset, simply download it as a CSV file and import it into your favourite data analysis software. From there, you can begin to explore the data and understand more about how these diseases have affected people throughout history

Research Ideas

• This dataset can be used to study the history of major disease outbreaks and the effects they have had on global and regional populations.

• This dataset can be used to predict future disease outbreaks by identifying patterns and trends in past outbreaks.

• This dataset can be used to develop better strategies for responding to and preventing future disease outbreaks

Acknowledgements

The dataset was compiled by the Centers for Disease Control and Prevention (CDC)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_16.csv

File: df_26.csv

File: df_20.csv

File: df_18.csv

File: df_25.csv

File: df_11.csv | Column name | Description | |:------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------| | vteNatural disasters – list by death toll | This column lists natural disasters by death toll. (Categorical) | | vteNatural disasters – list by death toll.1 | This column lists natural disasters by death toll and provides additional information on the disaster. (Categorical) |

File: df_1.csv | Column name | Description | |:-----------------------------|:----------------------------------------------------------------------------------| | Rank | The rank of the disease outbreak. (Numeric) | | Disease | The name of the disease. (String) | | Death toll | The number of deaths caused by the disease outbreak. (Numeric) | | Global population lost | The percentage of the global population lost to the disease outbreak. (Numeric) | | Regional population lost | The percentage of the regional population lost to the disease outbreak. (Numeric) | | Date | The date of the disease outbreak. (Date) | | Location | The location of the disease outbreak. (String) |

File: df_4.csv

File: df_21.csv

File: df_17.csv

File: df_24.csv

File: df_9.csv

File: df_13.csv

File: df_14.csv

File: df_22.csv

File: df_15.csv

File: df_10.csv

File: df_3.csv

File: df_19.csv

File: df_2.csv | Column name | Description | |:--------------------------|:--------------------------------------------------------------------| | Date | The date of the disease outbreak. (Date) | | Location | The location of the disease outbreak. (String) | | Disease | The name of the disease. (String) | | Event | A description of the disease outbreak. (String) ...

BackgroundElectronic health records (EHRs) are increasingly used to investigate health inequalities across ethnic groups. While there are some studies showing that the recording of ethnicity in EHR is imperfect, there is no robust evidence on the accuracy between the ethnicity information recorded in various real-world sources and census data.Methods and findingsWe linked primary and secondary care NHS England data sources with Census 2021 data and compared individual-level agreement of ethnicity recording in General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES), Ethnic Category Information Asset (ECIA), and Talking Therapies for anxiety and depression (TT) with ethnicity reported in the census. Census ethnicity is self-reported and, therefore, regarded as the most reliable population-level source of ethnicity recording. We further assessed the impact of multiple approaches to assigning a person an ethnic category. The number of people that could be linked to census from ECIA, GDPPR, HES, and TT were 47.4m, 43.5m, 47.8m, and 6.3m, respectively. Across all 4 data sources, the White British category had the highest level of agreement with census (≥96%), followed by the Bangladeshi category (≥93%). Levels of agreement for Pakistani, Indian, and Chinese categories were ≥87%, ≥83%, and ≥80% across all sources. Agreement was lower for Mixed (≤75%) and Other (≤71%) categories across all data sources. The categories with the lowest agreement were Gypsy or Irish Traveller (≤6%), Other Black (≤19%), and Any Other Ethnic Group (≤25%) categories.ConclusionsCertain ethnic categories across all data sources have high discordance with census ethnic categories. These differences may lead to biased estimates of differences in health outcomes between ethnic groups, a critical data point used when making health policy and planning decisions.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Context

TruMedicines has trained a deep convolutional neural network to autoencode and retrieve a saved image, from a large image dataset based on the random pattern of dots on the surface of the pharmaceutical tablet (pill). Using a mobile phone app a user can query the image datebase and verify the query pill is not counterfeit and is authentic, additional meta data can be displayed to the user: manf date, manf location, drug expiration date, drug strength, adverse reactions etc.

Content

TruMedicines Pharmaceutical images of 252 speckled pill images. We have convoluted the images to create 20,000 training database by: rotations, grey scale, black and white, added noise, non-pill images, images are 292px x 292px in jpeg format

In this playground competition, Kagglers are challenged to develop deep Convolutional Neural Network and hash codes to accurately identify images of pills and quickly retrieved from our database. Jpeg images of pills can be autoencoded using a CNN and retrieved using a CNN hashing code index. Our Android app takes a phone of a pill and sends a query to the image database for a match, then returns meta data abut the pill: manf date, expiration date, ingredients, adverse reactions etc. Techniques from computer vision alongside other current technologies can make recognition of non-counterfeit, medications cheaper, faster, and more reliable.

Acknowledgements

Special Thanks to Microsoft Paul Debaun and Steve Borg and NWCadence, Bellevue WA for their assistance

Inspiration

TruMedicines is using machine learning on a mobile app to stop the spread of counterfeit medicines around the world. Every year the World Health Organization WHO estimates 1 million people die or become disabled due to counterfeit medicine.

In 1789, on the eve of the Haitian (and French) Revolution, the French colony of St Domingue had an estimated population of 556 thousand people. Of these, 500 thousand are thought to have been African slaves (approximately half of the entire Caribbean's slave population at the time), while just over ten percent of the population were whites or free people of color. Following the Haitian Revolution's conclusion in 1804, Haiti would become just the second nation in the Americas to gain its independence, and was the first (and only) country in the world to have been established by former slaves.