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

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

Key observations

The percent distribution of Globe population by race (across all racial categories recognized by the U.S. Census Bureau): 58.09% are white, 2.70% are Black or African American, 5.26% are American Indian and Alaska Native, 2.92% are Asian, 0.12% are Native Hawaiian and other Pacific Islander, 11.37% are some other race and 19.54% 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 Globe
• Population: The population of the racial category (excluding ethnicity) 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 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

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 2019 to 2023 for Citizens Of The World Charter School Silver Lake vs. California and Citizens Of The World Charter School Silver Lake School District

This dataset tracks annual asian student percentage from 1991 to 2023 for Park Center Ib World School vs. Minnesota and Osseo Public School District

This dataset tracks annual asian student percentage from 2014 to 2023 for World View High School vs. New York and New York City Geographic District #10

This dataset tracks annual asian student percentage from 2022 to 2023 for Citizens Of The World Charter School East Valley vs. California and Citizens Of The World Charter School East Valley School District

Women Education Rate in South Asia (1960-2010)

Data sourced from the (World Bank), analyzing the percentage of women without education across South Asia over five decades, for different age groups.

Dataset Overview

This dataset contains information on the percentage of women aged 15 and older with no education across South Asian countries from 1960 to 2010. The data is recorded every five years, capturing the changes in education levels for women in countries like Afghanistan, Bangladesh, India, Pakistan, Nepal, and Sri Lanka.

The dataset includes the percentage of women without education in the following age groups with details of countries and years:

Columns

• 15-19 years
• 20-24 years
• 25-29 years
• 30-34 years
• 35-39 years
• 40-44 years
• 45-49 years
• 50-54 years
• 55-59 years
• 60-64 years
• 65-69 years
• 70-74 years
• Above 75 years
• Country: The country in South Asia.
• Year: The year in which the data was recorded (every 5 years from 1960 to 2020).

The data provides valuable insights into the education gaps faced by women of different age groups in South Asia, and how these gaps have evolved over time. It helps in analyzing regional differences, trends over time, and the impact of education policies on women's educational outcomes.

Usage

This dataset can be used for:

• Trend analysis on the improvement or decline of female education across different age groups.
• Comparative analysis between countries in South Asia.
• Understanding the socio-economic factors influencing female education rates in various age groups.

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 1992 to 2023 for Rim Of The World Unified School District vs. California

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

About this dataset

Context

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

Key observations

The percent distribution of Blue Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 82.59% are white, 1.07% are Black or African American, 0.32% are American Indian and Alaska Native, 1.83% are Asian, 5.36% are some other race and 8.83% 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 Blue Earth
• Population: The population of the racial category (excluding ethnicity) in the Blue Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Blue 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 Blue Earth Population by Race & Ethnicity. You can refer the same here

This dataset tracks annual asian student percentage from 2013 to 2023 for Meridian World School Llc vs. Texas and Meridian World School LLC School District

This dataset tracks annual asian student percentage from 2016 to 2023 for Light Of The World Academy vs. Michigan and Light Of The World Academy School District

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/

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.

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 &

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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...

This dataset tracks annual asian student percentage from 1991 to 2023 for Top Of The World Elementary School vs. California and Laguna Beach Unified School District

Time series data for the statistic Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports) and country Belarus. Indicator Definition:Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.The indicator "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)" stands at 7.74 as of 12/31/2023, the highest value at least since 12/31/1993, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 15.67 percent compared to the value the year prior.The 1 year change in percent is 15.67.The 3 year change in percent is 75.60.The 5 year change in percent is 139.50.The 10 year change in percent is 198.31.The Serie's long term average value is 3.05. It's latest available value, on 12/31/2023, is 154.11 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1995, to it's latest available value, on 12/31/2023, is +742.20%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

This dataset tracks annual asian student percentage from 1994 to 2023 for Mary P. Henck Intermediate vs. California and Rim Of The World Unified School District