16 datasets found
  1. N

    cities in Blue Earth County Ranked by Non-Hispanic Asian Population // 2025...

    • neilsberg.com
    csv, json
    Updated Feb 11, 2025
    + more versions
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    Neilsberg Research (2025). cities in Blue Earth County Ranked by Non-Hispanic Asian Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/cities-in-blue-earth-county-mn-by-non-hispanic-asian-population/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 11, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Blue Earth County, Minnesota
    Variables measured
    Non-Hispanic Asian Population, Non-Hispanic Asian Population as Percent of Total Population of cities in Blue Earth County, MN, Non-Hispanic Asian Population as Percent of Total Non-Hispanic Asian Population of Blue Earth County, MN
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    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/.

  2. N

    Globe, AZ Population Breakdown By Race (Excluding Ethnicity) Dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 21, 2025
    + more versions
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    Neilsberg Research (2025). Globe, AZ Population Breakdown By Race (Excluding Ethnicity) Dataset: Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/7573e287-ef82-11ef-9e71-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 21, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Arizona, Globe
    Variables measured
    Asian Population, Black Population, White Population, Some other race Population, Two or more races Population, American Indian and Alaska Native Population, Asian Population as Percent of Total Population, Black Population as Percent of Total Population, White Population as Percent of Total Population, Native Hawaiian and Other Pacific Islander Population, and 4 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the racial categories idetified by the US Census Bureau. It is ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories, and do not rely on any ethnicity classification. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    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

  3. d

    Data from: Datasets for transcriptomic analyses of maize leaves in response...

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    Updated Apr 21, 2025
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    Agricultural Research Service (2025). Data from: Datasets for transcriptomic analyses of maize leaves in response to Asian corn borer feeding and/or jasmonic acid [Dataset]. https://catalog.data.gov/dataset/data-from-datasets-for-transcriptomic-analyses-of-maize-leaves-in-response-to-asian-corn-b-d9ac5
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Agricultural Research Service
    Description

    Corn (Zea mays) is one of the most widely grown crops throughout the world. However, many corn fields develop pest problems such as corn borers every year that seriously affect its yield and quality. Corn's response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. This Data In Brief paper summarizes the datasets of the transcriptome of corn plants in response to corn stalk borers (Ostrinia furnacalis) and/or methyl jasmonate (MeJA). Altogether, 39, 636 genes were found to be differentially expressed. The sequencing data are available in the NCBI SRA database under accession number SRS965087. This dataset will provide more scientific and valuable information for future work such as the study of the functions of important genes or proteins and develop new insect-resistant maize varieties. Includes supplementary tables and data in fasta and GTF format. Resources in this dataset:Resource Title: Datasets for transcriptomic analyses of maize leaves in response to Asian corn borer feeding and/or jasmonic acid. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340916301792 Data in Brief Article including supplemental data in fasta and GTF format.

  4. h

    Central_Asian_Food_Scenes_Dataset

    • huggingface.co
    Updated Apr 30, 2025
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    Institute of Smart Systems and Artificial Intelligence, Nazarbayev University (2025). Central_Asian_Food_Scenes_Dataset [Dataset]. https://huggingface.co/datasets/issai/Central_Asian_Food_Scenes_Dataset
    Explore at:
    Dataset updated
    Apr 30, 2025
    Dataset authored and provided by
    Institute of Smart Systems and Artificial Intelligence, Nazarbayev University
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Central Asian Food Scenes Dataset

    In this work, we propose the first Central Asia Food Scenes Dataset that contains 21,306 images with 69,856 instances across 239 food classes. To make sure that the dataset contains various food items, we took as a benchmark the ontology of Global Individual Food Tool developed by Food and Agriculture Organization (FAO) together with the World Health Organization (WHO) [1]. The dataset contains food items across 18 coarse classes: 🍅 Vegetables •… See the full description on the dataset page: https://huggingface.co/datasets/issai/Central_Asian_Food_Scenes_Dataset.

  5. The potential impact of international migration on prospective population...

    • zenodo.org
    bin, csv, txt
    Updated Dec 8, 2024
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    Markus Dörflinger; Markus Dörflinger; Michaela Potančoková; Michaela Potančoková; Guillaume Marois; Guillaume Marois (2024). The potential impact of international migration on prospective population ageing in Asian countries: Code and datasets [Dataset]. http://doi.org/10.5281/zenodo.12705066
    Explore at:
    bin, csv, txtAvailable download formats
    Dataset updated
    Dec 8, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Markus Dörflinger; Markus Dörflinger; Michaela Potančoková; Michaela Potančoková; Guillaume Marois; Guillaume Marois
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Asia
    Description

    We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

    This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

    • Constant chronological old-age dependency ratio (Constant OADR scenario)
    • Constant prospective old-age dependency ratio (Constant POADR scenario)
    • Constant chronological working-age population (Constant WA scenario)
    • Constant prospective working-age population (Constant PWA scenario)
    • Zero-migration (ZM scenario)

    Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

    Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

    For detailed information about the scenarios and parameters:

    Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

    All underlying data (UN World Population Prospects 2022) are openly available at:

    https://population.un.org/wpp/Download/Archive

    Code

    1_Data.R:

    • Load and merge data from UN World Population Prospects 2022
    • Define sample
    • Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

    2_Scenarios.R:

    • Replacement migration scenarios:
      • Constant chronological old-age dependency
      • Constant prospective old-age dependency
      • Constant chronological working-age population
      • Constant prospective working-age population
    • Zero-migration scenario

    3_Robustness_checks.R:

    • Run replacement migrations scenarios with different model sex and age patterns for net migration

    Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

    Datasets

    The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

    Acknowledgements

    Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

  6. Mobile internet users in Southeast Asia 2010-2029

    • statista.com
    Updated Jul 1, 2025
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    Statista Research Department (2025). Mobile internet users in Southeast Asia 2010-2029 [Dataset]. https://www.statista.com/topics/9093/internet-usage-in-southeast-asia/
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    Dataset updated
    Jul 1, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    South East Asia, Asia
    Description

    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.

  7. California Commuting Mode Choice

    • kaggle.com
    Updated Jan 12, 2023
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    The Devastator (2023). California Commuting Mode Choice [Dataset]. https://www.kaggle.com/datasets/thedevastator/california-commuting-mode-choice-from-2000-2010
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 12, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Area covered
    California
    Description

    California Commuting Mode Choice

    Regional Disparities in Risk of Injury and Death

    By Health [source]

    About this dataset

    This dataset contains data on the modes of transportation used by California residents aged 16 and older to commute to work. It includes data from the U.S. Census Bureau, Decennial Census and American Community Survey, covering all regions, counties, cities/towns, and census tracts in California. With each region showing detailed information regarding how its population travels to work (modes of transportation used), this dataset provides vital insight into the development of transport infrastructure in California over the past decade.

    Unlike other states where private cars constitute an overwhelming majority of daily commuters (over 79% nationwide according to a 2015 survey), Californians have built up varied commuting habits – bicycles are commonly reported 5%, public transit stands at 15%, walking alone 4%, and carpooling is at 11%. Commuting plays a significant role on overall health—active modes such as biking or walking lead to healthier lifestyles that lower heart disease risks, obesity rates, diabetes prevalence; passengers on public transport also have a lower chance of injury in collisions compared with pedestrians or cyclists.

    The consequences of inadequate planning for human mobility extend beyond physical health – it can also cause huge disparities between different racial groups such as Native Americans who experience four times higher death rate from pedestrian-car collisions than Whites or Asians; African-Americans and Latinos suffer twice as much as White people do when driving privately in their own cars due to air pollution hazards or lack thereof access to reliable public transportation system that could provide them with healthier alternatives. It is our hope that policymakers will use this dataset prominently stated by the Healthy Communities Data & Indicators Project - part of the Office Of Health Equity - while ensuring every resident’s right for safe mobility no matter their background!

    More Datasets

    For more datasets, click here.

    Featured Notebooks

    • 🚨 Your notebook can be here! 🚨!

    How to use the dataset

    This dataset contains information on the percent of Californians aged 16 and older who use different modes of transportation to get to work. The data is collected from the U.S. Census Bureau and American Community Survey, and covers all counties, cities/towns and census tracts in California.

    In this dataset, there are several columns of data such as mode (mode of transport), race_eth_name (name of the race/ethnicity), region_code (code for the region) and pop_total (total population). This makes it possible to look at relations between transportation choice and demographic factors like gender or ethnicity, or comparison between regions within California regarding commuting habits.

    The purpose of this dataset is to provide information on how Californians travel to their jobs with respect to both geographical area as well as demographic characteristics. It allows studies into why certain areas might have higher usage rates for specific types of transport compared with others, how gender affects travel decisions, or which regions have access issues with public transit compared with driving for example.

    To use this dataset you should start by familiarizing yourself with descriptive statistics such as percentages, hazard ratios etc., in order to understand each variable's contribution towards commuting trends more effectively. It might also help if you filter data by geographic area or personal characteristics first before performing more detailed analysis for more insightful results that can be used in policy-making when planning effective infrastructure investments related to transportation options over time or among differing populations within California state population levels noted here year-by-year across a decade period provided here

    Research Ideas

    • Creating interactive maps to visualize and compare the transportation methods of different race/ethnicities in California.
    • Analyzing the transportation trends across regions, counties, cities/towns, and census tracts to forecast and plan for infrastructure investments.
    • Comparing the risk ratio of pedestrian-car fatalities across different ethnic groups in order to address safety issues within underserved populations

    Acknowledgements

    If you use this dataset in your research, please credit the original authors. Data Source

    License

    **License: [Open Database License (ODbL) v1.0](https://opendatacommons.org/lice...

  8. Import Export Data | Import, Export & Trade Professionals in Asia | Verified...

    • datarade.ai
    Updated Jan 1, 2018
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    Success.ai (2018). Import Export Data | Import, Export & Trade Professionals in Asia | Verified Global Profiles from 700M+ Dataset | Best Price Guarantee [Dataset]. https://datarade.ai/data-products/import-export-data-import-export-trade-professionals-in-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Jan 1, 2018
    Dataset provided by
    Area covered
    Azerbaijan, Brunei Darussalam, India, Lao People's Democratic Republic, Kuwait, Syrian Arab Republic, Afghanistan, Indonesia, Bhutan, Qatar
    Description

    Success.ai’s Import Export Data for Import, Export & Trade Professionals in Asia delivers a comprehensive dataset tailored for businesses aiming to connect with key players in Asia’s dynamic trade industry. Covering professionals involved in import/export operations, international logistics, and supply chain management, this dataset provides verified contact details, firmographic insights, and actionable professional data.

    With access to over 700 million verified global profiles and 70 million business datasets, Success.ai ensures your outreach, market research, and trade strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the complexities of global trade in Asia.

    Why Choose Success.ai’s Import Export Data?

    1. Verified Contact Data for Effective Engagement

      • Access verified work emails, phone numbers, and LinkedIn profiles of trade professionals, logistics experts, and supply chain managers.
      • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication efficiency.
    2. Comprehensive Coverage of Asian Trade Markets

      • Includes profiles of professionals from key Asian markets such as China, India, Japan, South Korea, and Southeast Asia.
      • Gain insights into regional trade trends, import/export regulations, and supply chain dynamics.
    3. Continuously Updated Datasets

      • Real-time updates capture changes in leadership roles, trade activities, and market expansions.
      • Stay aligned with evolving market conditions and emerging trade opportunities.
    4. Ethical and Compliant

      • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage for all business initiatives.

    Data Highlights:

    • 700M+ Verified Global Profiles: Connect with import/export professionals, logistics managers, and trade consultants across Asia.
    • 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
    • Contact Details: Gain verified work emails, phone numbers, and business locations for precise targeting.
    • Industry Trends: Understand key import/export opportunities, supply chain challenges, and market dynamics in Asia.

    Key Features of the Dataset:

    1. Professional Profiles in Import/Export and Logistics

      • Identify and engage with trade professionals managing cross-border operations, customs compliance, and supply chain efficiency.
      • Target individuals responsible for vendor selection, international partnerships, and trade negotiations.
    2. Firmographic and Geographic Insights

      • Access data on company structures, trade volumes, and operational hubs in key Asian markets.
      • Pinpoint high-value prospects in established and emerging trade routes for strategic engagement.
    3. Advanced Filters for Precision Campaigns

      • Filter professionals by industry focus (manufacturing, wholesale, retail), geographic location, or revenue size.
      • Tailor campaigns to address specific trade needs such as market entry, cost optimization, or regulatory compliance.
    4. AI-Driven Enrichment

      • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with trade professionals.

    Strategic Use Cases:

    1. Sales and Business Development

      • Present trade services, logistics solutions, or supply chain optimization tools to import/export managers and trade consultants.
      • Build relationships with procurement teams and logistics managers seeking reliable partners and innovative solutions.
    2. Market Research and Competitive Analysis

      • Analyze trends in Asia’s import/export landscape, including key trade routes, regulatory changes, and logistics challenges.
      • Benchmark against competitors to identify growth opportunities, underserved markets, and emerging needs.
    3. Partnership Development and Trade Collaboration

      • Engage with businesses seeking partnerships for supply chain management, customs compliance, or international expansion.
      • Foster alliances that enhance efficiency, reduce costs, and drive growth in the import/export sector.
    4. Recruitment and Talent Acquisition

      • Target HR professionals and hiring managers recruiting for roles in international trade, logistics, or operations.
      • Provide workforce optimization platforms or training solutions tailored to the trade and logistics industry.

    Why Choose Success.ai?

    1. Best Price Guarantee

      • Access premium-quality import/export data at competitive prices, ensuring maximum ROI for your marketing, sales, and trade initiatives.
    2. Seamless Integration

      • Integrate verified trade data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and ...
  9. f

    Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating...

    • figshare.com
    zip
    Updated Mar 29, 2024
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    Wu; Xu; Xiaoping Liu; Xuecao Li; Weilin Liao; Limin Jiao; Zhenzhong Zeng; Guangzhao Chen; Xia Li (2024). Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality [Dataset]. http://doi.org/10.6084/m9.figshare.21507537.v1
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    zipAvailable download formats
    Dataset updated
    Mar 29, 2024
    Dataset provided by
    figshare
    Authors
    Wu; Xu; Xiaoping Liu; Xuecao Li; Weilin Liao; Limin Jiao; Zhenzhong Zeng; Guangzhao Chen; Xia Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

  10. i

    Asian Barometer Survey 2010-2011 - World

    • dev.ihsn.org
    • catalog.ihsn.org
    Updated Apr 25, 2019
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    Institute of Political Science (2019). Asian Barometer Survey 2010-2011 - World [Dataset]. https://dev.ihsn.org/nada//catalog/73829
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    Dataset updated
    Apr 25, 2019
    Dataset provided by
    East Asia Democratic Studies
    Institute of Political Science
    Time period covered
    2010 - 2011
    Area covered
    World
    Description

    Abstract

    The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

    The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

    The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

    Geographic coverage

    13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

    Analysis unit

    -Individuals

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

    As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

    Research Protocols:

    • National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

    A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

    Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

    Cleaning operations

    Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.

  11. Genera Semagystia Schoorl and Dyspessa Hubner (Lepidoptera, Cossidae) in...

    • gbif.org
    Updated Apr 21, 2023
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    Roman V. Yakovlev; Artem E. Naydenov; Polina D. Pavlova; Roman V. Yakovlev; Artem E. Naydenov; Polina D. Pavlova (2023). Genera Semagystia Schoorl and Dyspessa Hubner (Lepidoptera, Cossidae) in Asian part of Russia and Central Asia [Dataset]. http://doi.org/10.15468/b3wkak
    Explore at:
    Dataset updated
    Apr 21, 2023
    Dataset provided by
    Global Biodiversity Information Facilityhttps://www.gbif.org/
    Altai State University
    Authors
    Roman V. Yakovlev; Artem E. Naydenov; Polina D. Pavlova; Roman V. Yakovlev; Artem E. Naydenov; Polina D. Pavlova
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Description

    The dataset contains an up-to-date list of species of genera Semagystia Schoorl and Dyspessa Hubner (Lepidoptera, Cossidae) in Asian part of Russia and Central Asia. The dataset is intended to provide all interested users of GBIF with information on the distribution of the genera within the Asian part of Russia and Central Asia, identifying the locations of storage of material in world collections and other types of analyzes (taxonomic, phenological, etc.) using the available datasets in GBIF.

  12. T

    GDP PER CAPITA by Country in ASIA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 26, 2017
    + more versions
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    TRADING ECONOMICS (2017). GDP PER CAPITA by Country in ASIA [Dataset]. https://tradingeconomics.com/country-list/gdp-per-capita?continent=asia
    Explore at:
    json, csv, xml, excelAvailable download formats
    Dataset updated
    May 26, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Area covered
    Asia
    Description

    This dataset provides values for GDP PER CAPITA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

  13. p

    Trends in Asian Student Percentage (1989-2023): Starbuck - An Ib World...

    • publicschoolreview.com
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    Public School Review, Trends in Asian Student Percentage (1989-2023): Starbuck - An Ib World School vs. Wisconsin vs. Racine Unified School District [Dataset]. https://www.publicschoolreview.com/starbuck-an-ib-world-school-profile
    Explore at:
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Racine School District, Wisconsin
    Description

    This dataset tracks annual asian student percentage from 1989 to 2023 for Starbuck - An Ib World School vs. Wisconsin and Racine Unified School District

  14. p

    Trends in Asian Student Percentage (2011-2023): Global Technology...

    • publicschoolreview.com
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    Public School Review, Trends in Asian Student Percentage (2011-2023): Global Technology Preparatory vs. New York vs. New York City Geographic District # 4 School District [Dataset]. https://www.publicschoolreview.com/global-technology-preparatory-profile
    Explore at:
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New York
    Description

    This dataset tracks annual asian student percentage from 2011 to 2023 for Global Technology Preparatory vs. New York and New York City Geographic District # 4 School District

  15. p

    Trends in Asian Student Percentage (1992-2023): Lake Arrowhead Elementary...

    • publicschoolreview.com
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    Public School Review, Trends in Asian Student Percentage (1992-2023): Lake Arrowhead Elementary School vs. California vs. Rim Of The World Unified School District [Dataset]. https://www.publicschoolreview.com/lake-arrowhead-elementary-school-profile
    Explore at:
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Rim of the World Unified School District, Lake Arrowhead, California
    Description

    This dataset tracks annual asian student percentage from 1992 to 2023 for Lake Arrowhead Elementary School vs. California and Rim Of The World Unified School District

  16. p

    Trends in Asian Student Percentage (1996-2023): Brooks Global Elementary...

    • publicschoolreview.com
    Updated Jul 7, 2017
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    Public School Review (2017). Trends in Asian Student Percentage (1996-2023): Brooks Global Elementary School vs. North Carolina vs. Guilford County Schools School District [Dataset]. https://www.publicschoolreview.com/brooks-global-elementary-school-profile
    Explore at:
    Dataset updated
    Jul 7, 2017
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    North Carolina, Guilford County Schools, Guilford County
    Description

    This dataset tracks annual asian student percentage from 1996 to 2023 for Brooks Global Elementary School vs. North Carolina and Guilford County Schools School District

  17. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Neilsberg Research (2025). cities in Blue Earth County Ranked by Non-Hispanic Asian Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/cities-in-blue-earth-county-mn-by-non-hispanic-asian-population/

cities in Blue Earth County Ranked by Non-Hispanic Asian Population // 2025 Edition

Explore at:
csv, jsonAvailable download formats
Dataset updated
Feb 11, 2025
Dataset authored and provided by
Neilsberg Research
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Blue Earth County, Minnesota
Variables measured
Non-Hispanic Asian Population, Non-Hispanic Asian Population as Percent of Total Population of cities in Blue Earth County, MN, Non-Hispanic Asian Population as Percent of Total Non-Hispanic Asian Population of Blue Earth County, MN
Measurement technique
To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
Dataset funded by
Neilsberg Research
Description
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/.

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