About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by Non-Hispanic Asian 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 Non-Hispanic Asian Population: This column displays the rank of cities in the Blue Earth County, MN by their Non-Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Asian Population: The Non-Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Non-Hispanic Asian Population: This tells us how much of the entire Blue Earth County, MN Non-Hispanic Asian 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/.

This dataset tracks annual asian student percentage from 2007 to 2023 for World Journalism Preparatory A College Board School vs. New York and New York City Geographic District #25 School District

This dataset tracks annual asian student percentage from 1999 to 2023 for World Learner Charter School District vs. Minnesota

The number of smartphone users in Southeast Asia was forecast to continuously increase between 2024 and 2029 by in total 105.9 million users (+23.9 percent). After the nineteenth consecutive increasing year, the smartphone user base is estimated to reach 548.92 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Western Asia and Southern Asia.

This dataset tracks annual asian student percentage from 2019 to 2023 for Citizens Of The World Charter School Mar Vista vs. California and Citizens Of The World Charter School Mar Vista School District

This dataset tracks annual asian student percentage from 2007 to 2023 for World Academy vs. California and Oakland Unified School District

The number of internet users in Southeast Asia was forecast to continuously increase between 2024 and 2029 by 86.4 million users (+15.32 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach a new peak at 650.4 million in 2029. Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the data source clarifies, connection quality and usage frequency are distinct aspects, not taken into account here. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic, and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press, and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like Central Asia and Eastern Asia.

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

This dataset tracks annual asian student percentage from 2021 to 2023 for Citizens Of The World Charter School West Valley vs. California and Citizens Of The World Charter School West Valley School District

The ferns, lycophytes and seed-free vascular plants commonly described as pteridophytes exhibit hyperdiversity in the insular vegetation that often characterizes Asian floras. Despite harboring biodiversity hotspots, these plants and their georegions have been poorly surveyed, particularly in Southeast Asia, where one third of the world's pteridophyte species are concentrated. More than 60 per cent of the approximately 4,500 species lack georeferenced records in GBIF and only 6 per cent have been DNA barcoded.

This project aims to increase the available knowledge on Asian pteridophytes by compiling a georeferenced occurrence dataset that includes images, DNA barcodes and other vouchering information from thousands of recent collections, building on the efforts of the Taiwan Pteridophyte Research Group and its Southeast Asian collaborators. The project team will set up a workflow incorporating next-generation sequencing for 1,500 Asian pteridophyte specimens from selected collections in Taiwan, Vietnam, the Philippines, Malaysia and other Southeast Asian countries that can fill in taxonomic and geographic gaps and represent Asian pteridophyte diversity.

Mobilization of and access to these vouchered and georeferenced DNA-derived records will advance further research into the biogeography of pteridophytes and other terrestrial vegetation and support the development of novel approaches to monitor biodiversity along the spatiotemporal scale, including metabarcoding of the invisible diversity held in soil and spore banks.

About this dataset

Context

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

Key observations

The percent distribution of Black Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 94.39% are white, 0.18% are Black or African American, 0.53% are Asian and 4.90% are multiracial.

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

This dataset tracks annual asian student percentage from 1991 to 2023 for Robert Randall World Languages vs. California and Milpitas Unified School District

This dataset tracks annual asian student percentage from 2009 to 2023 for World Language Academy vs. Georgia and Hall County School District

The number of international tourist arrivals in Asia was forecast to continuously increase between 2024 and 2029 by in total 174.7 million arrivals (+33.49 percent). After the ninth consecutive increasing year, the arrivals is estimated to reach 696.34 million arrivals and therefore a new peak in 2029. Depicted is the number of inbound international tourists. According to World Bank this refers to tourists travelling to a country which is not their usual residence, whereby the main purpose is not work related and the planned visitation period does not exceed 12 months. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of international tourist arrivals in countries like North America and Caribbean.

About this dataset

Context

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

Key observations

The percent distribution of Black Earth town population by race (across all racial categories recognized by the U.S. Census Bureau): 95.17% are white, 2.42% are Asian and 2.42% are multiracial.

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

This dataset tracks annual asian student percentage from 2011 to 2023 for New World Preparatory Charter School District vs. New York

This dataset tracks annual asian student percentage from 2019 to 2023 for Our World Neighborhood Charter School 2 School District vs. New York

This report provides a snapshot of adolescent girls' digital literacy across the East Asia and Pacific region with a special focus on Cambodia, Indonesia, Lao PDR, Timor-Leste and Viet Nam.

Digital literacy is critical to participation in today’s world and by the year 2030, up to 80 per cent of jobs in Southeast Asia will require basic digital literacy and applied information and communication technology (ICT) skills. In 2022, an estimated 73 per cent of youth aged 15–24 years in the Asia Pacific region used the internet, but use and digital competences vary by gender across the region.

UNICEF calls on stakeholders to address the gender digital divide by supporting the empowerment of girls to develop advanced digital competencies safely and by ensuring both girls and boys have increased access to affordable internet and digital devices.

This file ‘all_areas_dataframe_renewables_and_non_renewables.xlsx’ is the result of the notebook https://www.kaggle.com/code/fords001/renewable-and-non-renewable-electricity-resources . It contains information from the years 2000 to 2023 and includes 18 sheets: for the percentage of electricity generation and for electricity generation in terawatt-hours (TWh) for each of the following world regions: Africa, Europe, Asia, North America, Latin America and the Caribbean, Oceania, as well as for the entire world. Each region has 11 columns representing different sources of electricity generation: Non-Renewables: Coal, Gas, Nuclear, Other Fossil (4 columns), Renewables: Bioenergy, Hydro, Solar, Wind, Other Renewables (5 columns). For each world region, we have two additional columns: Total Non-Renewables (1 column) and Total Renewables (1 column), which will be the sum of the related electricity generation columns .

List of dataframes : 'All_Areas_Common_Percent ' - Percentage dataframe for all areas 'All_Areas_Common_TWh' - Terawatt-hours dataframe for all areas 'All_Areas_Percent_Ren_Non_R' - Percentage df for all areas for 2 columns(Non-Renewables , Renewable) 'All_Areas_TWh_Ren_and_Non_R' - TWh df for all areas for 2 columns(Non-Renewables , Renewable) 'World_DF_Percent' - World dataframe Percentage 'World_DF_TWh' - World dataframe Terawatt-hours 'Africa_DF_Percent' - Africa dataframe Percentage 'Africa_DF_TWh' - Africa dataframe Terawatt-hours 'Europe_DF_Percent' - Europe dataframe Percentage 'Europe_DF_TWh' - Europe dataframe Terawatt-hours 'Asia_DF_Percent' - Asia dataframe Percentage 'Asia_DF_TWh' - Asia dataframe Terawatt-hours 'North_America_DF_Percent' - North America dataframe Percentage 'North_America_DF_TWh' - North America dataframe Terawatt-hours 'Latin_America_and_C_DF_Percent' - World dataframe Percentage 'Latin_America_and_C_DF_Twh' - World dataframe Terawatt-hours 'Oceania_DF_Percent' - Oceania dataframe Percentage 'Oceania_DF_TWh' - Oceania dataframe Terawatt-hours

In this data analysis I used the dataset ‘yearly_full_release_long_format.csv’, from https://ember-energy.org/data/yearly-electricity-data/ .It has a license (Creative Commons Attribution Licence (CC-BY-4.0). This license means. Share — copy and redistribute the material in any medium or format for any purpose, even commercially. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. These are the links to the license description . https://ember-energy.org/creative-commons/ and https://creativecommons.org/licenses/by/4.0/

This dataset tracks annual asian student percentage from 2007 to 2023 for High School Of World Cultures vs. New York and New York City Geographic District #12 School District