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

About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by Multi-Racial Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Multi-Racial Other Race Population: This column displays the rank of cities in the Blue Earth County, MN by their Multi-Racial Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial Other Race Population: The Multi-Racial Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Multi-Racial Other Race Population: This tells us how much of the entire Blue Earth County, MN Multi-Racial Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Black Earth, the largest racial group is White alone with a population of 1,565 (95.72% 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 Black Earth
• Population: The population of the racial category (for Non-Hispanic) in the Black Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black 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 Black Earth Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Black Earth town, the largest racial group is White alone with a population of 392 (97.03% 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 Black Earth town
• Population: The population of the racial category (for Non-Hispanic) in the Black Earth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black Earth town 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 Black Earth town Population by Race & Ethnicity. You can refer the same here

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Race/Ethnicity, Educational Attainment, Unemployment, Health Insurance, Disability and Vehicle Access.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Race/Ethnicity

·
WPop: Total population of those who identify as white alone (B01001A).

·
PWPop: Percentage of total population that identifies as white alone (B01001A).

·
BPop: Total population of those who identify as black or African American alone (B01001B).

·
PWPop: Percentage of total population that identifies as black or African American alone (B01001B).

·
AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).

·
PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).

·
APop: Total population of those who identify as Asian alone (B01001D).

·
PAPop: Percentage of total population that identifies as Asian alone (B01001D).

·
PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).

·
PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).

·
OPop: Total population of those who identify as Some Other Race alone (B01001F).

·
POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).

·
MPop: Total population of those who identify as Two or More Races (B01001G).

·
PMPop: Percentage of total population that identifies as Two or More Races (B01001G).

·
WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).

·
PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).

·
LPop: Total population of those who identify as Hispanic or Latino (B01001I).

·
PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).

Educational Attainment

·
EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).

·
PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).

·
EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

·
PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

·
EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

·
PEdSC1824: Percentage of population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

·
EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

·
PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

·
EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).

·
PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).

·
Ed912nD: Total population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

·
PEd912nD: Percentage of population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

·
EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).

·
PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).

·
EdSC: Total population ages 25 and over with some college education but no degree (S1501).

·
PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).

·
EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).

·
PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).

·
EdB: Total population ages 25 and over with bachelor’s degree (S1501).

·
PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).

·
EdG: Total population ages 25 and over with a graduate or professional degree (S1501).

·
PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).

Unemployment, Health Insurance, Disability

·
UnempR: Unemployment rate among the population ages 16 and over (S2301).

·
UnIn: Total non-institutionalized population without health insurance (B27001).

·
PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).

·
Disab: Total non-institutionalized population with a disability (S1810).

·
PDisab: Percentage of non-institutionalized populations with a disability (B27001).

Vehicle Access

·
OwnNV: Total number of owner-occupied households without a vehicle (B25044).

·
POwnNV: Percentage of owner-occupied households without a vehicle (B25044).

·
OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).

·
POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).

·
RentNV: Total number of renter-occupied households without a vehicle (B25044).

·
PRentNV: Percentage of renter-occupied households without a vehicle (B25044).

·
RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).

·
POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

Brunei Population: Mid Year: By Race: Other: Female data was reported at 35.000 Person th in 2023. This records an increase from the previous number of 28.900 Person th for 2022. Brunei Population: Mid Year: By Race: Other: Female data is updated yearly, averaging 26.900 Person th from Jun 1972 (Median) to 2023, with 52 observations. The data reached an all-time high of 48.748 Person th in 2016 and a record low of 2.289 Person th in 1972. Brunei Population: Mid Year: By Race: Other: Female data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

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.

Brunei Population: Mid Year: By Race: Other data was reported at 76.200 Person th in 2024. This records an increase from the previous number of 75.000 Person th for 2023. Brunei Population: Mid Year: By Race: Other data is updated yearly, averaging 68.200 Person th from Jun 1972 (Median) to 2024, with 53 observations. The data reached an all-time high of 101.567 Person th in 2021 and a record low of 7.102 Person th in 1972. Brunei Population: Mid Year: By Race: Other data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

Mutation in mismatch repair genes (MMR) is the genetic predisposition for gastrointestinal cancer represented by the Lynch Syndrome. Identification of the mutation carrier is critical in prevention and treatment of the cancer. Chinese is the largest ethnic population with the largestgastrointestinal cancer cases in the world, but systematic knowledge for the mutation in MMR is lack in Chinese population. Through comprehensive data mining, we collected nearly all MMR data derived from 33,998 Chinese of 23,938 cancer and 10,060 non-cancer cases reported from 1997 to 2019. Upon standardization and re-annotation, the data following international standards, we identified a total of 540 distinct MMR variants including 487 single base change and indel, and 53 large deletion/duplication in four MMR genes of MLH1, MSH2, MSH6and PMS2; 153 of the variants were classified as Pathogenic or Likely Pathogenic. This MMR dataset is the largest collection from a single, non-Caucasian population. We developed an open-access database, dbMMR-Chinese (https://dbMMR-chinese.fhs.um.edu.mo), to share with community for MMR mutation-related cancer study and clinical application.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Earth, the largest racial group is White alone with a population of 275 (86.75% 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 Earth
• Population: The population of the racial category (for Non-Hispanic) in the Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of 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 Earth Population by Race & Ethnicity. You can refer the same here

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_3de421ca89ff660c9fdc00debbf9764e/view

Brunei Population: Mid Year: By Race: Chinese data was reported at 43.800 Person th in 2024. This records an increase from the previous number of 43.400 Person th for 2023. Brunei Population: Mid Year: By Race: Chinese data is updated yearly, averaging 39.200 Person th from Jun 1972 (Median) to 2024, with 53 observations. The data reached an all-time high of 44.350 Person th in 1985 and a record low of 31.700 Person th in 1992. Brunei Population: Mid Year: By Race: Chinese data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

About this dataset

Context

The dataset tabulates the Black Earth Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Black Earth, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Black Earth.

Key observations

Among the Hispanic population in Black Earth, regardless of the race, the largest group is of Mexican origin, with a population of 77 (86.52% of the total 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.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Black Earth
• Population: The population of the specific origin for Hispanic or Latino population in the Black Earth is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Black Earth total Hispanic or Latino 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 Black Earth Population by Race & Ethnicity. You can refer the same here

Abstract

The Born in Bradford study is tracking the health and wellbeing of over 13,500 children, and their parents born at Bradford Royal Infirmary between March 2007 and December 2010.

Documentation

Born in Bradford is a prospective pregnancy and birth cohort established to examine how genetic, nutritional, environmental, behavioral and social factors affect health and development during childhood, and subsequently adult life, in a deprived multi-ethnic population. It was developed in close consultation with local communities, clinicians and policy makers with commitment from the outset to undertake research that would both inform interventions to improve health in the city and generate robust science relevant to similar communities in the UK and across the world. Between 2007 and 2011 information on a wide range of characteristics were collected from 12,453 women (and 3,356 partners) who experienced 13,778 pregnancies and delivered 13,818 live births.

Source

Notes

Data Presentation: Born in Bradford Data

Born in Bradford Data Dictionary

Born in Bradford has a number of unique strengths: a) Composition. Half of all the families recruited are living in the UK’s most deprived wards, and 45% are of Pakistani origin. Half of Pakistani-origin mothers and fathers were born outside the UK and over half are related to their partner. This combination enhances the opportunity to study the interplay of deprivation, ethnicity, migration and cultural characteristics and their relationship to social, economic and health outcomes research relevant to many communities across the world.

b) Rich characterization. Detailed information has been collected from parents about demographic, economic, lifestyle, cultural, medical and health factors. Pregnancy oral glucose tolerance tests (OGTT), have been completed in 85% of the cohort and in combination with repeat fetal ultrasound data and subsequent follow-up growth and adiposity (repeat skinfolds, weight and height from birth to current age) will enable BiB uniquely to explore ethnic differences in body composition trajectories through infancy and childhood.

c) Genetic and biomarker data. Maternal, neonatal and follow-up child blood samples have provided biomarker measures of adiposity and immunity, together with stored samples, for which funding has been secured, to assess targeted NMR metabolites in maternal pregnancy fasting samples, cord-blood and infant samples taken at 12-24 months. Genome wide data is available for 9000+ mothers and 8000+ children and funding has been secured for DNA methylation of 1000 mother-child pairs. Our BiB biobank contains 200,000 stored samples.

d) System-wide coverage. The study has successfully linked primary and secondary care, radiology, laboratory and local authority data. This successful data linkage to routine health and education data will allow life-time follow up of clinical outcomes for BiB children and their parents, and educational attainment for children.

e) Community involvement. Close links with members of the public and particularly with cohort members allow the co-production of research in terms of the identification of research questions, monitoring the demands research makes on participants and discussion of the implementation of findings. The study has strong community roots and city-wide support.

Full details of the cohort and related publications can be found on the website

Patient characteristics Children born in the city of Bradford Claims years: 2007-2011 12,453 women with 13,776 pregnancies and 3,448 of their partners Cord blood samples have been obtained and stored and DNA extraction on 10,000 mother\offspring pairs. Sex: Adults: 12,453 women, 3,448 males

Application

If you are interested in working with these data, the application packet, with examples, can be found here: Born in Bradford Application Packet

Brunei Population: Mid Year: By Race: Malay data was reported at 335.500 Person th in 2024. This records an increase from the previous number of 332.100 Person th for 2023. Brunei Population: Mid Year: By Race: Malay data is updated yearly, averaging 206.300 Person th from Jun 1972 (Median) to 2024, with 53 observations. The data reached an all-time high of 335.500 Person th in 2024 and a record low of 92.095 Person th in 1972. Brunei Population: Mid Year: By Race: Malay data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

Brunei Population: Mid Year: By Race: Malay: Female data was reported at 157.100 Person th in 2023. This records a decrease from the previous number of 160.900 Person th for 2022. Brunei Population: Mid Year: By Race: Malay: Female data is updated yearly, averaging 99.850 Person th from Jun 1972 (Median) to 2023, with 52 observations. The data reached an all-time high of 160.900 Person th in 2022 and a record low of 45.780 Person th in 1972. Brunei Population: Mid Year: By Race: Malay: Female data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Globe, the largest racial group is White alone with a population of 3,012 (78.19% 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 Globe
• Population: The population of the racial category (for Non-Hispanic) in the Globe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Globe 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 Globe Population by Race & Ethnicity. You can refer the same here

Brunei Population: Mid Year: By Race: Chinese: Female data was reported at 20.700 Person th in 2023. This records an increase from the previous number of 20.600 Person th for 2022. Brunei Population: Mid Year: By Race: Chinese: Female data is updated yearly, averaging 18.477 Person th from Jun 1972 (Median) to 2023, with 52 observations. The data reached an all-time high of 20.933 Person th in 2016 and a record low of 14.552 Person th in 1972. Brunei Population: Mid Year: By Race: Chinese: Female data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population.

Brunei Population: Mid Year data was reported at 455,500.000 Person in 2024. This records an increase from the previous number of 450,500.000 Person for 2023. Brunei Population: Mid Year data is updated yearly, averaging 309,500.000 Person from Jun 1972 (Median) to 2024, with 53 observations. The data reached an all-time high of 455,500.000 Person in 2024 and a record low of 141,980.000 Person in 1972. Brunei Population: Mid Year data remains active status in CEIC and is reported by Department of Economic Planning and Statistics, Ministry of Finance and Economy. The data is categorized under Global Database’s Brunei – Table BN.G001: Population. excludes Popolation: Mid Year: By Race: Other Indigenous (61657402)