86 datasets found
  1. N

    White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
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    Neilsberg Research (2025). White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in White Earth - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4baee3ca-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    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
    North Dakota, White Earth
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the White Earth population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of White Earth. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 42 (49.41% of the total population). 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.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the White Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in White Earth is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the White Earth is shown in the following column.

    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 White Earth Population by Age. You can refer the same here

  2. N

    Earth, TX Age Cohorts Dataset: Children, Working Adults, and Seniors in...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Earth, TX Age Cohorts Dataset: Children, Working Adults, and Seniors in Earth - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4b7c63d8-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    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
    Texas, Earth
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Earth population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Earth. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 447 (47.71% of the total population). 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.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Earth is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Earth is shown in the following column.

    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 Earth Population by Age. You can refer the same here

  3. Fish Dataset

    • kaggle.com
    Updated May 20, 2021
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    Alin Cijov (2021). Fish Dataset [Dataset]. https://www.kaggle.com/alincijov/fish-dataset/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 20, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Alin Cijov
    Description

    Dataset

    Camper dataset form https://stats.idre.ucla.edu/r/dae/zip/. The dataset contains data on 250 groups that went to a park. Each group was questioned about how many fish they caught (count), how many children were in the group (child), how many people were in the group (persons), if they used a live bait and whether or not they brought a camper to the park (camper). You split the data into train and test dataset.

    Acknowledgements

    University of California, Los Angeles (UCLA) Dataset.

  4. Child and Infant Mortality

    • kaggle.com
    Updated Aug 21, 2022
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    hrterhrter (2022). Child and Infant Mortality [Dataset]. https://www.kaggle.com/datasets/programmerrdai/child-and-infant-mortality
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 21, 2022
    Dataset provided by
    Kaggle
    Authors
    hrterhrter
    License

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

    Description

    One in every 100 children dies before completing one year of life. Around 68 percent of infant mortality is attributed to deaths of children before completing 1 month. 15,000 children die every day – Child mortality is an everyday tragedy of enormous scale that rarely makes the headlines Child mortality rates have declined in all world regions, but the world is not on track to reach the Sustainable Development Goal for child mortality Before the Modern Revolution child mortality was very high in all societies that we have knowledge of – a quarter of all children died in the first year of life, almost half died before reaching the end of puberty Over the last two centuries all countries in the world have made very rapid progress against child mortality. From 1800 to 1950 global mortality has halved from around 43% to 22.5%. Since 1950 the mortality rate has declined five-fold to 4.5% in 2015. All countries in the world have benefitted from this progress In the past it was very common for parents to see children die, because both, child mortality rates and fertility rates were very high. In Europe in the mid 18th century parents lost on average between 3 and 4 of their children Based on this overview we are asking where the world is today – where are children dying and what are they dying from?

    5.4 million children died in 2017 – Where did these children die? Pneumonia is the most common cause of death, preterm births and neonatal disorders is second, and diarrheal diseases are third – What are children today dying from? This is the basis for answering the question what can we do to make further progress against child mortality? We will extend this entry over the course of 2020.

    @article{owidchildmortality, author = {Max Roser, Hannah Ritchie and Bernadeta Dadonaite}, title = {Child and Infant Mortality}, journal = {Our World in Data}, year = {2013}, note = {https://ourworldindata.org/child-mortality} }

  5. g

    World Bank - Learning Poverty Global Database | gimi9.com

    • gimi9.com
    Updated Oct 18, 2019
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    (2019). World Bank - Learning Poverty Global Database | gimi9.com [Dataset]. https://gimi9.com/dataset/worldbank_wb_lpgd/
    Explore at:
    Dataset updated
    Oct 18, 2019
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10. For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

  6. M

    Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5

    • ceicdata.com
    Updated Nov 27, 2021
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    CEICdata.com (2021). Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 [Dataset]. https://www.ceicdata.com/en/mali/health-statistics/ml-prevalence-of-overweight-weight-for-height--of-children-under-5
    Explore at:
    Dataset updated
    Nov 27, 2021
    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, 1987 - Dec 1, 2015
    Area covered
    Mali
    Description

    Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

  7. Hindi Children Speech Dataset – 34 Hours (Real-world Conversation &...

    • nexdata.ai
    Updated Sep 12, 2025
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    Nexdata (2025). Hindi Children Speech Dataset – 34 Hours (Real-world Conversation & Monologue) [Dataset]. https://www.nexdata.ai/datasets/speechrecog/1377
    Explore at:
    Dataset updated
    Sep 12, 2025
    Dataset authored and provided by
    Nexdata
    Area covered
    World
    Variables measured
    Age, Format, Country, Accuracy, Language, Content category, Language(Region) Code, Recording environment, Features of annotation
    Description

    This dataset contains 34 hours of Hindi children’s speech.The recordings cover self-media, conversations, live talk, lectures, variety show and other generic domains, mirrors real-world interactions. Each utterance is transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

  8. World Day Against Child Labor

    • data.virginia.gov
    • catalog.data.gov
    html
    Updated Sep 6, 2025
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    Administration for Children and Families (2025). World Day Against Child Labor [Dataset]. https://data.virginia.gov/dataset/world-day-against-child-labor
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Sep 6, 2025
    Dataset provided by
    Administration for Children and Families
    Description

    Dear Partners,

    This month, the Administration for Children and Families (ACF) observed World Day Against Child Labor by spotlighting and encouraging those, who could, to join the Within and Beyond Our Borders: Collective Action to Address Hazardous Child Labor organized by the U.S. Department of Labor (DOL) on June 12, 2023. If you missed it, or would like to rewatch it, you can find it here

    .

    Since 2018, the DOL has seen a 69 percent increase in children being employed illegally by companies. In the last fiscal year, the department found that 835 companies it investigated had employed more than 3,800 children in violation of labor laws. There has been a 26 percent increase in children employed in hazardous occupations. These numbers tell us that we have work to do as the human services sector to learn more and become engaged in preventing unlawful child labor and supporting youth.

    As I have said before, child labor exploitation can disrupt a youth’s health, safety, education, and overall well-being, which are unacceptable consequences for any child. The Administration for Children and Families (ACF) supports a broad network of resources for vulnerable youth. We know that migrant and immigrant youth are especially vulnerable to exploitation, and it is often youth in or exiting the child welfare system who are targeted for various forms of exploitation. Child labor exploitation can impact children and youth across demographics.

    On March 24, 2023, the DOL and the U.S. Department of Health and Human Services (HHS) announced a Memorandum of Agreement - PDF

    to advance ongoing efforts to address child labor exploitation. In addition, DOL and HHS are collaborating on training and educational materials.

    As we expand this work, we know how important our partners throughout the country are in this effort. The Administration for Children and Families (ACF) is committed to addressing the increased presence of child labor exploitation through a variety of actions including equipping partners with materials and educational resources to build knowledge about child labor laws and rights, and remedies. This information is important for our human services sector and the children and families who may be most at risk.

    Please join ACF in increasing awareness and distributing resources to address child labor exploitation including the following:

    ACF resources may be also useful when working with a youth who has concerns about their safety. This includes the Family and Youth Services Bureau (FYSB)’s program on Runaway and Homeless Youth which provides a hotline for youth, concerned adults, and providers to access resources. At, www.1800runaway.org

    , their 24/7 crisis connection allows for calls, texts, live chat, and email to get information and resources.

    In addition, ACF’s Office of Trafficking In-Persons (OTIP) is an important resource for identifying and supporting survivors of trafficking. The National Human Trafficking Hotline

    provides a 24/7, confidential, multilingual hotline for victims, survivors, and witnesses of human trafficking. While labor exploitation should not be conflated with labor trafficking, in some cases labor exploitation may rise to meet the legal definitions of trafficking. The OTIP website

    contains many resources for grantees and communities on labor trafficking.

    Again, I hope you will continue to build awareness for yourself, your organization, or your community on child labor exploitation. It takes a whole community effort to support our children and youth.

    Most sincerely,

    January Contreras

    Metadata-only record linking to the original dataset. Open original dataset below.

  9. H

    2014 Global Hunger Index Data

    • dataverse.harvard.edu
    • dataone.org
    Updated Mar 31, 2017
    + more versions
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    Welthungerhilfe (WHH) (2017). 2014 Global Hunger Index Data [Dataset]. http://doi.org/10.7910/DVN/27557
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 31, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Welthungerhilfe (WHH)
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/27557

    Time period covered
    1990 - 2012
    Area covered
    CARIBBEAN; Commonwealth of Independent States; LATIN AMERICA; MIDDLE EAST; NORTH AFRICA; EAST AFRICA; EAST ASIA; SOUTH ASIA; EASTERN EUROPE; SOUTHERN AFRICA; AFRICA SOUTH OF SAHARA; AFRICA; ASIA;
    Description

    The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

  10. Prevalence of Child Malnutrition: Percent of Underweight Children

    • hub.arcgis.com
    Updated Dec 28, 2013
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    Esri Community Portal for GEOSS (2013). Prevalence of Child Malnutrition: Percent of Underweight Children [Dataset]. https://hub.arcgis.com/maps/9a3a4996234c458aa417044e9f5fceed
    Explore at:
    Dataset updated
    Dec 28, 2013
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Community Portal for GEOSS
    Area covered
    Description

    The Global Subnational Prevalence of Child Malnutrition dataset consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular dataset of the same and associated data. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

  11. w

    Dataset of books called Bringing up race : how to raise a kind child in a...

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books called Bringing up race : how to raise a kind child in a prejudiced world [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=book&fop0=%3D&fval0=Bringing+up+race+%3A+how+to+raise+a+kind+child+in+a+prejudiced+world
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

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

    Description

    This dataset is about books. It has 1 row and is filtered where the book is Bringing up race : how to raise a kind child in a prejudiced world. It features 7 columns including author, publication date, language, and book publisher.

  12. w

    Dataset of author, BNB id, book publisher, and publication date of Teaching...

    • workwithdata.com
    Updated Apr 17, 2025
    + more versions
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    Work With Data (2025). Dataset of author, BNB id, book publisher, and publication date of Teaching and learning in a diverse world : multicultural education for young children [Dataset]. https://www.workwithdata.com/datasets/books?col=author%2Cbnb_id%2Cbook%2Cbook%2Cbook_publisher%2Cpublication_date&f=1&fcol0=book&fop0=%3D&fval0=Teaching+and+learning+in+a+diverse+world+%3A+multicultural+education+for+young+children
    Explore at:
    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

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

    Description

    This dataset is about books. It has 2 rows and is filtered where the book is Teaching and learning in a diverse world : multicultural education for young children. It features 5 columns: author, publication date, book publisher, and BNB id.

  13. g

    World Bank - ID4D Global Dataset | gimi9.com

    • gimi9.com
    Updated May 10, 2025
    + more versions
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    (2025). World Bank - ID4D Global Dataset | gimi9.com [Dataset]. https://gimi9.com/dataset/worldbank_wb_id4d/
    Explore at:
    Dataset updated
    May 10, 2025
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The Identification for Development (ID4D) Global Dataset, compiled by the World Bank Group's Identification for Development (ID4D) Initiative, presents a collection of indicators that are of relevance for the estimation of adult and child ID coverage and for understanding foundational ID systems' digital capabilities. The indicators have been compiled from multiple sources, including a specialized ID module included in the Global Findex survey and officially recognized international sources such as UNICEF. Although there is no single, globally recognized measure of having a "proof of legal identity" that would cover children and adults at all ages or, of the digital capabilities of foundational ID systems, the combination of these indicators can help better understand where and what gaps in remain in accessing identification and, in turn, in accessing the services and transactions for which an official proof of identity is often required. Newly in 2022, adult ID ownership data is primarily based on survey data questions collected in partnership with the Global Findex Survey, while coverage for children is based on birth registration rates compiled by UNICEF. These data series are accessible directly from the World Bank's Databank: https://databank.worldbank.org/source/identification-for-development-(id4d)-data. Prior editions of the data from 2017 and 2018 are available for download here. Updates were released on a yearly basis until 2018; beginning in 2021-2022, the dataset will be released every three years to align with the Findex survey. For further details, please refer to https://id4d.worldbank.org/annual-reports This collection includes only a subset of indicators from the source dataset.

  14. w

    Dataset of book subjects that contain The adventures of Raoul the owl : a...

    • workwithdata.com
    Updated Nov 7, 2024
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    Work With Data (2024). Dataset of book subjects that contain The adventures of Raoul the owl : a story for children of all ages and parents of any age who believe the world is still a place of magic and fun [Dataset]. https://www.workwithdata.com/datasets/book-subjects?f=1&fcol0=j0-book&fop0=%3D&fval0=The+adventures+of+Raoul+the+owl+:+a+story+for+children+of+all+ages+and+parents+of+any+age+who+believe+the+world+is+still+a+place+of+magic+and+fun&j=1&j0=books
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    Dataset updated
    Nov 7, 2024
    Dataset authored and provided by
    Work With Data
    License

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

    Description

    This dataset is about book subjects. It has 1 row and is filtered where the books is The adventures of Raoul the owl : a story for children of all ages and parents of any age who believe the world is still a place of magic and fun. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

  15. U

    United States US: Prevalence of Underweight: Weight for Age: % of Children...

    • ceicdata.com
    Updated Nov 27, 2021
    + more versions
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    CEICdata.com (2021). United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 [Dataset]. https://www.ceicdata.com/en/united-states/health-statistics/us-prevalence-of-underweight-weight-for-age--of-children-under-5
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    Dataset updated
    Nov 27, 2021
    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, 1969 - Dec 1, 2012
    Area covered
    United States
    Description

    United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 0.500 % in 2012. This records a decrease from the previous number of 0.800 % for 2009. United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 0.900 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 1.100 % in 2005 and a record low of 0.500 % in 2012. United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

  16. r

    Health Behaviour in School-Aged Children, Sweden 2013/14

    • researchdata.se
    • demo.researchdata.se
    Updated Jul 1, 2025
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    Public Health Agency of Sweden (2025). Health Behaviour in School-Aged Children, Sweden 2013/14 [Dataset]. https://researchdata.se/en/catalogue/dataset/ext0160-1
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    (4658784), (4519941)Available download formats
    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    Public Health Agency of Sweden
    Time period covered
    Jan 2014
    Area covered
    Sweden
    Description

    The survey Schoolchildren's Health Habits is a part of the international research project Health Behaviour in School-aged Children, created on the initiative of the World Health Organization. Sweden participated in the survey since 1985/86.

    The study includes a random sample of 11 -, 13 - and 15-year-olds in each country. The survey covers questions about health, lifestyle, the environment at school and at home. The results are used to monitor the children's and young people's health over time and to identify areas requiring action to promote child and adolescent health. School children's health habits is a basis in the follow-up work in the public health policy goal area three, children and young people's living conditions.

    The study that was conducted in 2013/14 is based on data from 11-, 13- and 15-year-olds, collected in January 2014. The survey was answered by nearly 7,867 students in Sweden, representing a response rate of 69,4 percent.

    Purpose:

    The purpose with Health Behaviour in School-aged children is partly to increase the knowledge of habits and conditions that are considered to be important for children's health, and partly to monitor progress over time and to make comparisons with other countries.

  17. g

    CIESIN, Subnational Prevalence of Child Malnutrition, Global, 2005

    • geocommons.com
    Updated May 6, 2008
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    CIESIN Center for International Earth Science Information Network (Columbia University) (2008). CIESIN, Subnational Prevalence of Child Malnutrition, Global, 2005 [Dataset]. http://geocommons.com/search.html
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    Dataset updated
    May 6, 2008
    Dataset provided by
    data
    CIESIN Center for International Earth Science Information Network (Columbia University)
    Description

    DESCRIPTION Enclosed are data from CIESIN's Global subnational rates of child underweight status database. Further documentation for these data is available in the enclosed catalog and on the CIESIN Poverty Mapping web site at: http://www.ciesin.columbia.edu/povmap This is the beta release of this product. See the Poverty Mapping home page for additional information on the product. CITATION We recommend the following for citing the database: Center for International Earth Science Information Network (CIESIN), Columbia University; 2005 Global subnational rates of child underweight status [dataset]. CIESIN, Palisades, NY, USA. Available at: http://www.ciesin.columbia.edu/povmap/ds_global.html

  18. e

    North Surrey school district data 1881-1895 - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Apr 10, 2023
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    (2023). North Surrey school district data 1881-1895 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/bc0ea3d1-5090-5077-92b4-e51c8cbf4a95
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    Dataset updated
    Apr 10, 2023
    Area covered
    Surrey
    Description

    This dataset includes all children (n = 4,310) who were admitted to the North Surrey School District between 1881 and 1895. The record of children includes information about the children’s ages, heights and weights, parents’ occupations and addresses, their training in the school and their work and welfare after leaving the school. The heights and weights of children at discharge from the school were less frequently recorded (n = 173).This project will explore how improvements in nutrition, sanitation, and medical knowledge during Britain's long-run health transition from 1850 onwards influenced children's growth pattern in terms of height, weight and BMI. Studying children's growth pattern (velocity of growth and shape of the growth curve) rather than their height at a specific age is a significant methodological innovation. Adaptive theories of human development and growth stress how exposure to poor nutrition or disease, especially in utero, does not merely affect the child's current height but also the timing of the pubertal growth spurt, their velocity of growth and the length of the growing period: in other words, their growth pattern. This project will extend existing knowledge of children's growth in Britain in three ways: first, by reconstructing boys' longitudinal growth measurements from training ship records spanning the century and a half from 1865 onwards; second, by producing and analysing new growth profiles from historical sources; and third, by placing the change in Britain's growth pattern in international context using growth profiles (the average height and weight of children across a number of ages) collected from 1850 to the present from around the world. Four new datasets will be produced and deposited in the UK Data Archive as a part of the project: three individual-level datasets with the heights and weights of children and a dataset with growth profiles for a wide range of countries around the world from 1850 to the present. The data produced will supply a longer-run perspective on the immediate and intergenerational factors influencing children's growth patterns in Britain and internationally and indicate how the shift from an unhealthy to healthy growth pattern took place. The data will also assemble new evidence on historical BMI growth curves and child obesity rates, providing historical context for the current child obesity crisis. The project's findings are particularly relevant to the current discussion about a post-2015 development framework to replace the Millennium Development Goals and to understanding the childhood obesity crisis and will inform health interventions and development policy goals for improving the health of children in both the developing and developed worlds. The data were transcribed from the record of children kept by the North Surrey School District about the children who lived at the school. A detailed description of the transcription methods and process is provided in the documentation.

  19. w

    Dataset of book subjects that contain The infinite gift : how children learn...

    • workwithdata.com
    Updated Nov 7, 2024
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    Work With Data (2024). Dataset of book subjects that contain The infinite gift : how children learn and unlearn the languages of the world [Dataset]. https://www.workwithdata.com/datasets/book-subjects?f=1&fcol0=j0-book&fop0=%3D&fval0=The+infinite+gift+:+how+children+learn+and+unlearn+the+languages+of+the+world&j=1&j0=books
    Explore at:
    Dataset updated
    Nov 7, 2024
    Dataset authored and provided by
    Work With Data
    License

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

    Description

    This dataset is about book subjects. It has 1 row and is filtered where the books is The infinite gift : how children learn and unlearn the languages of the world. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

  20. 93 Hours - Korean(Korea) Children Real-world Casual Conversation and...

    • m.nexdata.ai
    • nexdata.ai
    Updated Feb 1, 2024
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    Nexdata (2024). 93 Hours - Korean(Korea) Children Real-world Casual Conversation and Monologue speech dataset [Dataset]. https://m.nexdata.ai/datasets/speechrecog/1329?source=Datarade
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    Dataset updated
    Feb 1, 2024
    Dataset authored and provided by
    Nexdata
    Area covered
    Korea, South Korea, World
    Variables measured
    Age, Format, Country, Accuracy, Language, Content category, Language(Region) Code, Recording environment, Features of annotation
    Description

    Korean(Korea) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

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Neilsberg Research (2025). White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in White Earth - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4baee3ca-f122-11ef-8c1b-3860777c1fe6/

White Earth, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in White Earth - Population and Percentage Analysis // 2025 Edition

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json, csvAvailable download formats
Dataset updated
Feb 22, 2025
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
North Dakota, White Earth
Variables measured
Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
Measurement technique
The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
Dataset funded by
Neilsberg Research
Description
About this dataset

Context

The dataset tabulates the White Earth population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of White Earth. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 42 (49.41% of the total population). 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.

Age cohorts:

  • Under 18 years
  • 18 to 64 years
  • 65 years and over

Variables / Data Columns

  • Age Group: This column displays the age cohort for the White Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
  • Population: The population for the age cohort in White Earth is shown in the following column.
  • Percent of Total Population: The population as a percent of total population of the White Earth is shown in the following column.

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 White Earth Population by Age. You can refer the same here

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