About this dataset

Context

The dataset tabulates the White Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of White Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of White Earth was 93, a 0% decrease year-by-year from 2022. Previously, in 2022, White Earth population was 93, a decline of 4.12% compared to a population of 97 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Earth increased by 28. In this period, the peak population was 99 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the White Earth is shown in this column.
• Year on Year Change: This column displays the change in White Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Year. You can refer the same here

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

As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

About this dataset

Context

The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.

Key observations

Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Earth is shown in the following column.
• Population (Female): The female population in the White Earth is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Earth for each age group.

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 Gender. 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 White 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 White Population: This column displays the rank of cities in the Blue Earth County, MN by their Multi-Racial White population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial White Population: The Multi-Racial White 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 White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Multi-Racial White Population: This tells us how much of the entire Blue Earth County, MN Multi-Racial White 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 White Earth population by age. The dataset can be utilized to understand the age distribution and demographics of White Earth.

Content

The dataset constitues the following three datasets

• White Earth, ND Age Group Population Dataset: A complete breakdown of White Earth age demographics from 0 to 85 years, distributed across 18 age groups
• White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in White Earth - Population and Percentage Analysis
• White Earth, ND Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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

Key observations

The percent distribution of White Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 100% are white.

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 (excluding ethnicity) for the White Earth
• Population: The population of the racial category (excluding ethnicity) 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 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

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

Estimates presented as 95% confidence/credible interval range (average or most probable number). Direct enumeration indicated by (E); JS indicates a Jolly-Seber based open population mark/recapture model was used; n/a = not applicable to study.

This is a late July 2013 YouGov political tracker survey combining data on attitudes to race and immigration with questions on mobility history as well as voting intention, media consumption and other background variables. Data is also geocoded to ward level and ward-level census variables appended. The quantitative research will be based on ONS longitudinal survey and census data, as well the large-scale Citizenship Surveys and Understanding Society surveys. We will identify individual respondents from the quantitative research and explore their responses through qualitative work, in the form of three focus groups - two in Greater London, one in Birmingham. These will probe connections between respondents' local and national identities, their intentions to move neighbourhood, and their opinions on immigration, interethnic relations, community cohesion and voting behaviour.In the past decade in Britain, the 'white working-class' has been the focus of unprecedented media and policy attention. While class is a longstanding discursive category, the prefix 'white' is an important rider. We live in an era of global migration. Population pressure from the global South, and demand for workers in the developed North, will power what some term a 'third demographic transition' involving significant declines in the white majority populations of the western world (Coleman 2010). In the UK, the upsurge in diversity arguably presents a greater challenge for the working-class part of the white British population than for the middle class. Why? First, because for lower-status members of dominant groups, their ethnic identity tends to be their most prestigious social identity (Yiftachel 1999). Second, minorities tend to be from disadvantaged backgrounds and are therefore more likely to compete for housing and jobs with the white working class. Finally, because the white working-class is less comfortable navigating the contours of the new global knowledge economy than the middle class, it is more attached to existential securities rooted in the local and national context (Skey 2011). How might the white working class respond to increasing diversity? Drawing upon Albert O. Hirschman's classic book Exit, Voice and Loyalty (1970), we posit three possible responses: 'exit', 'voice' and 'accommodation.' The first possibility is white 'exit': geographic segregation, or, in the extreme, 'white flight'. A second avenue is 'voice': spearheading an identity politics based on opposition to immigration and voting for white nationalist parties. A third possibility is accommodation, in which members of the white working-class become more comfortable with elevated levels of ethnic diversity in their neighbourhood and nation. From exploratory research and existing literature, we suggest that a three-stage pattern of voice, exit and accommodation may be a useful way of thinking about white working-class responses to diversity in the UK. In other words, initial diversity meets strong white working-class resistance, expressed in attitudes and voting. This is followed by a degree of white out-migration, and then by a decline in anti-immigration sentiment and far right voting. Yet these broad patterns require finer-grained analysis that takes both individual characteristics and local context into account. This project will test these propositions through quantitative and qualitative research. There are three major dimensions of white working class attitudes and behaviour we seek to explain. Namely, whether members of the white working-class: 1) are more likely than other groups to leave or avoid areas with large or growing minority populations; 2) oppose immigration more strongly if they reside in diverse or ethnically changing wards and local authorities; and 3) support far right parties more if they reside in diverse or ethnically changing wards and local authorities. A central question we seek to answer is whether inter-ethnic contact reduces white working-class antagonism toward minorities (the contact hypothesis), or whether increased diversity leads to white flight, leaving relatively tolerant whites remaining in diverse neighbourhoods. The latter, 'hydraulic' process mimics the contact hypothesis but does not signify increased accommodation. Telephone interview of 1869 individuals (YouGov) in Britain. Further details available in the YouGov Archive Birbeck results pdf which is available in the related resources section of this project record.

The third Gaia Data Release has provided the astronomical community with astrometric data of more than 1.8 billion sources, and low resolution spectra for 220 million. Such a large amount of data is difficult to handle by means of visual inspection. In the last years, artificial intelligence and machine learning algorithms have started to be applied in astronomy for data analysis and automatic classification, with excellent results. In this work, we present a spectral analysis of the Gaia white dwarf population up to 500pc from the Sun based on artificial intelligence algorithms to classify the sample into their main spectral types and subtypes. In order to classify the sample, which consists of 78920 white dwarfs with available Gaia spectra, we have applied a Random Forest algorithm to the Gaia spectral coefficients. We used the Montreal White Dwarf Database of already labeled objects as our training sample. The classified sample is compared with other already published catalogs and with our own higher resolution Gran Telescopio Canarias (GTC) spectra, enabling the construction of a golden sample of well-classified objects. The Random Forest spectral classification of the 500-pc white dwarf population achieves an excellent global accuracy of 0.91 and an F1-score of 0.88 for the DA versus non-DA classification. In addition, we obtain a very high accuracy of 0.76 and a global F1-score of 0.62 for the non-DA subtype classification. In particular, our classification shows an excellent recall for DAs, DBs and DCs (>90%), and a very good precision (>=80%) for DQs, DZs and DOs. Unfortunately, our algorithm does not perform well in correctly classifying subtypes given the low resolution of the Gaia spectra. The use of machine learning techniques, particularly the Random Forest algorithm, has enabled us to spectrally classify 78920 white dwarfs - an increase of 543.6% over those previously labeled - with reasonable accuracy. Having an estimate of the spectral type for the vast majority of white dwarfs up to 500pc provides the possibility of making better estimates of cooling ages, star formation rates, and stellar evolution processes, among other fundamental aspects for the study of the white dwarf population.

This Dataset shows some basic demographic data from the US census located around the San Francisco MSA at tract level. Attributes include Average age, female and male population, white population, hispanic population, population density, and total population.

The Portal Project Teaching Database is a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The database is currently available in csv, json, and sqlite. This database is not designed for research as it intentionally removes some of the real-world complexities. The original database is published at Ecological Archives(http://esapubs.org/archive/ecol/E090/118/) and this version of the database should be used for research purposes. The Python code used for converting the original database to this teach version is included as 'create_portal_teach_dataset.py'. Suggested changes or additions to this dataset can be requested or contributed in the project GitHub repository(https://github.com/weecology/portal-teachingdb).

About this dataset

Context

The dataset tabulates the White Earth population by race and ethnicity. The dataset can be utilized to understand the racial distribution of White Earth.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• White Earth, ND Population Breakdown by Race
• White Earth, ND Non-Hispanic Population Breakdown by Race
• White Earth, ND Hispanic or Latino Population Distribution by Their Ancestries

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/.

This dataset displays countries that had ten percent or more of their population age 65 and older. This data was collecte through agingstats.gov.

This data set illustrates the world's wine production in hundreds of liters, by country and year. A value of -1 means that no data was available. Source URL: http://www.swivel.com/data_sets/show/1001590 Date Accessed: October 9th, 2007

This dataset displays the number of television receivers by country for the time period covering 1990 through 1997. Covered throughout this dataset is 150+ countries, This dataset was gathered from the United Nations Statistics Division. http://unstats.un.org/unsd/databases.htm Access Date: October 31, 2007

This dataset displays the annual pork exports of the United states. The data is displayed by country on a scale of carcass weight by 1000 pounds. The time period covered is 2003 to Jan 2008

This dataset displays the amount of hydroelectric power that was consumed on a nation level. The dataset covers the time period spanning from 1980 to 2005. Data is available for 200+ countries. This data is scalled at: Billion Kilowatt hours. Data references:Energy Information Administration International Energy Annual 2005 Table Posted: September 11, 2007 Next Update: June 2008 This data is available directly at: http://www.eia.doe.gov/fuelrenewable.html Access Date: November 8, 2007.

This dataset provides highly detailed (Block Level) views of various demographics for Manhattan, New York city. this dataset includes information on age, race, sex, income, housing, and various other attributes. This data comes from the 2000 Us Census and was joined to the Census Tiger line files to create the output. enjoy!