This dataset provides a comprehensive look at population and migration trends in five South Asian countries: Afghanistan, Bangladesh, India, Pakistan, and Sri Lanka, covering the years 1960 to 2023. The data is sourced directly from the World Bank API and contains detailed statistics on total population and net migration for each year.

This dataset is ideal for:

• Time-series analysis to study population trends over six decades.
• Migration studies to assess policy impacts and demographic shifts.
• Data visualization for dashboards and presentations.
• Machine learning applications in predictive analytics.

Columns: - Country: Name of the country. - Year: Year of the recorded data. - Total Population: The total population of the country. - Net Migration: Net migration balance (positive for immigration surplus, negative for emigration surplus).

Key Insights: - Afghanistan: Significant migration shifts due to conflicts and crises. - India: Continuous population growth with varying migration trends. - Bangladesh: A history of large emigration and its impact on demographics. - Pakistan: Migration surpluses in some years and large outflows in others. - Sri Lanka: Gradual population growth and consistent emigration patterns.

About this dataset

Context

This list ranks the 50 states in the United States by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each state 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
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Chinese Population: This column displays the rank of state in the United States by their Chinese population, using the most recent ACS data available.
• State: The State for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the state is shown in this column.
• % of Total State Population: This shows what percentage of the total state population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total United States Chinese Population: This tells us how much of the entire United States Chinese population lives in that state. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

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

United States Current Population Survey: Population: Asian data was reported at 15,934.000 Person th in Jun 2018. This records an increase from the previous number of 15,874.000 Person th for May 2018. United States Current Population Survey: Population: Asian data is updated monthly, averaging 10,833.000 Person th from Jan 2000 (Median) to Jun 2018, with 222 observations. The data reached an all-time high of 15,983.000 Person th in Mar 2018 and a record low of 8,992.000 Person th in Jan 2003. United States Current Population Survey: Population: Asian data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G007: Current Population Survey: Population.

Datasets provides long-term climate data for large Asian cities with populations over 500,000. The dataset includes data on cloud cover, temperature range, number of frost days, potential evapotranspiration, precipitation, minimum temperature, mean temperature, maximum temperature, relative humidity, and number of wet days. The dataset includes data for 831 cities.

Columns:

• ID
• Date
• Latitude
• Longitude
• cld: Cloud cover (%)
• dtr: Temperature range (°C)
• frs: Number of frost days
• pet: Potential evapotranspiration (mm)
• pre: Precipitation (mm)
• tmn: Minimum temperature (°C)
• tmp: Mean temperature (°C)
• tmx: Maximum temperature (°C)
• vap: Relative humidity (%)
• wet: Number of wet days

Inspiration:
Are you interested in predicting the future weather conditions in your city or one of the 831 cities in our climate dataset? Our climate dataset contains data on various climate metrics, including temperature, precipitation, cloud cover, wind speed, and humidity. This data can be used to train a machine learning model that can predict future weather conditions with high accuracy. Imagine using a machine learning model to predict the weather in your city for the next week, month, or year. This information could be used to make decisions about planning, adaptation, and risk mitigation.

Please note:
This dataset contains satellite-derived climate data from the website https://crudata.uea.ac.uk. Satellite data are measured using sensors that may be subject to error. Therefore, it is possible that these data may differ from ground-based observations, which are typically used to generate real-world data. This difference is generally greater in remote areas and regions with high cloud.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This dataset provides a detailed view of South Asian countries' socio-economic, environmental, and governance metrics from 2000 to 2023. It compiles key indicators like GDP, unemployment, literacy rates, energy use, governance measures, and more to facilitate a comprehensive analysis of each country’s growth, stability, and development trends over the years. The data covers Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and Maldives.

Key Indicators Economic Metrics: Includes GDP (both total and per capita in USD), annual GDP growth rates, inflation, and foreign direct investment. These metrics offer insight into economic health, growth rate, and international investment trends across the region. Employment and Trade: Tracks unemployment rates as a percentage of the labor force and trade (as a percentage of GDP), helping assess workforce stability and international commerce engagement. Income and Poverty: Features the Gini index (for income inequality) and poverty headcount ratio at $2.15/day, showing income distribution and poverty levels. These indicators reveal disparities and poverty within each country. Population Statistics: Includes total population, annual population growth, and urban population percentage, capturing demographic trends and urbanization rates. Social Indicators: Covers literacy rates, school enrollment in primary education, life expectancy at birth, infant mortality rates, and access to electricity, basic water, and sanitation services. These data points help measure the population’s health, education levels, and access to essential services. Environmental and Energy Metrics: Tracks CO2 emissions, PM2.5 air pollution, renewable energy consumption, and forest area. This environmental data is crucial for analyzing air quality, sustainable energy use, and forest coverage trends. Governance Indicators: Includes metrics such as control of corruption, political stability, regulatory quality, rule of law, and voice and accountability. These indicators reflect each country’s governance quality and institutional stability. Digital and Technological Growth: Measures internet usage rates, research and development spending, and high-technology exports. These statistics indicate digital access, innovation, and technological progress. This dataset, sourced from the World Bank DataBank, provides a robust foundation for studying South Asia's socio-economic, environmental, and governance progress. By analyzing these diverse indicators, researchers and policymakers can gain a deeper understanding of the region’s development path and identify areas that need improvement.

United States Employment: NH: Asian data was reported at 11,969.000 Person th in Apr 2025. This records an increase from the previous number of 11,948.000 Person th for Mar 2025. United States Employment: NH: Asian data is updated monthly, averaging 9,758.000 Person th from Jan 2016 (Median) to Apr 2025, with 112 observations. The data reached an all-time high of 11,969.000 Person th in Apr 2025 and a record low of 8,161.000 Person th in May 2020. United States Employment: NH: Asian data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Employment.

Introduction

The East Asian Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.

Facial Image Data

The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.

Diversity and Representation

Quality and Image Conditions

To ensure robust model training and generalizability, images are captured under varied natural conditions:

Metadata

Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:

This metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.

Applications

This dataset is ideal for a wide range of computer vision use cases, including:

Ethical Collection and Data Security

We maintain the highest ethical and security standards throughout the data lifecycle:

As of October 2025, there were 449 data centers in China, the most of any country or territory in the Asia-Pacific region. China had the fourth-highest number of data centers worldwide. Data centers in China As the leading market in public cloud in the Asia-Pacific region and an aspiring global leader in artificial intelligence, China has placed considerable weight on data center infrastructure, which underlies most of the advances in internet technology. The country dominates the global data center market in terms of revenue, trailing only the United States. In addition, China accounted for around 16 percent of the worldwide hyperscale data center capacity in the 4th quarter of 2023. The data center segment revenue in China is expected to have an annual growth rate of around 8.3 percent between 2025 and 2029. The outlook of data centers in the Asia-Pacific region The pandemic has accelerated enterprise digitalization across the Asia-Pacific region, driving a surge in demand for computational power. This trend, coupled with advancements in artificial intelligence and the region's significant population growth, points to a promising future for data centers in the region. For instance, the revenue in the data center market in India was forecast to grow further and is set to reach about 11.85 billion U.S. dollars by 2029. Meanwhile, economic growth and increasing internet penetration rates in Southeast Asian countries have been the primary drivers for data center demand growth in the subregion.

Introduction

Welcome to the South Asian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 8,000 facial image sets of South Asian individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Number of people who are undernourished in South Asia was reported at 217500000 in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Number of people who are undernourished - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Asian Alone (5-year estimate) in Blue Earth County, MN (B03002016E027013) from 2009 to 2023 about Blue Earth County, MN; Mankato; asian; latino; hispanic; MN; estimate; 5-year; persons; population; and USA.

Face Images Dataset

Description

10,109 people - face images dataset includes people collected from many countries. Multiple photos of each person’s daily life are collected, and the gender, race, age, etc. of the person being collected are marked.This Dataset provides a rich resource for artificial intelligence applications. It has been validated by multiple AI companies and proves beneficial for achieving outstanding performance in real-world applications. Throughout the process of Dataset collection, storage, and usage, we have consistently adhered to Dataset protection and privacy regulations to ensure the preservation of user privacy and legal rights. All Dataset comply with regulations such as GDPR, CCPA, PIPL, and other applicable laws. For more details, please refer to the link: https://www.nexdata.ai/datasets/computervision/1402?source=Kaggle

Data size

10,109 people, no less than 30 images per person

Race distribution

3,504 black people, 3,559 Indian people and 3,046 Asian people

Gender distribution

4,930 males, 5,179 females

Age distribution

most people are young aged, the middle-aged and the elderly cover a small portion

Collecting environment

including indoor and outdoor scenes

Data diversity

different face poses, races, accessories, ages, light conditions and scenes

Data format

.jpg, .png, .jpeg

Licensing Information

Commercial License

Population density (people per sq. km of land area) in South Asia was reported at 492 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Content

This dataset contains Covid-19 data of world countries as on November 08, 2021

## Attribute Information

• Country - Name of world countries
• Total Cases - Total number of Covid-19 cases
• Total Deaths - Total number of Deaths
• Total Recovered - Total number of recovered cases
• Active Cases - Total number of Active cases
• Total Cases/1 mil population- Total Cases per 1 million of the population
• Death/1 mil population - Total Deaths per 1 million of the population
• Total Tests - Total number of Covid tests done
• Tests/1 mil population - Covid tests done per 1 million of the population
• Population - Population of the country

Source

Link : https://www.worldometers.info/coronavirus/#countries

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The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

The founding of New World populations by Asian peoples is the focus of considerable archaeological and genetic research, and there persist important questions on when and how these events occurred. Genetic data offer great potential for the study of human population history, but there are significant challenges in discerning distinct demographic processes. A new method for the study of diverging populations was applied to questions on the founding and history of Amerind-speaking Native American populations. The model permits estimation of founding population sizes, changes in population size, time of population formation, and gene flow. Analyses of data from nine loci are consistent with the general portrait that has emerged from archaeological and other kinds of evidence. The estimated effective size of the founding population for the New World is fewer than 80 individuals, approximately 1% of the effective size of the estimated ancestral Asian population. By adding a splitting parameter to population divergence models it becomes possible to develop detailed portraits of human demographic history. Analyses of Asian and New World data support a model of a recent founding of the New World by a population of quite small effective size.

United States Employment: Asian: Male data was reported at 6,551.000 Person th in Apr 2025. This records an increase from the previous number of 6,499.000 Person th for Mar 2025. United States Employment: Asian: Male data is updated monthly, averaging 4,143.500 Person th from Jan 2000 (Median) to Apr 2025, with 304 observations. The data reached an all-time high of 6,551.000 Person th in Apr 2025 and a record low of 3,016.000 Person th in Nov 2003. United States Employment: Asian: Male data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Employment.

I applied bits of text mining, natural langauge processing, and data science to a pair of annual editions of Race and Ethnic Relations, and below is a summary of what I learned.