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.

Context The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion in 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growing more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

Approximately, 21 million people worldwide could be affected by river floods on average each year, and the 15 countries with the most people exposed, including India, Bangladesh, China, Vietnam, Pakistan, Indonesia, Egypt, Myanmar, Afghanistan, Nigeria, Brazil, Thailand, Democratic Republic of Congo, Iraq, and Cambodia, account for nearly 80 percent of the total population affected in an average year. Summary The Aqueduct Global Flood Risk Country Ranking ranks 163 countries by their current annual average population affected by river floods using the Aqueduct Global Flood Analyzer. Approximately, 21 million people worldwide could be affected by river floods on average each year, and the 15 countries with the most people exposed, including India, Bangladesh, China, Vietnam, Pakistan, Indonesia, Egypt, Myanmar, Afghanistan, Nigeria, Brazil, Thailand, Democratic Republic of Congo, Iraq, and Cambodia, account for nearly 80 percent of the total population affected in an average year. A country-wide estimated average flood protection level was given to each country based on its income level. Cautions Assumption: We assigned a country-wide average flood protection level for each country based on its income level (World Bank). 1) For low-income countries, we assume 10-year flood protection; 2) for lower-middle income countries, we assume 25-year flood protection; 3) for upper-middle income countries, we assume 50-year flood protection; 4) for high-income countries, we assume 100-year flood protection; and 5) for the Netherlands, we assume a 1000-year flood protection. Citation

Introduction

The South 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:

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:

Based on a large number of historical records and previous studies, we first estimated the historical population of South Asia (including India, Pakistan, Nepal, and Bangladesh) for AD 640-1871, and then calculated the per capita cropland area of South Asia from 640 to 1871 through some reliable historical archives at several time points. Then, by multiplying the historical per capita cropland area by the number of people, the cropland area from 640 to 1871 AD was estimated, and it was connected with the official cropland area statistics from 1900 to 2016 to obtain the cropland area in South Asia from 640 to 2016. Finally, according to the topography, soil and climate characteristics of South Asia, we evaluated the land suitability for cultivation and constructed the spatial reconstruction model of historical cropland in South Asia, and the estimated cropland area was input into the model, and the 1km cropland dataset from 640 to 2016 in South Asia was obtained. Compared with the global historical land use datasets HYDE and KK10, this dataset can more realistically reflect the history of cropland change in South Asia, and can be used to explore the impact of cropland change in South Asia on carbon emissions, climate change, biodiversity and ecosystem services changes in the past millennium.

Monthly data on remittance inflow to South Asian countries (Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka) from their partner countries is collected from January 2018 to December 2022 from the Central Bank database. As an alternative to monthly GDP data, monthly Industrial Production Index (IPI) data is used instead as a proxy for GDP. This is because monthly GDP data is not available. Monthly IPI data was collected from International Financial Statistics by the International Monetary Fund (IMF) for South Asian countries and partner countries (Singapore, Malaysia, Japan, Italy, and the UK). Libya and Middle Eastern nations, however, don't have monthly IPI statistics. Since the economies of those countries are heavily dependent on oil production, we created the Oil Production Index as a proxy for GDP. World Bank and EIA monthly crude oil price and production data are used to calculate Oil Production Index. Distance and standard gravity control variables like population, contiguity, and common language are taken from the Dynamic Gravity datasets constructed by the United States International Trade Commission. Migration stock data is collected from the Bureau of Manpower Employment and Training (BMET) and the International Organisation of Migration (IOM). We collect exchange rate data from the Central Bank dataset. To tackle the issue of different currency units, a Bilateral Exchange Rate Index (BERI) is constructed, where the exchange rate of each month for each country is divided by the exchange rate of the base year of that particular country. Furthermore, COVID cases, COVID mortality, and COVID vaccination data are collected from the Our World in Data website.

Introduction

Welcome to the South Asian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

this is the data of Top 10 populated countries of world as on 30 March 2024 with history of their population from 1955. it also have forecasted population values of these countries from 2025 to 2050.

here are the detail of columns

1: year:1955 to 2050

2: India: (population in millions)

3: china: (population in millions)

4: USA: (population in millions)

5: Indonesia: (population in millions)

6: Pakistan: (population in millions)

7: Nigeria: (population in millions)

8: Brazil: (population in millions)

9: Bangladesh: (population in millions)

10: Russia: (population in millions)

11: Mexico: (population in millions)

Acknowledgement This Dataset is created from https://www.worldometers.info/. If you want to learn more, you can visit the Website.

South Asia is one of the most densely populated regions in the world. This dataset comprehensively collects historical materials related to the population of South Asia and previous research results (see data description documents and references for details), carefully examines and estimates the population of South Asia (now India, Pakistan, Nepal, Bangladesh) from 640 to 1801 AD, and connects it with the population census data of British India from 1871 to 1941 (Nepal's data comes from Nepal's census data) and the United Nations World Population Prospects data from 1950 to 2020, obtaining the population of South Asia for a total of 22 periods (640, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1595, 1750, 1801, 1871, 1901, 1921, 1941, 1960, 1980, 2000, 2010, 2020) from 640 to 2020. Next, based on geographic detectors, select the dominant environmental factors that affect the spatial distribution of population, collect historical data on the distribution of residential areas (see data description document and references for details), and use a random forest regression model to spatialize the population size. On the basis of excluding uninhabited areas such as water bodies, glaciers, and bare/unused land, and determining the maximum historical population distribution range, a 1km resolution population dataset for South Asia from 640 to 2020 was developed. The leave one method was used to test the model, and the variance explained was 0.81, indicating good model accuracy. Compared with the existing HYDE historical population dataset, this study incorporates more historical materials and the latest research results in estimating the historical population; In using random forest regression for historical population spatial simulation, this study considers the changes in South Asian settlements over the past millennium, while the HYDE dataset only considers natural elements and considers them stable and unchanged. Therefore, this dataset is more reliable than the HYDE dataset and can more reasonably reveal the spatiotemporal characteristics of population changes in South Asia during historical periods. It is the basic data for the long-term evolution of human land relations, climate change attribution, and ecological protection research in South Asia.

Foreign Exchange Reserves in Bangladesh increased to 32335.20 USD Million in October from 31426.80 USD Million in September of 2025. This dataset provides - Bangladesh Foreign Exchange Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

Welcome to the South Asian Facial Expression Image Dataset, curated to support the development of advanced facial expression recognition systems, biometric identification models, KYC verification processes, and a wide range of facial analysis applications. This dataset is ideal for training robust emotion-aware AI solutions.

Facial Expression Data

The dataset includes over 2000 high-quality facial expression images, grouped into participant-wise sets. Each participant contributes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure generalizability and robustness in model training, images were captured under varied real-world conditions:

Metadata

Each participant's image set is accompanied by detailed metadata, enabling precise filtering and training:

This metadata helps in building expression recognition models that are both accurate and inclusive.

Use Cases & Applications

This dataset is ideal for a variety of AI and computer vision applications, including:

Secure & Ethical Collection

Dataset Updates & Customization

To support evolving AI development needs, this dataset is regularly updated and can be tailored to project-specific requirements. Custom options include:

Introduction

Welcome to the South Asian Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 5,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Linearized FST distances between Mauritius and the different South Asian sub-regions (MAU  =  Mauritius; PWI  =  Pakistan and West India; SWI  =  Southwest India; NI  =  North India; SEI  =  Southeast India; BEI  =  Bangladesh and East India).

Context:

A comprehensive dataset covering key socio-economic, environmental, and governance indicators of South Asian countries from 2000 to 2023. The dataset includes GDP, unemployment, literacy rates, energy usage, governance metrics, and more, enabling in-depth analysis of growth, stability, and development in the region.

Source:

The World Bank DataBank

South Asian Countries:

Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and the Maldives.

Column Description:

• GDP (current USD)
• GDP growth (annual %)
• GDP per capita (current USD)
• Unemployment, total (% of total labor force) (modeled ILO estimate)
• Inflation, consumer prices (annual %)
• Foreign direct investment, net inflows (% of GDP)
• Trade (% of GDP)
• Gini index
• Population, total
• Population growth (annual %)
• Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)
• Life expectancy at birth, total (years)
• Mortality rate, infant (per 1,000 live births)
• Literacy rate, adult total (% of people ages 15 and above)
• School enrollment, primary (% gross)
• Urban population (% of total population)
• Access to electricity (% of population)
• People using at least basic drinking water services (% of population)
• People using at least basic sanitation services (% of population)
• Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita)
• PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)
• Renewable energy consumption (% of total final energy consumption)
• Forest area (% of land area)
• Control of Corruption: Percentile Rank
• Political Stability and Absence of Violence/Terrorism: Estimate
• Regulatory Quality: Estimate
• Rule of Law: Estimate
• Voice and Accountability: Estimate
• Individuals using the Internet (% of population)
• Research and development expenditure (% of GDP)
• High-technology exports (% of manufactured exports)

BackgroundMulti-drug resistant typhoid fever remains an enormous public health threat in low and middle-income countries. However, we still lack a detailed understanding of the epidemiology and genomics of S. Typhi in many regions. Here we have undertaken a detailed genomic analysis of typhoid in urban Dhaka, Bangladesh to unravel the population structure and antimicrobial resistance patterns in S. Typhi isolated between 2004–2016.Principal findingsWhole genome sequencing of 202 S. Typhi isolates obtained from three study locations in urban Dhaka revealed a diverse range of S. Typhi genotypes and AMR profiles. The bacterial population within Dhaka were relatively homogenous with little stratification between different healthcare facilities or age groups. We also observed evidence of exchange of Bangladeshi genotypes with neighboring South Asian countries (India, Pakistan and Nepal) suggesting these are circulating throughout the region. This analysis revealed a decline in H58 (genotype 4.3.1) isolates from 2011 onwards, coinciding with a rise in a diverse range of non-H58 genotypes and a simultaneous rise in isolates with reduced susceptibility to fluoroquinolones, potentially reflecting a change in treatment practices. We identified a novel S. Typhi genotype, subclade 3.3.2 (previously defined only to clade level, 3.3), which formed two localized clusters (3.3.2.Bd1 and 3.3.2.Bd2) associated with different mutations in the Quinolone Resistance Determining Region (QRDR) of gene gyrA.SignificanceOur analysis of S. Typhi isolates from urban Dhaka, Bangladesh isolated over a twelve year period identified a diverse range of AMR profiles and genotypes. The observed increase in non-H58 genotypes associated with reduced fluoroquinolone susceptibility may reflect a change in treatment practice in this region and highlights the importance of continued molecular surveillance to monitor the ongoing evolution of AMR in Dhaka. We have defined new genotypes and lineages of Bangladeshi S. Typhi which will facilitate the identification of these emerging AMR clones in future surveillance efforts.

QBB aimed to collect biological samples and data to promote medical research in Qatar and worldwide. The study recruits people aged at least 18 years who are Qatari nationals or who have resided in Qatar for at least 15 years. Participating long-term residents of Qatar are from a variety of countries, including Algeria, Armenia, Bahrain, Bangladesh, Canada, Cyprus, Denmark, Egypt, Ethiopia, India, Iraq, Japan, Jordan, Pakistan, Philippines, Saudi Arabia, Somalia, Sudan, Syria, Tajikistan and more. As of 2019, over 17,000 participants were included in QBB. QBB aims to recruit 60,000 participants and, as of 2024, is continuing to recruit participants.

On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).