Overview: This dataset provides population and migration data for five key South Asian countries: Pakistan, India, Bangladesh, Afghanistan, and Sri Lanka, spanning the years 1960 to 2023. The data, sourced from the World Bank API, sheds light on population growth trends and net migration patterns across these nations, offering rich insights into the region's demographic changes over 63 years.

Key Features: - Total Population: Yearly population data for five countries. - Net Migration: The net effect of immigration and emigration for each year. - Time Span: Covers data from 1960 to 2023. - Source: Extracted from the official World Bank API, ensuring credibility and accuracy.

Use Cases: - Explore regional migration trends and their impact on demographics. - Analyze population growth in South Asia. - Compare migration and population patterns among Pakistan, India, Bangladesh, Afghanistan, and Sri Lanka. - Develop predictive models for demographic and migration forecasts.

About the Data: The dataset is publicly available under the World Bank Open Data License. It can be used freely for educational, research, or commercial purposes with appropriate attribution.

Columns: - Country: Name of the country (Pakistan, India, Bangladesh, Afghanistan, and Sri Lanka). - Year: The year of recorded data. - Total Population: Total population of the country for the given year. - Net Migration: Net migration value (immigration minus emigration).

Key Insights (1960–2023) - Pakistan: Steady growth from 45M (1960) to 240M (2023), with varying migration trends influenced by political and economic changes. - India: Rapid increase from 450M (1960) to 1.43B (2023), with consistently low net migration. - Bangladesh: Population rose from 55M (1960) to 170M (2023), showing negative net migration due to significant emigration. - Afghanistan: Marked by volatile migration due to conflict; population increased from 8M (1960) to 41M (2023). - Sri Lanka: Moderate growth from 10M (1960) to 22M (2023), with net migration losses during periods of civil unrest.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

"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 dataset contains Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate

Note: 1. The Population figures exclude population of areas under unlawful occupation of Pakistan and China, where Census could not be taken. 2. In Arunachal Pradesh, the census was conducted for the first time in 1961. 3. Population data of Assam include Union Territory of Mizoram, which was carved out of Assam after the 1971. 4. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 5. The 1991 Census could not be held in Jammu & Kashmir. Total Population for 1991 has been worked out by Interpolation. 6. India and Manipur figures include estimated Population for those of the three sub-divisions viz., Mao Maram,Paomata and Purul of Senapati district of Manipur as census result of 2001 in these three sub-divisions were cancelled due to technical and administrative reasons

Rising global food insecurity driven by population growth needs urgent measure for universal access to food. This research employs Comparative Performance Analysis (CPA) to evaluate the Global Food Security Index (GFSI), its components [Affordability (AF), Availability (AV), Quality & Safety (Q&S) and Sustainability & Adaptation (S&A)] in tandem with Annual Population Change (APC) for world’s five most populous countries (India, China, USA, Indonesia and Pakistan) using dataset spanning from 2012 to 2022. CPA is applied using descriptive analysis, correlation analysis, Rule of Thumb (RoT) and testing of hypothesis etc. RoT is used with a new analytical approach by applying the significance measures for correlation coefficients. The study suggests that India should enhance its GFSI rank by addressing AF and mitigating the adverse effects of APC on GFSI with a particular focus on Q&S and S&A. China needs to reduce the impact of APC on GFSI by prioritizing AV and S&A. The USA is managing its GFSI well, but focused efforts are still required to reduce APC’s impact on Q&S and S&A. Indonesia should improve across all sectors with a particular focus on APC reduction and mitigating its adverse effects on AF, AV, and S&A. Pakistan should intensify efforts to boost its rank and enhance all sectors with reducing APC. There is statistically significant and negative relation between GFSI and APC for China, Indonesia and found insignificant for others countries. This study holds promise for providing crucial policy recommendations to enhance food security by tackling its underlying factors.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

The dataset contains Year and State wise Density of Population

Note: 1. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 2. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. 3. For working out the density of India and Jammu & Kashmir for 1991,2001, the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account.

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.

Description:

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This dataset consists of high-quality images of handwritten text in the Urdu language, one of the most commonly spoken languages in South Asia, especially in Pakistan, India, and surrounding regions. The dataset has been created by inviting native Urdu speakers from diverse social, educational, and cultural backgrounds to write a predefined text in their natural handwriting style. This predefined text was carefully curated to cover the full range of Urdu characters, ligatures, diacritics, dots, and special symbols used in everyday writing.

Dataset Features

Diverse Handwriting Styles: The dataset includes contributions from native speakers across different demographics, ensuring a rich variety of handwriting styles.

Comprehensive Character Set: The predefined text covers all characters, ligatures, diacritics, and dots commonly used in Urdu script.

Inclusivity: Contributions from people with disabilities add unique variations to the dataset, making it more diverse and comprehensive.

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Demographic Information

The demographic details of contributors, including age, gender, and educational background, are recorded. This information is particularly valuable for research related to author identification, handwriting analysis, and text-matching algorithms.

Potential Applications

This dataset has numerous applications, including:

OCR Development: Enhancing Optical Character Recognition systems for Urdu text.

Handwriting Authentication: Improving security through handwriting-based user verification.

Linguistic Studies: Supporting research in Urdu language processing, script digitalization, and handwriting analysis.

Forensic Handwriting Analysis: Assisting in forensic research for identifying individual handwriting patterns.

Multilingual Handwriting Recognition: Building robust AI models that can recognize handwriting across different languages and scripts.

Quality Control

The dataset has undergone a rigorous quality check to ensure consistency, accuracy, and usability across various academic and commercial research projects, particularly those that involve natural language processing and computer vision technologies.

This dataset is sourced from Kaggle.

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.

This dataset contains genome-wide SNPs for pink bollworm (Pectinophora gossypiella) samples from Australia, India, the U.S., and Pakistan. SNP data was generated by Diversity Arrays Technology, Canberra.

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.

The data contain information on 837 low-cost for-profit private schools (LCPS) from three districts in Punjab, Pakistan: Faisalabad, Gujranwala, and Sialkot. The past few decades have seen an exponential increase in the growth of these LCPS globally, and in countries like Pakistan and India, the private sector now commands a large and quickly increasing share of the market. Over forty percent of primary school enrolment in Pakistan is now in LCPS, and students in private schools in Pakistan far out-perform those in public schools. Yet, firm innovation and expansion is constrained for private schools, likely due to a range of supply-side and market level failures. The main research questions this study and the uploaded dataset seek to answer are: (1) To what extent are schools constrained by finance, and does the type of financing vehicle (loan vs equity) matter? (2) Is LCPS quality improvement constrained by a lack of access to appropriate quality-enhancing products and services, i.e. educational support services (ESS)? (3) Is there a positive interaction between access to finance and the provision of appropriate innovative investment opportunities? The dataset includes topics such as school administration, facilities, fees, enrolment, student population, finances, and financial expectations and literacy. Schools are uniquely identified using the variables mauza (administrative district) code and school code. While most of the variables are school-level, there are a few individual-level data pieces that were collected from the school owner. For each school we interviewed only one owner, therefore both schools and school owners are identified using the same mauza code and school code ID.

Most interventions to improve education in developing countries require spending significant amounts of money on improving the quality of the inputs to the education system. While this is often a useful approach, in countries with weak governments and low tax collection, little resources are available to invest in schools. In these settings, such as in Pakistan, private schools have provided an alternative to the low quality public schools, and parents are willing to pay for the improved quality, and so even in many remote rural areas, parents can pick from sending their child to the public school or one of several private schools in the village. This variety of schools has prompted us to study education markets instead of the inputs to the production of learning, applying theories from studying Small and Medium Enterprises (SMEs) to private schools. Instead of going to schools and telling them which inputs they should focus on, we tend to ask them what prevents them from expanding in quality and quantity. Over the past decade, our research team led by Tahir Andrabi (Pomona College), Jishnu Das (World Bank), and Asim Ijaz Khwaja (Harvard University) has studied the education markets in Pakistan. Despite the tremendous growth in the low cost private school (LCPS) sector (rising from 3,300 schools in 1983 to over 70,000 in 2011) and relatively better quality than the public sector (LCPS are 1.5 years ahead in learning outcomes relative to government schools), there is also evidence of substantial untapped innovation potential in this sector. The team has gathered both primary data and implemented randomized controlled trials (RCTs) that reveal constraints to growth and quality improvement for LCPS. Two factors that contribute to this innovation constraint are the lack of financing (a financial market failure) and access to affordable educational support services (ESS) (an input market failure), which together create a very challenging context for school owners. The current project is a RCT that seeks to explain how alleviating these constraints one at a time or simultaneously would affect learning outcomes, enrolment and school profitability. The randomized component means that schools are randomly allocated to either receiving offers of a loan product or an equity product to alleviate financial constraints, and/or receive access to buying ESS such as teacher training, improved curricula or student testing services. The controlled component of the trial means that some - randomly chosen - schools do not get any of these treatments, which allows us to compare the treatment outcomes with the counterfactual. The two financial products were developed together with one of Pakistan's largest microfinance banks. The equity product represents an innovation in the type of financial product offered to SMEs, and it is particularly relevant to LCPS since its revenue-contingent interest rate (if the school earns more, it will pay a higher interest rate) effectively shifts some of the risk of an investment over to the bank, and LCPS tend to have to make more lumpy investments than other SMEs. Our theory is that a less risky financial product would allow schools to take on more risky investments, such as investments in...

UNOSAT code FL20220808PAK, GDACS Id: 1101522 This map illustrates cumulative satellite-detected water using VIIRS in Pakistan between 01 to 07 September 2022. Within the analyzed area of about 880,000 km², a total of about 60,000 km² of lands appear to be affected with flood waters. Floodwaters seem to have decreased of about 25,000 km² since the period between the 01st July to 31rd of August 2022. Based on Worldpop population data and the maximum flood water coverage, at least 22 million people were potentially exposed or living close to flooded areas in August 2022.
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not been agreed upon by the parties.
The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre - UNOSAT is not responsible for the misuse or misrepresentation of the map.

The genetic origin of ethnic groups present in South Asia is complex. Widespread factors such as complex societal caste structure, language shifts, the coexistence of tribal alongside caste populations, and a varied and vast geography augment this complexity. In face of this complexity, a holistic approach is required for the study of population histories. The isolated Dravidian population, Brahui in Pakistan, represents a remnant of complex ethnolinguistic population history. Genetic studies conducted to date have not demonstrated a close genetic link between the Brahui and other Dravidian populations of the Indian subcontinent. However, none of these studies included the Kurukh-Malto populations, which are linguistically closest to the Brahui. In this study, we included the Kurukh speaking Oraon population and their neighbours in high-resolution genetic analyses to investigate their allele and haplotype sharing with the Brahui population of Pakistan. Our intrapopulation analyses on Oraon collected from Bangladesh and India suggested a a common South Asian source for the Oraon that is genetically distinct from the extent of Indian Mundari (Austroasiatic) populations. The interpopulation comparison of Oraon showed a closer genetic affinity with the geographically more distant Mawasi (North Munda) and Gond (South Dravidian) populations, rather than their immediate neighbours. Moreover, our extensive statistical analyses found no signal of an Oraon-related ancestry inBrahui. One possible explanation to this finding is that thegenetic signature related to the Dravidian population might have been entirely lost in Brahui due toextensiveadmixture with neighboring populations.

Antenatal care (ANC) is an opportunity to receive interventions that can prevent low birth weight (LBW). We sought to 1) estimate LBW prevalence and burden in South Asia, 2) describe the number of ANC visits (quantity) and interventions received (quality), and 3) explore associations between ANC quantity, quality and LBW. We used Demographic and Health Survey (DHS) data from Afghanistan (2015), Bangladesh (2018), India (2016), Nepal (2016), Pakistan (2018) and Sri Lanka (2016) (n = 146,284 children

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.

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 disaster risk and vulnerability of storm surge in each unit are extracted and calculated using100 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit.At the same time, the data set includes the corresponding risk index, exposure index and vulnerability assessment results.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).

Antenatal care (ANC) is an opportunity to receive interventions that can prevent low birth weight (LBW). We sought to 1) estimate LBW prevalence and burden in South Asia, 2) describe the number of ANC visits (quantity) and interventions received (quality), and 3) explore associations between ANC quantity, quality and LBW. We used Demographic and Health Survey (DHS) data from Afghanistan (2015), Bangladesh (2018), India (2016), Nepal (2016), Pakistan (2018) and Sri Lanka (2016) (n = 146,284 children