India is the second most populated country in the world with a sixth of the world's population. According to the 2022 revision of the World Population Prospects the population stood at 1,402,807,867.

Between 1975 and 2010, the population doubled to 1.2 billion, reaching the billion mark in 1998. India is projected to surpass China to become the world's most populous country by 2023. It is expected to become the first country to be home to more than 1.5 billion people by 2030, and its population is set to reach 1.7 billion by 2050. Its population growth rate is 0.98%, down from 2.3% from 1972 to 1983, ranking 112th in the world in 2017.

India has more than 50% of its population below the age of 25 and more than 65% below the age of 35. In 2022, the average age of an Indian is 28.7 years, compared to 38.4 for China and 48.6 for Japan; and, by 2030, India's dependency ratio will be just over 0.4. However, the number of children in India peaked more than a decade ago and is now falling. The number of children under the age of five peaked in 2007, and since then the number has been falling. The number of Indians under 15 years old peaked slightly later (in 2011) and is now also declining. India has more than two thousand ethnic groups, and every major religion is represented, as are four major families of languages (Indo-European, Dravidian, Austroasiatic and Sino-Tibetan languages) as well as two language isolates: the Nihali language, spoken in parts of Maharashtra, and the Burushaski language, spoken in parts of Jammu and Kashmir. 1,000,000 people in India are Anglo-Indians and 700,000 United States citizens are living in India. They represent over 0.1% of the total population of India. Overall, only the continent of Africa exceeds the linguistic, genetic and cultural diversity of the nation of India.

The sex ratio was 944 females for 1000 males in 2016, and 940 per 1000 in 2011. This ratio has been showing an upward trend for the last two decades after a continuous decline in the last century.

Since mid of 20th century, anthropogenic greenhouse gas emissions have increased, it is very possible of being driven largely by economic and population growth, and causing the global warming. Based on the global carbon emissions data of 2014 in each country from CDIAC (Carbon Dioxide Information Analysis Center) and population density data in 2015 from SEDAC (Socioeconomic Data and Applications Center), the population based global carbon emissions dataset in 0.1° resolution (2014) was developed by the model of integrating population density as an economic-population composite indicator to weighted carbon emissions. The result shows the main carbon emission areas are located in the eastern United States, eastern China, Japan, Korea, India, Southeast Asia and Europe, and there are spatial differences in each region. The result can reflect spatial distribution of the current global carbon emissions and provide basic data for global change research. The dataset was archived in .tif data format with the data size of 22.7 MB (3.92MB in compressed file).

Foundation Item： Ministry of Science and Technology of P. R. China (2016YFA0602704) Data Citation： "FAN Zhixin,SU Yun*,FANG Xiuqi.2017.Population Based Global Carbon Emissions Dataset in 0.1°Resolution (2014) ( GlobalPopCarbonEmis2014 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2017.03.12.V1"

Representative single stage or multi-stage sampling of the adult population of the country 18 years old and older was used for the EVS 2017. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. 8 countries out of 16 deviated from the guidelines and planned with an effective sample size below the set threshold. Germany, Netherlands, Iceland and Switzerland, due to the mixed mode design, allocated only part (50% or more) of the effective sample size to the interviewer-administered mode. Sample design and other relevant information about sampling were reviewed by the EVS-Methodology Group (EVS-MG) and approved prior to contracting of fieldwork agency or starting of data collection. In case of on-field sampling EVS-MG proposed necessary protocols for documentation of the probabilities of selection of each respondent. The sampling was documented using the Sampling Design Form (SDF) delivered by the national teams (see the EVS2017 Methodological Guidelines, Sampling). The SDF includes the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it includes the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights. WVS 7: The sampling procedures differ from country to country: Probability Sample: Multistage Sample Probability Sample: Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2020. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are required to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines and planned with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode Face-to-face interview: CAPI (Computer Assisted Personal Interview) Face-to-face interview: PAPI (Paper and Pencil Interview) Telephone interview: CATI (Computer Assisted Telephone Interview) Self-administered questionnaire: CAWI (Computer-Assisted Web Interview) Self-administered questionnaire: Paper In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2021 is face to face (interviewer-administered). Several countries employed mixed-mode approach to data collection: USA (CAWI; CATI); Australia and Japan (CAWI; postal survey); Hong Kong SAR (PAPI; CAWI); Malaysia (CAWI; PAPI). The WVS Master Questionnaire was provided in English and each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. A central team monitored the translation process. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). The sampling procedures differ from country to country; probability Sample: Multistage Sample Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2020. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines and planned with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

The aim of the article is to compare health system outcomes in the BRICS countries, assess the trends of their changes in 2000−2017, and verify whether they are in any way correlated with the economic context. The indicators considered were: nominal and per capita current health expenditure, government health expenditure, gross domestic product (GDP) per capita, GDP growth, unemployment, inflation, and composition of GDP. The study covered five countries of the BRICS group over a period of 18 years. We decided to characterize countries covered with a dataset of selected indicators describing population health status, namely: life expectancy at birth, level of immunization, infant mortality rate, maternal mortality ratio, and tuberculosis case detection rate. We constructed a unified synthetic measure depicting the performance of individual health systems in terms of their outcomes with a single numerical value. Descriptive statistical analysis of quantitative traits consisted of the arithmetic mean (xsr), standard deviation (SD), and, where needed, the median. The normality of the distribution of variables was tested with the Shapiro–Wilk test. Spearman's rho and Kendall tau rank coefficients were used for correlation analysis between measures. The correlation analyses have been supplemented with factor analysis. We found that the best results in terms of health care system performance were recorded in Russia, China, and Brazil. India and South Africa are noticeably worse. However, the entire group performs visibly worse than the developed countries. The health system outcomes appeared to correlate on a statistically significant scale with health expenditures per capita, governments involvement in health expenditures, GDP per capita, and industry share in GDP; however, these correlations are relatively weak, with the highest strength in the case of government's involvement in health expenditures and GDP per capita. Due to weak correlation with economic background, other factors may play a role in determining health system outcomes in BRICS countries. More research should be recommended to find them and determine to what extent and how exactly they affect health system outcomes.

This dataset, released August 2017, contains the top ten birthplaces of people born in non-English speaking countries, 2016. The data comprise residents of Australia who were born overseas in one of the predominantly non-English speaking countries which are in the top ten for Australia in terms of high numbers of migrants. These are, from highest to lowest: China, India, Philippines, Vietnam, Italy, Malaysia, Sri Lanka, Germany, Republic of Korea (South), and Greece. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.