100+ datasets found
  1. T

    India - Rural Population

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jan 13, 2017
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    TRADING ECONOMICS (2017). India - Rural Population [Dataset]. https://tradingeconomics.com/india/rural-population-percent-of-total-population-wb-data.html
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    excel, xml, json, csvAvailable download formats
    Dataset updated
    Jan 13, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    India
    Description

    Rural population (% of total population) in India was reported at 63.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  2. India Employed Persons

    • ceicdata.com
    Updated Mar 15, 2025
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    CEICdata.com (2025). India Employed Persons [Dataset]. https://www.ceicdata.com/en/indicator/india/employed-persons
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    Dataset updated
    Mar 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2010 - Dec 1, 2021
    Area covered
    India
    Variables measured
    Employment
    Description

    Key information about India Employed Persons

    • India Employed Persons was reported at 470,495,536.230 Person in Dec 2021
    • It recorded an increase from the previous number of 447,183,819.730 Person for Dec 2020
    • India Employed Persons data is updated yearly, averaging 384,395,378.330 Person from Dec 1970 to 2021, with 52 observations
    • The data reached an all-time high of 485,507,600.000 Person in 2019 and a record low of 209,275,793.440 Person in 1970
    • India Employed Persons data remains active status in CEIC and is reported by CEIC Data
    • The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Employed Persons: Annual: Asia

    Organisation for Economic Co-operation and Development provides annual Employed Persons.

  3. a

    PerCapita CO2 Footprint InDioceses FULL

    • hub.arcgis.com
    • catholic-geo-hub-cgisc.hub.arcgis.com
    Updated Sep 23, 2019
    + more versions
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    burhansm2 (2019). PerCapita CO2 Footprint InDioceses FULL [Dataset]. https://hub.arcgis.com/content/95787df270264e6ea1c99ffa6ff844ff
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    Dataset updated
    Sep 23, 2019
    Dataset authored and provided by
    burhansm2
    License

    Attribution-NoDerivs 4.0 (CC BY-ND 4.0)https://creativecommons.org/licenses/by-nd/4.0/
    License information was derived automatically

    Area covered
    Description

    PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

  4. I

    India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30...

    • ceicdata.com
    Updated Aug 7, 2020
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    CEICdata.com (2020). India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female [Dataset]. https://www.ceicdata.com/en/india/health-statistics/in-mortality-from-cvd-cancer-diabetes-or-crd-between-exact-ages-30-and-70-female
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    Dataset updated
    Aug 7, 2020
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2000 - Dec 1, 2016
    Area covered
    India
    Description

    India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

  5. 🦈 Shark Tank India dataset 🇮🇳

    • kaggle.com
    Updated Apr 20, 2025
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    Satya Thirumani (2025). 🦈 Shark Tank India dataset 🇮🇳 [Dataset]. https://www.kaggle.com/datasets/thirumani/shark-tank-india
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 20, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Satya Thirumani
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Shark Tank India Data set.

    Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

    All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

    Here is the data dictionary for (Indian) Shark Tank season's dataset.

    • Season Number - Season number
    • Startup Name - Company name or product name
    • Episode Number - Episode number within the season
    • Pitch Number - Overall pitch number
    • Season Start - Season first aired date
    • Season End - Season last aired date
    • Original Air Date - Episode original/first aired date, on OTT/TV
    • Episode Title - Episode title in SonyLiv
    • Anchor - Name of the episode presenter/host
    • Industry - Industry name or type
    • Business Description - Business Description
    • Company Website - Company Website URL
    • Started in - Year in which startup was started/incorporated
    • Number of Presenters - Number of presenters
    • Male Presenters - Number of male presenters
    • Female Presenters - Number of female presenters
    • Transgender Presenters - Number of transgender/LGBTQ presenters
    • Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
    • Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
    • Pitchers City - Presenter's town/city or place where company head office exists
    • Pitchers State - Indian state pitcher hails from or state where company head office exists
    • Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
    • Monthly Sales - Total monthly sales, in lakhs
    • Gross Margin - Gross margin/profit of company, in percentages
    • Net Margin - Net margin/profit of company, in percentages
    • EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
    • Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
    • SKUs - Stock Keeping Units or number of varieties, at the time of pitch
    • Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
    • Bootstrapped - Startup is bootstrapped or not (yes/no)
    • Part of Match off - Competition between two similar brands, pitched at same time
    • Original Ask Amount - Original Ask Amount, in lakhs INR
    • Original Offered Equity - Original Offered Equity, in percentages
    • Valuation Requested - Valuation Requested, in lakhs INR
    • Received Offer - Received offer or not, 1-received, 0-not received
    • Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
    • Total Deal Amount - Total Deal Amount, in lakhs INR
    • Total Deal Equity - Total Deal Equity, in percentages
    • Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
    • Debt Interest - Debt interest rate, in percentages
    • Deal Valuation - Deal Valuation, in lakhs INR
    • Number of sharks in deal - Number of sharks involved in deal
    • Deal has conditions - Deal has conditions or not? (yes or no)
    • Royalty Percentage - Royalty percentage, if it's royalty deal
    • Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
    • Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
    • Namita Investment Amount - Namita Investment Amount, in lakhs INR
    • Namita Investment Equity - Namita Investment Equity, in percentages
    • Namita Debt Amount - Namita Debt Amount, in lakhs INR
    • Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
    • Vineeta Investment Equity - Vineeta Investment Equity, in percentages
    • Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
    • Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
    • Anupam Investment Equity - Anupam Investment Equity, in percentages
    • Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
    • Aman Investment Amount - Aman Investment Amount, in lakhs INR
    • Aman Investment Equity - Aman Investment Equity, in percentages
    • Aman Debt Amount - Aman Debt Amount, in lakhs INR
    • Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
    • Peyush Investment Equity - Peyush Investment Equity, in percentages
    • Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
    • Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
    • Ritesh Investment Equity - Ritesh Investment Equity, in percentages
    • Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
    • Amit Investment Amount - Amit Investment Amount, in lakhs INR
    • Amit Investment Equity - Amit Investment Equity, in percentages
    • Amit Debt Amount - Amit Debt Amount, in lakhs INR
    • Guest Investment Amount - Guest Investment Amount, in lakhs INR
    • Guest Investment Equity - Guest Investment Equity, in percentages
    • Guest Debt Amount - Guest Debt Amount, in lakhs INR
    • Invested Guest Name - Name of the guest(s) who invested in deal
    • All Guest Names - Name of all guests, who are present in episode
    • Namita Present - Whether Namita present in episode or not
    • Vineeta Present - Whether Vineeta present in episode or not
    • Anupam ...
  6. T

    India - Urban Population (% Of Total)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jul 22, 2013
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    TRADING ECONOMICS (2013). India - Urban Population (% Of Total) [Dataset]. https://tradingeconomics.com/india/urban-population-percent-of-total-wb-data.html
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    excel, json, xml, csvAvailable download formats
    Dataset updated
    Jul 22, 2013
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    India
    Description

    Urban population (% of total population) in India was reported at 36.87 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  7. Life Expectancy in India

    • kaggle.com
    zip
    Updated Feb 4, 2020
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    Nimish Ukey (2020). Life Expectancy in India [Dataset]. https://www.kaggle.com/nimishukey/life-expectancy-in-india
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    zip(7081 bytes)Available download formats
    Dataset updated
    Feb 4, 2020
    Authors
    Nimish Ukey
    Area covered
    India
    Description

    Life expectancy is an estimate of how long a person would live, on average.

    Life expectancy is affected by many factors such as: • Socioeconomic status, including employment, income, education and economic wellbeing. • The quality of the health system and the ability of people to access it; health behaviors such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise. • Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation, etc.

    With the help of the above-mentioned factors, I tried to analyse t the data and come up with measurable solutions to improve the Life Expectancy.

  8. India Census: Population: Age: 100+

    • ceicdata.com
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    CEICdata.com, India Census: Population: Age: 100+ [Dataset]. https://www.ceicdata.com/en/india/census-population-by-single-age/census-population-age-100
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    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 1, 1991 - Mar 1, 2011
    Area covered
    India
    Variables measured
    Population
    Description

    India Census: Population: Age: 100+ data was reported at 605,778.000 Person in 2011. This records an increase from the previous number of 139,472.000 Person for 2001. India Census: Population: Age: 100+ data is updated yearly, averaging 151,646.000 Person from Mar 1991 (Median) to 2011, with 3 observations. The data reached an all-time high of 605,778.000 Person in 2011 and a record low of 139,472.000 Person in 2001. India Census: Population: Age: 100+ data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD002: Census: Population: by Single Age.

  9. T

    India - Researchers In R&D (per Million People)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 29, 2017
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    TRADING ECONOMICS (2017). India - Researchers In R&D (per Million People) [Dataset]. https://tradingeconomics.com/india/researchers-in-r-d-per-million-people-wb-data.html
    Explore at:
    csv, xml, json, excelAvailable download formats
    Dataset updated
    May 29, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    India
    Description

    Researchers in R&D (per million people) in India was reported at 259 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Researchers in R&D (per million people) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  10. The ORBIT (Object Recognition for Blind Image Training)-India Dataset

    • zenodo.org
    • data.niaid.nih.gov
    Updated Apr 24, 2025
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    Gesu India; Gesu India; Martin Grayson; Martin Grayson; Daniela Massiceti; Daniela Massiceti; Cecily Morrison; Cecily Morrison; Simon Robinson; Simon Robinson; Jennifer Pearson; Jennifer Pearson; Matt Jones; Matt Jones (2025). The ORBIT (Object Recognition for Blind Image Training)-India Dataset [Dataset]. http://doi.org/10.5281/zenodo.12608444
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Gesu India; Gesu India; Martin Grayson; Martin Grayson; Daniela Massiceti; Daniela Massiceti; Cecily Morrison; Cecily Morrison; Simon Robinson; Simon Robinson; Jennifer Pearson; Jennifer Pearson; Matt Jones; Matt Jones
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    India
    Description

    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:

    1. 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

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

    3. 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

  11. Internet penetration rate in India 2014-2025

    • statista.com
    • ai-chatbox.pro
    Updated Jul 14, 2025
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    Statista (2025). Internet penetration rate in India 2014-2025 [Dataset]. https://www.statista.com/statistics/792074/india-internet-penetration-rate/
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    Dataset updated
    Jul 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    The internet penetration rate in India rose over 55 percent in 2025, from about 14 percent in 2014. Although these figures seem relatively low, it meant that more than half of the population of 1.4 billion people had internet access that year. This also ranked the country second in the world in terms of active internet users. Internet availability and accessibility By 2021, the number of internet connections across the country tripled with urban areas accounting for a higher density of connections than rural regions. Despite incredibly low internet prices, internet usage in India has yet to reach its full potential. Lack of awareness and a tangible gender gap lie at the heart of the matter, with affordable mobile handsets and mobile internet connections presenting only a partial solution. Reliance Jio was the popular choice among Indian internet subscribers, offering them wider coverage at cheap rates. Digital living Home to one of the largest bases of netizens in the world, India is abuzz with internet activities being carried out every moment of every day. From information and research to shopping and entertainment to living in smart homes, Indians have welcomed digital living with open arms. Among these, social media usage was one of the most common reasons for accessing the internet.

  12. Number of internet users in India 2010-2050

    • statista.com
    Updated Apr 14, 2025
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    Statista (2025). Number of internet users in India 2010-2050 [Dataset]. https://www.statista.com/statistics/255146/number-of-internet-users-in-india/
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    Dataset updated
    Apr 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    In 2023, India had over 1.2 billion internet users across the country. This figure was projected to grow to over 1.6 billion users by 2050, indicating a big market potential in internet services for the South Asian country. In fact, India was ranked as the second largest online market worldwide in 2022, second only to China. The number of internet users was estimated to increase in both urban as well as rural regions, indicating a dynamic growth in access to internet.

    Mobile connectivity

    Of the total internet users in the country, a majority of the people access the internet via their mobile phones. There were nearly the same amount of smartphone users as internet users across the country. Cheap availability of mobile data, a growing smartphone user base in the country along with the utility value of smartphones compared to desktops and tablets are some of the factors contributing to the mobile heavy internet access in India.

    Growth is on the cards

    Despite the large number of internet users in the country, the internet penetration levels took longer to catch up equally. At the same time, the number of women who have access to internet is much lower than men in the country, and the bias is even more evident in rural India. Similarly, internet usage is lower among older adults in the country due to internet literacy and technological know-how. By encouraging internet accessibility among marginalized groups including women, older people and rural inhabitants in the country, India’s digital footprint has significant headroom to grow.

  13. Census Data

    • catalog.data.gov
    • datadiscoverystudio.org
    • +3more
    Updated Mar 1, 2024
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    U.S. Bureau of the Census (2024). Census Data [Dataset]. https://catalog.data.gov/dataset/census-data
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    Dataset updated
    Mar 1, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

  14. Literacy rate in India 1981-2022, by gender

    • statista.com
    • ai-chatbox.pro
    Updated May 30, 2025
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    Aaron O'Neill (2025). Literacy rate in India 1981-2022, by gender [Dataset]. https://www.statista.com/topics/10801/demographics-of-india/
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    Dataset updated
    May 30, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Aaron O'Neill
    Area covered
    India
    Description

    Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2022, the degree of literacy in India was about 76.32 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

  15. Digital Quality of Life index India 2024, by pillar

    • statista.com
    Updated Jul 18, 2025
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    Tanushree Basuroy (2022). Digital Quality of Life index India 2024, by pillar [Dataset]. https://www.statista.com/topics/2157/internet-usage-in-india/
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    Dataset updated
    Jul 18, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Tanushree Basuroy
    Area covered
    India
    Description

    According to the 2024 Digital Quality of Life index covering 121 countries, India's rank dropped to 60, scoring an average of 0.47 points across five digital aspects. While the country moved up the ranks on the internet affordability and electronic security pillars that year, it saw a decline across the other three aspects that had been its strength in 2023.

  16. N

    Income Bracket Analysis by Age Group Dataset: Age-Wise Distribution of...

    • neilsberg.com
    csv, json
    Updated Aug 7, 2024
    + more versions
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    Neilsberg Research (2024). Income Bracket Analysis by Age Group Dataset: Age-Wise Distribution of Indian Trail, NC Household Incomes Across 16 Income Brackets // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/ac78fdc2-54ae-11ef-a42e-3860777c1fe6/
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    csv, jsonAvailable download formats
    Dataset updated
    Aug 7, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Indian Trail, North Carolina
    Variables measured
    Number of households with income $200,000 or more, Number of households with income less than $10,000, Number of households with income between $15,000 - $19,999, Number of households with income between $20,000 - $24,999, Number of households with income between $25,000 - $29,999, Number of households with income between $30,000 - $34,999, Number of households with income between $35,000 - $39,999, Number of households with income between $40,000 - $44,999, Number of households with income between $45,000 - $49,999, Number of households with income between $50,000 - $59,999, and 6 more
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It delineates income distributions across 16 income brackets (mentioned above) following an initial analysis and categorization. Using this dataset, you can find out the total number of households within a specific income bracket along with how many households with that income bracket for each of the 4 age cohorts (Under 25 years, 25-44 years, 45-64 years and 65 years and over). For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Indian Trail: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

    Key observations

    • Upon closer examination of the distribution of households among age brackets, it reveals that there are 184(1.41%) households where the householder is under 25 years old, 4,951(37.86%) households with a householder aged between 25 and 44 years, 5,924(45.30%) households with a householder aged between 45 and 64 years, and 2,019(15.44%) households where the householder is over 65 years old.
    • The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Indian Trail, showcasing varying income levels among different age demographics.
    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

    Income brackets:

    • Less than $10,000
    • $10,000 to $14,999
    • $15,000 to $19,999
    • $20,000 to $24,999
    • $25,000 to $29,999
    • $30,000 to $34,999
    • $35,000 to $39,999
    • $40,000 to $44,999
    • $45,000 to $49,999
    • $50,000 to $59,999
    • $60,000 to $74,999
    • $75,000 to $99,999
    • $100,000 to $124,999
    • $125,000 to $149,999
    • $150,000 to $199,999
    • $200,000 or more

    Variables / Data Columns

    • Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
    • Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
    • 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
    • 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
    • 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

    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 Trail median household income by age. You can refer the same here

  17. i

    National Family Health Survey 1992-1993 - India

    • catalog.ihsn.org
    • dev.ihsn.org
    • +1more
    Updated Jul 6, 2017
    + more versions
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    International Institute for Population Sciences (IIPS) (2017). National Family Health Survey 1992-1993 - India [Dataset]. https://catalog.ihsn.org/catalog/2547
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    Dataset updated
    Jul 6, 2017
    Dataset authored and provided by
    International Institute for Population Sciences (IIPS)
    Time period covered
    1992 - 1993
    Area covered
    India
    Description

    Abstract

    The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

    The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

    The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

    Geographic coverage

    National

    Analysis unit

    • Household
    • Data collected for women 13-49, indicators calculated for women 15-49

    Universe

    The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

    Kind of data

    Sample survey data

    Sampling procedure

    SAMPLE DESIGN

    The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

    SAMPLE SIZE AND ALLOCATION

    The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

    The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

    THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

    A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

    Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

    In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

    THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

    A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

    All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

    Mode of data collection

    Face-to-face

    Research instrument

    Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

  18. I

    India Population density - data, chart | TheGlobalEconomy.com

    • theglobaleconomy.com
    csv, excel, xml
    Updated May 11, 2020
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    Globalen LLC (2020). India Population density - data, chart | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/India/population_density/
    Explore at:
    excel, csv, xmlAvailable download formats
    Dataset updated
    May 11, 2020
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1961 - Dec 31, 2021
    Area covered
    India
    Description

    India: Population density, people per square km: The latest value from 2021 is 473 people per square km, an increase from 470 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for India from 1961 to 2021 is 305 people per square km. The minimum value, 153 people per square km, was reached in 1961 while the maximum of 473 people per square km was recorded in 2021.

  19. m

    Data from: A Dataset on 'Social media and India’s Foreign Policy: The Case...

    • data.mendeley.com
    Updated Dec 19, 2024
    + more versions
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    Mukund Narvenkar (2024). A Dataset on 'Social media and India’s Foreign Policy: The Case Study of ‘X’ Diplomacy during the Covid-19 Pandemic' [Dataset]. http://doi.org/10.17632/xfr9y9ggkm.3
    Explore at:
    Dataset updated
    Dec 19, 2024
    Authors
    Mukund Narvenkar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    India
    Description

    Social media platforms have become integral tools in the conduct of foreign policy for many nations, including India. This dataset serves as a resource for analyzing ‘Social Media and India’s Foreign Policy: The Case Study of ‘X’ Diplomacy during the Covid-19 Pandemic.’ The data were collected through a web-based questionnaire distributed primarily to people aged 18 – 61 and above in India. A total of 171 valid data were collected from 17 states offering extensive geographic coverage and stored in Mendeley. The 15 contributor states are Goa, Maharashtra, Tamil Nadu, Gujarat, Delhi, Assam, Haryana, Jammu and Kashmir, Karnataka, Kerala, Punjab, Rajasthan, Tripura, Uttar Pradesh and West Bengal. It encompasses diverse question formats, including single-choice, multiple-choice, quizzes, and open-ended. The study underscores the opportunities and challenges of employing 'X' diplomacy in India's foreign policy. Thus, there were two hypotheses. First, India's effective use of 'X' diplomacy positively impacts public perception of India's foreign policy effectiveness. Second, India's adept use of 'X' diplomacy during the COVID-19 pandemic enhances its ability to manage and respond to the crisis effectively. This data shows public perception of the effective use of social media by the Government of India, particularly in the crisis situation. Data also highlight the significant change in India’s narrative through its ‘X’ diplomacy, effectively setting the narratives, public perceptions, and diplomatic strategies. This data can be fully utilized in the study of the significance of social media in India’s foreign policy, the role of social media like ‘X’ in the making of India’s foreign policy, how effective social media like ‘X’ was during the Covid-19 pandemic and how Indian government utilized social media like ‘X’ to delivered messages and to set the narrative in the international politics.

  20. F

    Audio Visual Speech Dataset: Indian English

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). Audio Visual Speech Dataset: Indian English [Dataset]. https://www.futurebeeai.com/dataset/multi-modal-dataset/indian-english-visual-speech-dataset
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the Indian English Language Visual Speech Dataset! This dataset is a collection of diverse, single-person unscripted spoken videos supporting research in visual speech recognition, emotion detection, and multimodal communication.

    Dataset Content

    This visual speech dataset contains 1000 videos in Indian English language each paired with a corresponding high-fidelity audio track. Each participant is answering a specific question in a video in an unscripted and spontaneous nature.

    Participant Diversity:
    Speakers: The dataset includes visual speech data from more than 200 participants from different states/provinces of India.
    Regions: Ensures a balanced representation of Skip 3 accents, dialects, and demographics.
    Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

    Video Data

    While recording each video extensive guidelines are kept in mind to maintain the quality and diversity.

    Recording Details:
    File Duration: Average duration of 30 seconds to 3 minutes per video.
    Formats: Videos are available in MP4 or MOV format.
    Resolution: Videos are recorded in ultra-high-definition resolution with 30 fps or above.
    Device: Both the latest Android and iOS devices are used in this collection.
    Recording Conditions: Videos were recorded under various conditions to ensure diversity and reduce bias:
    Indoor and Outdoor Settings: Includes both indoor and outdoor recordings.
    Lighting Variations: Captures videos in daytime, nighttime, and varying lighting conditions.
    Camera Positions: Includes handheld and fixed camera positions, as well as portrait and landscape orientations.
    Face Orientation: Contains straight face and tilted face angles.
    Participant Positions: Records participants in both standing and seated positions.
    Motion Variations: Features both stationary and moving videos, where participants pass through different lighting conditions.
    Occlusions: Includes videos where the participant's face is partially occluded by hand movements, microphones, hair, glasses, and facial hair.
    Focus: In each video, the participant's face remains in focus throughout the video duration, ensuring the face stays within the video frame.
    Video Content: In each video, the participant answers a specific question in an unscripted manner. These questions are designed to capture various emotions of participants. The dataset contain videos expressing following human emotions:
    Happy
    Sad
    Excited
    Angry
    Annoyed
    Normal
    Question Diversity: For each human emotion participant answered a specific question expressing that particular emotion.

    Metadata

    The dataset provides comprehensive metadata for each video recording and participant:

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TRADING ECONOMICS (2017). India - Rural Population [Dataset]. https://tradingeconomics.com/india/rural-population-percent-of-total-population-wb-data.html

India - Rural Population

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29 scholarly articles cite this dataset (View in Google Scholar)
excel, xml, json, csvAvailable download formats
Dataset updated
Jan 13, 2017
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Jan 1, 1976 - Dec 31, 2025
Area covered
India
Description

Rural population (% of total population) in India was reported at 63.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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