🔍 "Unraveling India's Mortality Mysteries: A Comprehensive Dataset on Causes of Death, 2009-2020" 📊

This unique dataset, sourced directly from the official Indian Census website, offers a deep dive into the intricate patterns and trends of mortality in India over the past decade. 🌍

Covering a wide range of data points, including:

Detailed breakdown of causes of death 🩺 Age-wise distribution of fatalities 👨‍🦳👧 Year-over-year reporting of mortality statistics 📈 Comprehensive sex-wise analysis 👨‍🌾👩‍🔬 This comprehensive dataset is a must-have for researchers, policymakers, and public health experts seeking to uncover the hidden narratives behind India's evolving health landscape. 🔍💡

Dive into this treasure trove of insights and unlock the keys to understanding the complex tapestry of life and death in the world's second-most populous nation. 🇮🇳🔑

Anyone need the data in the form of excel please make request in the suggestion box . I will upload the excel form of the data

This comprehensive dataset provides a deep dive into the infant mortality rates (IMR) in India, tracing its trajectory through various decades. It offers valuable insights into health indicators, socio-economic factors, and policy initiatives, showcasing how India has evolved in its approach to child health and safety. Researchers, policymakers, and enthusiasts can tap into this rich resource to gain a better understanding of the challenges and progress made in the realm of infant health in India.

It's worth noting that while the dataset is expansive, there are multiple null values for data points prior to the 1990s. This underscores the limitations in the available data from that period, and users are advised to exercise caution when making historical comparisons or drawing conclusions from these early years. Regardless, this dataset stands as a testament to the strides India has made and the distances yet to be covered in ensuring the well-being of its youngest citizens.

Source

BackgroundOver 75% of the annual estimated 9.5 million deaths in India occur in the home, and the large majority of these do not have a certified cause. India and other developing countries urgently need reliable quantification of the causes of death. They also need better epidemiological evidence about the relevance of physical (such as blood pressure and obesity), behavioral (such as smoking, alcohol, HIV-1 risk taking, and immunization history), and biological (such as blood lipids and gene polymorphisms) measurements to the development of disease in individuals or disease rates in populations. We report here on the rationale, design, and implementation of the world's largest prospective study of the causes and correlates of mortality. Methods and FindingsWe will monitor nearly 14 million people in 2.4 million nationally representative Indian households (6.3 million people in 1.1 million households in the 1998–2003 sample frame and 7.6 million people in 1.3 million households in the 2004–2014 sample frame) for vital status and, if dead, the causes of death through a well-validated verbal autopsy (VA) instrument. About 300,000 deaths from 1998–2003 and some 700,000 deaths from 2004–2014 are expected; of these about 850,000 will be coded by two physicians to provide causes of death by gender, age, socioeconomic status, and geographical region. Pilot studies will evaluate the addition of physical and biological measurements, specifically dried blood spots. Preliminary results from over 35,000 deaths suggest that VA can ascertain the leading causes of death, reduce the misclassification of causes, and derive the probable underlying cause of death when it has not been reported. VA yields broad classification of the underlying causes in about 90% of deaths before age 70. In old age, however, the proportion of classifiable deaths is lower. By tracking underlying demographic denominators, the study permits quantification of absolute mortality rates. Household case-control, proportional mortality, and nested case-control methods permit quantification of risk factors. ConclusionsThis study will reliably document not only the underlying cause of child and adult deaths but also key risk factors (behavioral, physical, environmental, and eventually, genetic). It offers a globally replicable model for reliably estimating cause-specific mortality using VA and strengthens India's flagship mortality monitoring system. Despite the misclassification that is still expected, the new cause-of-death data will be substantially better than that available previously.

The dataset contains year- and state-wise compiled on the number of persons killed in road accidents which have happened in India

This dataset is a comprehensive collection of data related to the spread of COVID-19 in India. It captures the number of confirmed cases and deaths in each state and union territory of India from the first reported case in January 2020 to the present day. The dataset was created to provide an understanding of the extent of the COVID-19 pandemic in India. It is important because it allows researchers, policy-makers and citizens to gain insights into the various factors that may be driving the spread of the virus in different states and regions of India. It also provides valuable information for researchers trying to understand the dynamics of the pandemic in India.

This dataset is important because it allows us to understand the current situation of the pandemic in India and to monitor the progress of the virus in each state. It can also be used to measure the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus. The dataset is applicable to anyone interested in understanding the dynamics of the COVID-19 pandemic in India, such as policy-makers, researchers, citizens, NGOs and media. It can be used to gain insights into the current situation and to track the progress of the virus in each state. It can also be used to monitor the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus.

Overall, this dataset provides a comprehensive view of the COVID-19 pandemic in India. It is updated on a daily basis, and provides essential information that is useful for researchers, policy-makers and citizens. It is an invaluable resource that can be used to understand the dynamics of the virus and to monitor the progress of the virus in each state.

Background and objectiveIndia contributes the highest global share of deaths among the under-fives. Continuous monitoring of the reduction in the under-five mortality rate (U5MR) at local level is thus essential to set priorities for policy-makers and health professionals. In this study, we aimed to provide an update on district-level disparities in the neonatal mortality rate (NMR) and the U5MR with special reference to Sustainable Development Goal 3 (SDG3) on preventable deaths among new-borns and children under five.Data and methodsWe used recently released population-based cross-sectional data from the National Family Health Survey (NFHS) conducted in 2015–2016. We used the synthetic cohort probability approach to analyze the full birth history information of women aged 15–49 to estimate the NMR and U5MR for the ten years preceding the survey.ResultsBoth the NMR and U5MR vary enormously across Indian districts. With respect to the SDG3 target for 2030 for the NMR and the U5MR, the estimated NMR for India for the period studied is about 2.4 times higher, while the estimated U5MR is about double. At district level, while 9% of the districts have already reached the NMR targeted in SDG3, nearly half (315 districts) are not likely to achieve the 2030 target even if they realize the NMR reductions achieved by their own states between the last two rounds of National Family Health Survey of India. Similarly, less than one-third of the districts (177) of India are unlikely to achieve the SDG3 target on the U5MR by 2030. While the majority of high-risk districts for the NMR and U5MR are located in the poorer states of north-central and eastern India, a few high-risk districts for NMR also fall in the rich and advanced states. About 97% of districts from Chhattisgarh and Uttar Pradesh, for example, are unlikely to meet the SDG3 target for preventable deaths among new-borns and children under age five, irrespective of gender.ConclusionsTo achieve the SDG3 target on preventable deaths by 2030, the majority of Indian districts clearly need to make a giant leap to reduce their NMR and U5MR.

Abstract

The Longitudinal Indian Family Health (LIFE) is a long-term research study that will examine socio-economic and environmental influences on children’s health and development in India. The main aim of the study is to understand the link between the environmental conditions in which Indian women conceive, go through their pregnancy and give birth, and their physical and mental health during this period.

The cohort comprises married women between 15 and 35 years of age (mean 22 years), recruited before pregnancy or in the first trimester of pregnancy, from 2009 to 2011. These CHVs focus on women in the village to ascertain pregnancy (by interview) and to educate and encourage the women to seek regular antenatal care and other health care services. REACH has enumerated all household members in these communities and mapped each dwelling by a geographical information system (GIS). During each visit, CHVs conduct interviews to collect and update information on demography and pregnancy. Since 2004, CHVs have been collecting data on infant deaths and birthweights in the population. Socio-demographic variables such as access to electricity, means of transportation and possession of audio-visual devices were collected from REACH database

Documentation

You can submit a proposal to collaborate with LIFE Study investigators. A written protocol must be submitted, reviewed and approved by the LIFE Data Sharing Plan Committee before initiation of new projects. For further information, contact Dr P. S. Reddy at [reddyps@verizon.net]. Updated information may be found on the research centre website at [www.sharefoundations.org].

Methodology

The LIFE study is being conducted in villages of Medchal Mandal, R.R.District, Telangana, India. Since 2009, 1227 women aged between 15 and 35 years were recruited before conception or within 14 weeks of gestation. Women were followed through pregnancy, delivery, and postpartum. Follow-up of children is ongoing. Baseline data were collected from husbands of 642 women.

Anthropometric measurements, biological samples and detailed questionnaire data were collected during registration, the first and third trimesters, delivery and at 1 month postpartum. Anthropometric measurements and health questionnaire data are obtained for each child, and a developmental assessment is done at 1, 6, 12, 18, 24, 36, 48 and 60 months. At 36 months, each child is screened for development and mental health problems. Questionnaires are completed for pregnancy loss and death of children under 5 years old. The LIFE Biobank preserves over 6000 samples.

Pew Research Center conducted face-to-face surveys among 29,999 adults (ages 18 and older) across 26 Indian states and three union territories in 17 languages. The sample includes interviews with 22,975 Hindus, 3,336 Muslims, 1,782 Sikhs, 1,011 Christians, 719 Buddhists and 109 Jains. An additional 67 respondents belong to other religions or are religiously unaffiliated. Six groups were targeted for oversampling as part of the survey design: Muslims, Christians, Sikhs, Buddhists, Jains and those living in the Northeast region. Interviews were conducted under the direction of RTI International from November 17, 2019, to March 23, 2020. Data collection used computer-assisted personal interviews (CAPI) after random selection of households.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation.

Two reports focused on the findings from this data: •Religion in India: Tolerance and Segregation: https://www.pewresearch.org/religion/2021/06/29/religion-in-india-tolerance-and-segregation/ •How Indians View Gender Roles in Families and Society: https://www.pewresearch.org/religion/2022/03/02/how-indians-view-gender-roles-in-families-and-society/

India recorded 531794 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, India reported 44983152 Coronavirus Cases. This dataset includes a chart with historical data for India Coronavirus Deaths.

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

This Dataset contains year, country and mission-wise details of expenditure incurred from Indian Community Welfare Fund (ICWF) for providing legal assistance to Indian nationals and expenditure incurred on transportation of mortal remains from the country of death to India

Note: Indian Community Welfare Fund (ICWF) has been set up in all Indian Missions and Posts abroad to assist Overseas Indian nationals in times of distress and emergency. Fund is used to provide legal assistance, boarding & lodging assistance, emergency medical care, air passage to stranded Indians, transportation of mortal remains of Indian nationals, payment of small fines and penalties for release of Indian Nationals from jail/ detention Centres.

The dataset contains All India Yearly Wholesale Price Index from Handbook of Statistics on Indian Economy.

Note: Food Articles and Non-food Articles are part of Primary Articles.

This annotated dataset comprises punishment-related information derived from judgments of various criminal cases, including offences such as rape, murder, kidnapping, and NDPS (Narcotic Drugs and Psychotropic Substances), adjudicated between 2000-2010 across different states of India. |BIB Citation: @online{IJPD, author = {Dr. KVK Santhy, Ranjit Thomas, Piyush Vashistha, Krishna Reddy P.}, title = {Indian Judgements Punishment Data (IJPD), NALSAR, University of Law, Hyderabad}, howpublished = { extit{A Critical Analysis of Punishment and Sentencing in India: Vis-A-Vis A special study in the state of Andhra Pradesh}}, url = {https://india-data.org/dataset-details/0502aa29-8506-4ddf-86a4-9a5dd7fb566e}, year={2008}, urldate = {2024-12-15} }

BackgroundDiverse socio-economic and cultural issues contribute to adverse health outcomes and increased mortality rates among rural Indian women across different age categories. The present study aims to comprehensively assess age-specific mortality rates (ASMR) and their temporal trends using a composite measure at the sub-national level for rural Indian females to capture cross-state differences.Materials and methodsA total of 19 states were included in the study to construct a composite age-specific mortality index for 2011 (base year) and 2018 (reference year) and examine the incremental changes in the index values across these years at the sub-national level in India. Sub-index values were calculated for each component age group and were subsequently used to compute the composite ASMR index using the geometric mean method. Based on the incremental changes, the performance of states was categorized into four different typologies.ResultsImprovement in mortality index scores in the 0–4 years age group was documented for all states. The mortality rates for the 60+ age group were recorded to be high for all states. Kerala emerged as the overall top performer in terms of mortality index scores, while Bihar and Jharkhand were at the bottom of the mortality index table. The overall mortality composite score has shown minor improvement from base year to reference year at all India level.ConclusionAn overall reduction in the mortality rates of rural Indian women has been observed over the years in India. However, in states like Bihar and Jharkhand, mortality is high and has considerable scope for improvement. The success of public health interventions to reduce the under-five mortality rate is evident as the female rural mortality rates have reduced sizably for all states. Nevertheless, there is still sizable scope for reducing mortality rates for other component age groups. Additionally, there is a need to divert attention toward the female geriatric (60+ years) population as the mortality rates are still high.

The ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice.

Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app.

The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon.

This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions.

REFERENCES:

Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: https://doi.org/10.25383/city.14294597

microsoft/ORBIT-Dataset. https://github.com/microsoft/ORBIT-Dataset

Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. https://doi.org/10.1145/3613905.3648641

Inflation Rate in India decreased to 0.25 percent in October from 1.44 percent in September of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

BackgroundLittle is known about the causes of death in children in India after age five years. The objective of this study is to provide the first ever direct national and sub-national estimates of infectious disease mortality in Indian children aged 5 to 14 years. MethodsA verbal autopsy based assessment of 3 855 deaths is children aged 5 to 14 years from a nationally representative survey of deaths occurring in 2001–03 in 1·1 million homes in India. ResultsInfectious diseases accounted for 58% of all deaths among children aged 5 to 14 years. About 18% of deaths were due to diarrheal diseases, 10% due to pneumonia, 8% due to central nervous system infections, 4% due to measles, and 12% due to other infectious diseases. Nationally, in 2005 about 59 000 and 34 000 children aged 5 to 14 years died from diarrheal diseases and pneumonia, corresponding to mortality of 24·1 and 13·9 per 100 000 respectively. Mortality was nearly 50% higher in girls than in boys for both diarrheal diseases and pneumonia. ConclusionsApproximately 60% of all deaths in this age group are due to infectious diseases and nearly half of these deaths are due to diarrheal diseases and pneumonia. Mortality in this age group from infectious diseases, and diarrhea in particular, is much higher than previously estimated.

The following files are included:
[Item 1 'stations_ghi.txt'] : complete station metadata, all 1677 stations, pipe(|) delimited
[Item 2 'hydromet_annual.txt'] : hydrometeorological time series for stations in Groups 1, 2 and 3, annual, pipe(|) delimited
[Item 3 'hydromet_monthly.txt'] : hydrometeorological time series for stations in Groups 1, 2 and 3, monthly, pipe(|) delimited
[Item 4 'basins_ghi'] : one shapefile of ghi composite basins
[Item 5 folder 'by_station'] : shapefiles of delineated catchment boundaries for stations in Groups 1, 2 and 3
[Item 6 folder 'pdfs'] : PDF files of station summary, annual time series charts and monthly time series charts for stations in Groups 1, 2 and 3 (one PDF per composite basin

About this dataset

Context

The dataset tabulates the population of Indian Village by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Indian Village across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 56.64% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Indian Village is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Indian Village total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Indian Village Population by Race & Ethnicity. You can refer the same here