100+ datasets found
  1. N

    Spray, OR Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Jul 24, 2024
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    Neilsberg Research (2024). Spray, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/f04de1bc-4983-11ef-ae5d-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Jul 24, 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
    OR, Spray
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. 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 data for the Spray, OR population pyramid, which represents the Spray population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Spray, OR, is 34.0.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Spray, OR, is 65.0.
    • Total dependency ratio for Spray, OR is 99.0.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Spray, OR is 1.5.
    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Spray population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Spray for the selected age group is shown in the following column.
    • Population (Female): The female population in the Spray for the selected age group is shown in the following column.
    • Total Population: The total population of the Spray for the selected age group 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 Spray Population by Age. You can refer the same here

  2. N

    Stanfield, OR Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Sep 16, 2023
    + more versions
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    Neilsberg Research (2023). Stanfield, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis [Dataset]. https://www.neilsberg.com/research/datasets/636af846-3d85-11ee-9abe-0aa64bf2eeb2/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Sep 16, 2023
    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
    Stanfield
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. 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 data for the Stanfield, OR population pyramid, which represents the Stanfield population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Stanfield, OR, is 27.5.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Stanfield, OR, is 18.6.
    • Total dependency ratio for Stanfield, OR is 46.0.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Stanfield, OR is 5.4.
    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Stanfield population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Stanfield for the selected age group is shown in the following column.
    • Population (Female): The female population in the Stanfield for the selected age group is shown in the following column.
    • Total Population: The total population of the Stanfield for the selected age group 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 Stanfield Population by Age. You can refer the same here

  3. N

    Canyonville, OR Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Jul 24, 2024
    + more versions
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    Neilsberg Research (2024). Canyonville, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/f01579da-4983-11ef-ae5d-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Jul 24, 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
    Canyonville
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. 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 data for the Canyonville, OR population pyramid, which represents the Canyonville population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

    Key observations

    • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Canyonville, OR, is 33.7.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Canyonville, OR, is 67.0.
    • Total dependency ratio for Canyonville, OR is 100.7.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Canyonville, OR is 1.5.
    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group for the Canyonville population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Canyonville for the selected age group is shown in the following column.
    • Population (Female): The female population in the Canyonville for the selected age group is shown in the following column.
    • Total Population: The total population of the Canyonville for the selected age group 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 Canyonville Population by Age. You can refer the same here

  4. d

    Hydraulics, DARLINGTON COUNTY, SC

    • catalog.data.gov
    Updated Jan 7, 2010
    + more versions
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    Watershed Concepts (Point of Contact) (2010). Hydraulics, DARLINGTON COUNTY, SC [Dataset]. https://catalog.data.gov/is/dataset/hydraulics-darlington-county-sc
    Explore at:
    Dataset updated
    Jan 7, 2010
    Dataset provided by
    Watershed Concepts (Point of Contact)
    Area covered
    Darlington County, South Carolina
    Description

    Recent developments in digital terrain and geospatial database management technology make it possible to protect this investment for existing and future projects to a much greater extent than was possible in the past. The minimum requirement for hydraulics data includes input and output files for all hydraulic models and spatial datasets that are needed to implement the models. (Source: FEMA Guidelines and Specs, Appendix N)

  5. t

    UNITS IN STRUCTURE - DP04_SAR_ZIP - Dataset - CKAN

    • portal.tad3.org
    Updated Jul 23, 2023
    + more versions
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    (2023). UNITS IN STRUCTURE - DP04_SAR_ZIP - Dataset - CKAN [Dataset]. https://portal.tad3.org/dataset/units-in-structure-dp04_sar_zip
    Explore at:
    Dataset updated
    Jul 23, 2023
    License

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

    Description

    SELECTED HOUSING CHARACTERISTICS UNITS IN STRUCTURE - DP04 Universe -Total housing units Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 A structure is a separate building that either has open spaces on all sides or is separated from other structures by dividing walls that extend from ground to roof. In determining the number of units in a structure, all housing units, both occupied and vacant, are counted. Stores and office space are excluded. The data are presented for the number of housing units in structures of specified type and size, not for the number of residential buildings.

  6. a

    Residential Parking Permit Zones

    • portal-nolagis.opendata.arcgis.com
    • data.nola.gov
    • +4more
    Updated Aug 29, 2016
    + more versions
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    City of New Orleans (2016). Residential Parking Permit Zones [Dataset]. https://portal-nolagis.opendata.arcgis.com/maps/68feb0b34b4c4a54a9588a8a76e64d8a
    Explore at:
    Dataset updated
    Aug 29, 2016
    Dataset authored and provided by
    City of New Orleans
    Area covered
    Description

    These 17 zones are maintained by the Department of Public Works. Residential Parking permits are valid for specific neighborhoods and for a specific duration of time. Applications plus full information on fees and required documents are available at: http://www.nola.gov/dpw/residential-parking-permit/

  7. NParks Tracks

    • dataportal.asia
    • cloud.csiss.gmu.edu
    kml
    Updated Sep 24, 2019
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    data.gov.sg (2019). NParks Tracks [Dataset]. https://dataportal.asia/id/dataset/b2dcf7c7-a159-4493-b8f4-edf4f3c1b612
    Explore at:
    kmlAvailable download formats
    Dataset updated
    Sep 24, 2019
    Dataset provided by
    Data.govhttps://data.gov/
    Description

    This dataset provides the indicative locations of the tracks within areas managed by the National Parks Board.

    Also included are the Park Connector Networks. Where possible, the tracks within parks and nature reserves are connected to the park connector network.

  8. a

    Florida COVID19 20200908 ByCounty

    • covid19-usflibrary.hub.arcgis.com
    Updated Jun 7, 2021
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    University of South Florida GIS (2021). Florida COVID19 20200908 ByCounty [Dataset]. https://covid19-usflibrary.hub.arcgis.com/datasets/florida-covid19-20200908-bycounty
    Explore at:
    Dataset updated
    Jun 7, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Description

    Florida COVID-19 Cases by County exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020-2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/ . https://doi.org/10.5038/USF-COVID-19-GISLive FDOH DataSource: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_COVID19_Cases/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. Up until 3/25 the FDOH Cases by County layer was updated twice a day, archives are taken from the 11AM update.For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Persons Under Investigation/Surveillance (PUI):Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results. All PUIs fit into one of three residency types: 1. Florida residents tested in Florida2. Non-Florida residents tested in Florida3. Florida residents tested outside of Florida Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outside of Florida, and were not exposed/infectious in Florida.Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state. Total Cases: The total (sum) number of Persons Under Investigation (PUI) who tested positive for COVID-19 while in Florida, as well as Florida residents who tested positive or were exposed/contagious while outside of Florida, and out-of-state residents who were exposed, contagious and/or tested in Florida.Deaths: The Deaths by Day chart shows the total number of Florida residents with confirmed COVID-19 that died on each calendar day (12:00 AM - 11:59 PM). Caution should be used in interpreting recent trends, as deaths are added as they are reported to the Department. Death data often has significant delays in reporting, so data within the past two weeks will be updated frequently.Prefix guide: "PUI" = PUI: Persons under surveillance (any person for which we have data about)"T_ " = Testing: Testing information for all PUIs and cases."C_" = Cases only: Information about cases, which are those persons who have COVID-19 positive test results on file“W_” = Surveillance and syndromic dataKey Data about Testing:T_negative : Testing: Total negative persons tested for all Florida and non-Florida residents, including Florida residents tested outside of the state, and those tested at private facilities.T_positive : Testing: Total positive persons tested for all Florida and non-Florida resident types, including Florida residents tested outside of the state, and those tested at private facilities.PUILab_Yes : All persons tested with lab results on file, including negative, positive and inconclusive. This total does NOT include those who are waiting to be tested or have submitted tests to labs for which results are still pending.Key Data about Confirmed COVID-19 Positive Cases: CasesAll: Cases only: The sum total of all positive cases, including Florida residents in Florida, Florida residents outside Florida, and non-Florida residents in FloridaFLResDeaths: Deaths of Florida ResidentsC_Hosp_Yes : Cases (confirmed positive) with a hospital admission notedC_AgeRange Cases Only: Age range for all cases, regardless of residency typeC_AgeMedian: Cases Only: Median range for all cases, regardless of residency typeC_AllResTypes : Cases Only: Sum of COVID-19 positive Florida Residents; includes in and out of state Florida residents, but does not include out-of-state residents who were treated/tested/isolated in Florida. All questions regarding this dataset should be directed to the Florida Department of Health.

  9. I

    sp028-20160628T1737

    • data.ioos.us
    • catalog.data.gov
    erddap +2
    Updated Dec 14, 2022
    + more versions
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    Glider DAC (2022). sp028-20160628T1737 [Dataset]. https://data.ioos.us/is/dataset/sp028-20160628t1737
    Explore at:
    erddap, opendap, erddap-tabledapAvailable download formats
    Dataset updated
    Dec 14, 2022
    Dataset provided by
    Glider DAC
    Time period covered
    Jun 28, 2016 - Aug 11, 2016
    Area covered
    Variables measured
    time, depth, latitude, longitude, sea_water_density, sea_water_pressure, sea_water_salinity, sea_water_temperature, eastward_sea_water_velocity, northward_sea_water_velocity, and 2 more
    Description

    Spray glider profile data from Scripps Institution of Oceanography Instrument Development Group (supported by NOAA).

  10. A

    Annual Water Management Program Report Prime Hook National Wildlife Refuge...

    • data.amerigeoss.org
    • datadiscoverystudio.org
    pdf
    Updated Jul 26, 2019
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    United States[old] (2019). Annual Water Management Program Report Prime Hook National Wildlife Refuge 1992 [Dataset]. https://data.amerigeoss.org/is/dataset/2ddbbc54-a9be-49da-8d4c-edb40ed61243
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jul 26, 2019
    Dataset provided by
    United States[old]
    Description

    This report outlines current and past water management efforts at Prime Hook National Wildlife Refuge. It starts with the history of water management, including the effects of water management in 1991. The report also includes a detailed outline of plans for Water Management in 1992.

  11. Center for Devices and Radiological Health Device Exemptions

    • johnsnowlabs.com
    csv
    + more versions
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    John Snow Labs, Center for Devices and Radiological Health Device Exemptions [Dataset]. https://www.johnsnowlabs.com/marketplace/center-for-devices-and-radiological-health-device-exemptions/
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    John Snow Labs
    Area covered
    United States
    Description

    The Center for Devices and Radiological Health (CDRH) Humanitarian Device Exemptions dataset includes the list of Humanitarian Device Exemptions delivered by the Food and Drug Administration (FDA). A Humanitarian Use Device (HUD) is a device that is intended to benefit patients by treating or diagnosing a disease or condition that affects or is manifested in fewer than 4,000 individuals in the United States per year.

  12. g

    Lea County 2010 Census County Subdivision County-based | gimi9.com

    • gimi9.com
    Updated Mar 12, 2011
    + more versions
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    (2011). Lea County 2010 Census County Subdivision County-based | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_lea-county-2010-census-county-subdivision-county-based/
    Explore at:
    Dataset updated
    Mar 12, 2011
    License

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

    Area covered
    Lea County
    Description

    The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data, and they include legally minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the legal MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas is covered by county subdivisions. The boundaries of all 2010 Census legal MCDs are as of January 1, 2010 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). For the 2010 Census, CCDs or their equivalents are delineated in 21 States. The boundaries of all 2010 Census statistical CCDs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP).

  13. d

    NASA 3D Models: Cassini

    • catalog.data.gov
    • s.cnmilf.com
    • +3more
    Updated Jun 25, 2018
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    National Aeronautics and Space Administration (2018). NASA 3D Models: Cassini [Dataset]. https://catalog.data.gov/ru/dataset/nasa-3d-models-cassini
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    Dataset updated
    Jun 25, 2018
    Dataset provided by
    National Aeronautics and Space Administration
    Description

    Cassini spacecraft model.

  14. TIGER/Line Shapefile, 2022, State, North Carolina, NC, County Subdivision

    • datasets.ai
    • gimi9.com
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    23, 55, 57
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    U.S. Census Bureau, Department of Commerce, TIGER/Line Shapefile, 2022, State, North Carolina, NC, County Subdivision [Dataset]. https://datasets.ai/datasets/tiger-line-shapefile-2022-state-north-carolina-nc-county-subdivision
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    23, 55, 57Available download formats
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    U.S. Census Bureau, Department of Commerce
    Area covered
    North Carolina
    Description

    The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The boundaries of most legal MCDs are as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

  15. Census 2000 Tabulation Geography Tallies

    • catalog.data.gov
    • data.amerigeoss.org
    • +1more
    Updated Oct 30, 2001
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    US Census Bureau, Department of Commerce (2001). Census 2000 Tabulation Geography Tallies [Dataset]. https://catalog.data.gov/is/dataset/census-2000-tabulation-geography-tallies
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    Dataset updated
    Oct 30, 2001
    Dataset provided by
    United States Department of Commercehttp://www.commerce.gov/
    Description

    Tallies of Census Tracts, Block Groups and Tabulation Blocks, by state.

  16. w

    Tundra swan population survey in Bristol Bay, northern Alaska Peninsula,...

    • data.wu.ac.at
    • datadiscoverystudio.org
    • +1more
    pdf
    Updated Feb 1, 2011
    + more versions
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    Department of the Interior (2011). Tundra swan population survey in Bristol Bay, northern Alaska Peninsula, 2003 and 2008 [Dataset]. https://data.wu.ac.at/schema/data_gov/YWM5M2NlOWItOGVjYS00OTU0LWJiYjctMzQxOTRkMzJkZTRj
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    pdfAvailable download formats
    Dataset updated
    Feb 1, 2011
    Dataset provided by
    Department of the Interior
    Area covered
    Bristol Bay, Alaska Peninsula, 6b75d48eab5a7e70eaea0c4000bfd6a053545f88
    Description

    Tundra swan (Cygnus columbianus) population surveys were conducted in spring and late summer of 2003 and 2008 according to the schedule established in the Alaska Peninsula/Becharof NWR (Refuge) draft Wildlife Inventory Plan (WIP). In 2003 WIP procedures were followed with two observers. Prior to the 2008 survey year, a series of questions were asked about survey design and sample size resulting in modified survey methods, data collection, and management; the 2008 survey included three observers who mapped all observations. This report outlines the survey methods and preliminary results from each survey.

  17. Dataset for the publication "Theory and Experimental Validation of Two...

    • zenodo.org
    • explore.openaire.eu
    • +1more
    bin
    Updated Nov 16, 2023
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    D'Avanzo Giovanni; Faifer Marco; Landi Carmine; Laurano Christian; Letizia Palma Sara; Luiso Mario; Ottoboni Roberto; Toscani Sergio; D'Avanzo Giovanni; Faifer Marco; Landi Carmine; Laurano Christian; Letizia Palma Sara; Luiso Mario; Ottoboni Roberto; Toscani Sergio (2023). Dataset for the publication "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities" [Dataset]. http://doi.org/10.5281/zenodo.7436628
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    binAvailable download formats
    Dataset updated
    Nov 16, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    D'Avanzo Giovanni; Faifer Marco; Landi Carmine; Laurano Christian; Letizia Palma Sara; Luiso Mario; Ottoboni Roberto; Toscani Sergio; D'Avanzo Giovanni; Faifer Marco; Landi Carmine; Laurano Christian; Letizia Palma Sara; Luiso Mario; Ottoboni Roberto; Toscani Sergio
    License

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

    Description

    This is dataset for paper published:

    G. D’Avanzo et al., "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022, Art no. 9001312, doi: 10.1109/TIM.2022.3147883.

  18. [s236912] [RT1-CE12]

    • thermofisher.cn
    Updated Jul 24, 2021
    + more versions
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    Thermo Fisher Scientific (2021). [s236912] [RT1-CE12] [Dataset]. https://www.thermofisher.cn/order/genome-database/details/sirna/s236912
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    Dataset updated
    Jul 24, 2021
    Dataset authored and provided by
    Thermo Fisher Scientifichttp://thermofisher.com/
    Description

    [Gene description is missing or is less than 50 characters]

  19. a

    Florida COVID19 20200713 ByCounty

    • covid19-usflibrary.hub.arcgis.com
    Updated Jun 9, 2021
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    University of South Florida GIS (2021). Florida COVID19 20200713 ByCounty [Dataset]. https://covid19-usflibrary.hub.arcgis.com/datasets/florida-covid19-20200713-bycounty
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    Dataset updated
    Jun 9, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Description

    Florida COVID-19 Cases by County exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020-2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/ . https://doi.org/10.5038/USF-COVID-19-GISLive FDOH DataSource: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_COVID19_Cases/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. Up until 3/25 the FDOH Cases by County layer was updated twice a day, archives are taken from the 11AM update.For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Persons Under Investigation/Surveillance (PUI):Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results. All PUIs fit into one of three residency types: 1. Florida residents tested in Florida2. Non-Florida residents tested in Florida3. Florida residents tested outside of Florida Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outside of Florida, and were not exposed/infectious in Florida.Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state. Total Cases: The total (sum) number of Persons Under Investigation (PUI) who tested positive for COVID-19 while in Florida, as well as Florida residents who tested positive or were exposed/contagious while outside of Florida, and out-of-state residents who were exposed, contagious and/or tested in Florida.Deaths: The Deaths by Day chart shows the total number of Florida residents with confirmed COVID-19 that died on each calendar day (12:00 AM - 11:59 PM). Caution should be used in interpreting recent trends, as deaths are added as they are reported to the Department. Death data often has significant delays in reporting, so data within the past two weeks will be updated frequently.Prefix guide: "PUI" = PUI: Persons under surveillance (any person for which we have data about)"T_ " = Testing: Testing information for all PUIs and cases."C_" = Cases only: Information about cases, which are those persons who have COVID-19 positive test results on file“W_” = Surveillance and syndromic dataKey Data about Testing:T_negative : Testing: Total negative persons tested for all Florida and non-Florida residents, including Florida residents tested outside of the state, and those tested at private facilities.T_positive : Testing: Total positive persons tested for all Florida and non-Florida resident types, including Florida residents tested outside of the state, and those tested at private facilities.PUILab_Yes : All persons tested with lab results on file, including negative, positive and inconclusive. This total does NOT include those who are waiting to be tested or have submitted tests to labs for which results are still pending.Key Data about Confirmed COVID-19 Positive Cases: CasesAll: Cases only: The sum total of all positive cases, including Florida residents in Florida, Florida residents outside Florida, and non-Florida residents in FloridaFLResDeaths: Deaths of Florida ResidentsC_Hosp_Yes : Cases (confirmed positive) with a hospital admission notedC_AgeRange Cases Only: Age range for all cases, regardless of residency typeC_AgeMedian: Cases Only: Median range for all cases, regardless of residency typeC_AllResTypes : Cases Only: Sum of COVID-19 positive Florida Residents; includes in and out of state Florida residents, but does not include out-of-state residents who were treated/tested/isolated in Florida. All questions regarding this dataset should be directed to the Florida Department of Health.

  20. F

    France Foreign Direct Investment Financial Flows: Outward: Total: Saint...

    • ceicdata.com
    Updated Dec 24, 2024
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    CEICdata.com (2024). France Foreign Direct Investment Financial Flows: Outward: Total: Saint Lucia [Dataset]. https://www.ceicdata.com/en/france/foreign-direct-investment-financial-flows-by-region-and-country-oecd-member-annual/foreign-direct-investment-financial-flows-outward-total-saint-lucia
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    Dataset updated
    Dec 24, 2024
    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, 2018 - Dec 1, 2020
    Area covered
    France
    Description

    France Foreign Direct Investment Financial Flows: Outward: Total: Saint Lucia data was reported at 0.000 EUR mn in 2020. This records an increase from the previous number of -1.000 EUR mn for 2019. France Foreign Direct Investment Financial Flows: Outward: Total: Saint Lucia data is updated yearly, averaging 0.000 EUR mn from Dec 2018 (Median) to 2020, with 3 observations. The data reached an all-time high of 1.000 EUR mn in 2018 and a record low of -1.000 EUR mn in 2019. France Foreign Direct Investment Financial Flows: Outward: Total: Saint Lucia data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s France – Table FR.OECD.FDI: Foreign Direct Investment Financial Flows: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) has never been observed or is very negligible. It would be treated as portfolio investment in theory. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.

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Neilsberg Research (2024). Spray, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/f04de1bc-4983-11ef-ae5d-3860777c1fe6/

Spray, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2024 Edition

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json, csvAvailable download formats
Dataset updated
Jul 24, 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
OR, Spray
Variables measured
Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
Measurement technique
The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. 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 data for the Spray, OR population pyramid, which represents the Spray population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

  • Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Spray, OR, is 34.0.
  • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Spray, OR, is 65.0.
  • Total dependency ratio for Spray, OR is 99.0.
  • Potential support ratio, which is the number of youth (working age population) per elderly, for Spray, OR is 1.5.
Content

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

Age groups:

  • Under 5 years
  • 5 to 9 years
  • 10 to 14 years
  • 15 to 19 years
  • 20 to 24 years
  • 25 to 29 years
  • 30 to 34 years
  • 35 to 39 years
  • 40 to 44 years
  • 45 to 49 years
  • 50 to 54 years
  • 55 to 59 years
  • 60 to 64 years
  • 65 to 69 years
  • 70 to 74 years
  • 75 to 79 years
  • 80 to 84 years
  • 85 years and over

Variables / Data Columns

  • Age Group: This column displays the age group for the Spray population analysis. Total expected values are 18 and are define above in the age groups section.
  • Population (Male): The male population in the Spray for the selected age group is shown in the following column.
  • Population (Female): The female population in the Spray for the selected age group is shown in the following column.
  • Total Population: The total population of the Spray for the selected age group 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 Spray Population by Age. You can refer the same here

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