100+ datasets found
  1. Z

    Data from: A 24-hour dynamic population distribution dataset based on mobile...

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Feb 16, 2022
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    Henrikki Tenkanen (2022). A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4724388
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    Dataset updated
    Feb 16, 2022
    Dataset provided by
    Claudia Bergroth
    Tuuli Toivonen
    Olle Järv
    Matti Manninen
    Henrikki Tenkanen
    License

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

    Area covered
    Finland, Helsinki Metropolitan Area
    Description

    Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

    In this dataset:

    We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

    Please cite this dataset as:

    Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

    Organization of data

    The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

    HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

    HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

    HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

    target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

    Column names

    YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

    H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

    In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

    License Creative Commons Attribution 4.0 International.

    Related datasets

    Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

    Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

  2. f

    Distribution of waiting times and displacements: A comparison of over 30...

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 1, 2023
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    Laura Alessandretti; Piotr Sapiezynski; Sune Lehmann; Andrea Baronchelli (2023). Distribution of waiting times and displacements: A comparison of over 30 datasets on human mobility. [Dataset]. http://doi.org/10.1371/journal.pone.0171686.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Laura Alessandretti; Piotr Sapiezynski; Sune Lehmann; Andrea Baronchelli
    License

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

    Description

    The table reports for each dataset: the reference to the journal article/book where the study was published, the type of data (LBSN stands for Location Based Social Networks, CDR for Call Detail Record), the number of individuals (or vehicles in the case of car/taxi data) involved in the data collection, the duration of the data collection (M → months, Y → years, D → days, W → weeks), the minimum and maximum length of spatial displacements, the shape of the probability distribution of displacements with the corresponding parameters, the temporal sampling, the shape of the distribution of waiting times with the corresponding parameters. Power-law (T), indicates a truncated power-law. The table can also be found at http://lauraalessandretti.weebly.com/plosmobilityreview.html.

  3. u

    Historical Unidata Internet Data Distribution (IDD) Global Observational...

    • data.ucar.edu
    • rda-web-prod.ucar.edu
    • +2more
    netcdf
    Updated Jul 30, 2025
    + more versions
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    Unidata, University Corporation for Atmospheric Research (2025). Historical Unidata Internet Data Distribution (IDD) Global Observational Data [Dataset]. http://doi.org/10.5065/9235-WJ24
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    netcdfAvailable download formats
    Dataset updated
    Jul 30, 2025
    Dataset provided by
    Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory
    Authors
    Unidata, University Corporation for Atmospheric Research
    Time period covered
    Jan 1, 1970 - Dec 31, 2029
    Area covered
    Earth
    Description

    This dataset contains the historical Unidata Internet Data Distribution (IDD) Global Observational Data that are derived from real-time Global Telecommunications System (GTS) reports distributed via the Unidata Internet Data Distribution System (IDD). Reports include surface station (SYNOP) reports at 3-hour intervals, upper air (RAOB) reports at 3-hour intervals, surface station (METAR) reports at 1-hour intervals, and marine surface (BUOY) reports at 1-hour intervals. Select variables found in all report types include pressure, temperature, wind speed, and wind direction. Data may be available at mandatory or significant levels from 1000 millibars to 1 millibar, and at surface levels. Online archives are populated daily with reports generated two days prior to the current date.

  4. d

    Promote Implementation of the Model Data Distribution Policy

    • datadiscoverystudio.org
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    Promote Implementation of the Model Data Distribution Policy [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/283d1d85cae9449ebbd80089fd760ac4/html
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    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  5. n

    Real-World Distribution Network and Loading Data

    • data.ncl.ac.uk
    xlsx
    Updated Sep 1, 2021
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    Ilias Sarantakos; David Greenwood; Peter Davison; Haris Patsios (2021). Real-World Distribution Network and Loading Data [Dataset]. http://doi.org/10.25405/data.ncl.16456014.v1
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    xlsxAvailable download formats
    Dataset updated
    Sep 1, 2021
    Dataset provided by
    Newcastle University
    Authors
    Ilias Sarantakos; David Greenwood; Peter Davison; Haris Patsios
    License

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

    Area covered
    World
    Description

    Network and loading data for a real-world distribution network in the North-East of England.

  6. f

    Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS:...

    • frontiersin.figshare.com
    zip
    Updated Jun 2, 2023
    + more versions
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    Florian Loffing (2023). Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial.ZIP [Dataset]. http://doi.org/10.3389/fpsyg.2022.808469.s001
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    zipAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers
    Authors
    Florian Loffing
    License

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

    Description

    Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

  7. Data from: METASHIFT: A DATASET OF DATASETS FOR EVALUATING CONTEXTUAL...

    • zenodo.org
    bin, json, txt
    Updated Jul 7, 2022
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    Xinyu Yang; Xinyu Yang (2022). METASHIFT: A DATASET OF DATASETS FOR EVALUATING CONTEXTUAL DISTRIBUTION SHIFTS [Dataset]. http://doi.org/10.5281/zenodo.6804766
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    txt, json, binAvailable download formats
    Dataset updated
    Jul 7, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Xinyu Yang; Xinyu Yang
    License

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

    Description

    Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. In this work, we present MetaShift—a collection of 12,868 sets of natural images across 410 classes—to address this challenge. We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. The key construction idea is to cluster images using its metadata, which provides context for each image (e.g. cats with cars or cats in bathroom) that represent distinct data distributions. MetaShift has two important benefits: first, it contains orders of magnitude more natural data shifts than previously available. Second, it provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets. Importantly, to support evaluating ImageNet trained models on MetaShift, we match MetaShift with ImageNet hierarchy. The matched version covers 867 out of 1,000 classes in ImageNet-1k. Each class in the ImageNet-matched Metashift contains 2301.6 images on average, and 19.3 subsets capturing images in different contexts. We also propose a method to construct tasks on the matched version, giving an example to construct 19,024 binary classification tasks on it.

  8. d

    Global Drought Hazard Frequency and Distribution

    • catalog.data.gov
    • data.nasa.gov
    • +2more
    Updated Apr 24, 2025
    + more versions
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    SEDAC (2025). Global Drought Hazard Frequency and Distribution [Dataset]. https://catalog.data.gov/dataset/global-drought-hazard-frequency-and-distribution
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    SEDAC
    Description

    The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).

  9. Data distribution service USA Import & Buyer Data

    • seair.co.in
    Updated Nov 29, 2014
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    Seair Exim (2014). Data distribution service USA Import & Buyer Data [Dataset]. https://www.seair.co.in
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    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Nov 29, 2014
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    United States
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

  10. d

    Data for the occurrence and distribution of strontium in U.S. groundwater

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Data for the occurrence and distribution of strontium in U.S. groundwater [Dataset]. https://catalog.data.gov/dataset/data-for-the-occurrence-and-distribution-of-strontium-in-u-s-groundwater
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    United States
    Description

    Water-quality data for groundwater samples collected from 4,824 sites, and ancillary data and information on sampled wells and principal aquifers, were used to assess the occurrence and distribution of strontium in U.S. groundwater from 32 principal aquifers. This data release includes one tab-delimited text file detailing these data. Table 1. Chemical data from the U.S. Geological Survey National Water Information System and ancillary data considered for assessment of strontium concentration in U.S. groundwater.

  11. U

    Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) Data Distribution

    • data.usgs.gov
    • catalog.data.gov
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    Alan Haywood; Lauren Burton; Aisling Dolan; Harry Dowsett; Tamara Fletcher; Kevin Foley; Daniel Hill; Stephen Hunter; Marci Robinson; Julia Tindall, Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) Data Distribution [Dataset]. http://doi.org/10.5066/P937UZV5
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    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Alan Haywood; Lauren Burton; Aisling Dolan; Harry Dowsett; Tamara Fletcher; Kevin Foley; Daniel Hill; Stephen Hunter; Marci Robinson; Julia Tindall
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    Jul 1, 2023
    Description

    These files provide global coverage data describing boundary conditions for various aspects of the physical world representing several chosen times in Earth's history to be used as input data for climate modeling experiments. The raster data sets are provided in NetCDF format which is standard for climate modelling.

  12. w

    Income Distribution Database

    • data360.worldbank.org
    Updated Apr 18, 2025
    + more versions
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    (2025). Income Distribution Database [Dataset]. https://data360.worldbank.org/en/dataset/OECD_IDD
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    Dataset updated
    Apr 18, 2025
    Time period covered
    1974 - 2023
    Description

    The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

    Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

    Small changes in estimates between years should be treated with caution as they may not be statistically significant.

    Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

  13. Global Cyclone Mortality Risks and Distribution - Dataset - NASA Open Data...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Apr 23, 2025
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    nasa.gov (2025). Global Cyclone Mortality Risks and Distribution - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/global-cyclone-mortality-risks-and-distribution
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    Dataset updated
    Apr 23, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The Global Cyclone Mortality Risks and Distribution is a 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

  14. e

    Diverse Distribution And Marketing Services Pty L | See Full Import/Export...

    • eximpedia.app
    Updated Feb 13, 2025
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    Seair Exim (2025). Diverse Distribution And Marketing Services Pty L | See Full Import/Export Data | Eximpedia [Dataset]. https://www.eximpedia.app/
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    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset provided by
    Eximpedia Export Import Trade Data
    Eximpedia PTE LTD
    Authors
    Seair Exim
    Area covered
    Belarus, Armenia, United Arab Emirates, Uzbekistan, Mali, Togo, Finland, Croatia, Kuwait, American Samoa
    Description

    Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

  15. Bottlenose Dolphin Distribution - Dataset - data.gov.ie

    • data.gov.ie
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    data.gov.ie, Bottlenose Dolphin Distribution - Dataset - data.gov.ie [Dataset]. https://data.gov.ie/dataset/bottlenose-dolphin-distribution2
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    Dataset provided by
    data.gov.ie
    License

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

    Description

    Ireland’s marine waters host a rich and diverse range of species and habitats, including important fish spawning and nursery areas. Ecosystems provide a series of services for human well-being (ecosystem services) either directly (as food and fibre) or indirectly by providing clean air and water. Biodiversity plays a key role in the functioning of ecosystems and their ability to provide ecosystem services. The value of biodiversity and benefits from ecosystem services reach far beyond that which can be measured in financial terms. Evidence from monitoring of natural habitats and species in Ireland’s marine environment indicates that many habitats are not in good condition. Protecting and improving the condition of marine habitats and ecosystems is a challenge for all users of the sea.

  16. Population Distribution for Medi-Cal Enrollees by Met and Unmet Share of...

    • data.chhs.ca.gov
    • healthdata.gov
    • +1more
    csv, zip
    Updated Jun 5, 2025
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    Department of Health Care Services (2025). Population Distribution for Medi-Cal Enrollees by Met and Unmet Share of Cost (SOC) [Dataset]. https://data.chhs.ca.gov/dataset/population-distribution-for-medi-cal-enrollees-by-met-and-unmet-share-of-cost-soc
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    zip, csv(2389)Available download formats
    Dataset updated
    Jun 5, 2025
    Dataset provided by
    California Department of Health Care Serviceshttp://www.dhcs.ca.gov/
    Authors
    Department of Health Care Services
    Description

    This dataset represents the counts of those individuals who have been determined to have a share of cost (SOC) obligation, which is the monthly amount of medical expenses they must incur before they are eligible to receive Medi-Cal benefits. The dataset includes individuals who have a met or unmet monthly SOC obligation. Individuals who have not met their monthly SOC obligation are not eligible for Medi-Cal. SOC obligations are calculated during the eligibility determination process based on household income.

  17. Global Earthquake Hazard Frequency and Distribution - Dataset - NASA Open...

    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    • data.nasa.gov
    Updated Apr 23, 2025
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    nasa.gov (2025). Global Earthquake Hazard Frequency and Distribution - Dataset - NASA Open Data Portal [Dataset]. https://data.staging.idas-ds1.appdat.jsc.nasa.gov/dataset/global-earthquake-hazard-frequency-and-distribution
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    Dataset updated
    Apr 23, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The Global Earthquake Hazard Frequency and Distribution is a 2.5 minute grid utilizing Advanced National Seismic System (ANSS) Earthquake Catalog data of actual earthquake events exceeding 4.5 on the Richter scale during the time period 1976 through 2002. To produce the final output, the frequency of an earthquake hazard is calculated for each grid cell, and the resulting grid cells are then classified into deciles (10 classes consisting of an approxiamately equal number of grid cells). The greater the grid cell value in the final output, the higher the relative frequency of hazard posed by earthquakes. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).

  18. Global import data of Distribution Amplifier

    • volza.com
    csv
    Updated Jul 23, 2025
    + more versions
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    Volza FZ LLC (2025). Global import data of Distribution Amplifier [Dataset]. https://www.volza.com/p/distribution-amplifier/import/
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    csvAvailable download formats
    Dataset updated
    Jul 23, 2025
    Dataset provided by
    Authors
    Volza FZ LLC
    License

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

    Variables measured
    Count of importers, Sum of import value, 2014-01-01/2021-09-30, Count of import shipments
    Description

    667 Global import shipment records of Distribution Amplifier with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

  19. A

    Digitized NHANES II X-ray Films

    • data.amerigeoss.org
    html
    Updated Dec 7, 2020
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    United States (2020). Digitized NHANES II X-ray Films [Dataset]. https://data.amerigeoss.org/dataset/digitized-nhanes-ii-x-ray-films
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    htmlAvailable download formats
    Dataset updated
    Dec 7, 2020
    Dataset provided by
    United States
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    The National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics, Centers for Disease Control (NCHS/CDC), were designed to assess the health and nutritional status of adults and children in the United States through interviews and direct physical examinations. The NHANES radiographs were scanned by Dr. Bernie Huang at the University of California at Los Angeles and the University of California at San Francisco. Dr. Huang’s group used a Lumysis 100 with a 175 micron spot to scan the first 6000 radiographs. The remaining radiographs were scanned on the Lumysis 150 again with a 175 micron spot size. NOTE: This dataset is no-longer updated with new content.

  20. w

    Data from: Summer Steelhead Distribution [ds341]

    • data.wu.ac.at
    • data.cnra.ca.gov
    • +6more
    zip
    Updated Jan 2, 2018
    + more versions
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    State of California (2018). Summer Steelhead Distribution [ds341] [Dataset]. https://data.wu.ac.at/schema/data_gov/YjBmNWE5ZmItYTYwZS00M2NiLThmYzQtNjJlYjk1MzUwMGE5
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    zipAvailable download formats
    Dataset updated
    Jan 2, 2018
    Dataset provided by
    State of California
    Area covered
    40a193bc03e562cf8fc48e6f263326303f683d13
    Description

    Summer Steelhead Distribution October 2009 Version This dataset depicts observation-based stream-level geographic distribution of anadromous summer-run steelhead trout, Oncorhynchus mykiss irideus (O. mykiss), in California. It was developed for the express purpose of assisting with steelhead recovery planning efforts. The distributions reported in this dataset were derived from a subset of the data contained in the Aquatic Species Observation Database (ASOD), a Microsoft Access multi-species observation data capture application. ASOD is an ongoing project designed to capture as complete a set of statewide inland aquatic vertebrate species observation information as possible. Please note: A separate distribution is available for winter-run steelhead. Contact information is the same as for the above. ASOD Observation data were used to develop a network of stream segments. These lines are developed by "tracing down" from each observation to the sea using the flow properties of USGS National Hydrography Dataset (NHD) High Resolution hydrography. Lastly these lines, representing stream segments, were assigned a value of either Anad Present (Anadromous present). The end result (i.e., this layer) consists of a set of lines representing the distribution of steelhead based on observations in the Aquatic Species Observation Database. This dataset represents stream reaches that are known or believed to be used by steelhead based on steelhead observations. Thus, it contains only positive steelhead occurrences. The absence of distribution on a stream does not necessarily indicate that steelhead do not utilize that stream. Additionally, steelhead may not be found in all streams or reaches each year. This is due to natural variations in run size, water conditions, and other environmental factors. The information in this data set should be used as an indicator of steelhead presence/suspected presence at the time of the observation as indicated by the 'Late_Yr' (Latest Year) field attribute. The line features in the dataset may not represent the maximum extent of steelhead on a stream; rather it is important to note that this distribution most likely underestimates the actual distribution of steelhead. This distribution is based on observations found in the ASOD database. The individual observations may not have occurred at the upper extent of anadromous occupation. In addition, no attempt was made to capture every observation of O. mykiss and so it should not be assumed that this dataset is complete for each stream. The distribution dataset was built solely from the ASOD observational data. No additional data (habitat mapping, barriers data, gradient modeling, etc.) were utilized to either add to or validate the data. It is very possible that an anadromous observation in this dataset has been recorded above (upstream of) a barrier as identified in the Passage Assessment Database (PAD). In the near future, we hope to perform a comparative analysis between this dataset and the PAD to identify and resolve all such discrepancies. Such an analysis will add rigor to and help validate both datasets. This dataset has recently undergone a review. Data source contributors as well as CDFG fisheries biologists have been provided the opportunity to review and suggest edits or additions during a recent review. Data contributors were notified and invited to review and comment on the handling of the information that they provided. The distribution was then posted to an intranet mapping application and CDFG biologists were provided an opportunity to review and comment on the dataset. During this review, biologists were also encouraged to add new observation data. This resulting final distribution contains their suggestions and additions. Please refer to "Use Constraints" section below.

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Henrikki Tenkanen (2022). A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4724388

Data from: A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland

Related Article
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Dataset updated
Feb 16, 2022
Dataset provided by
Claudia Bergroth
Tuuli Toivonen
Olle Järv
Matti Manninen
Henrikki Tenkanen
License

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

Area covered
Finland, Helsinki Metropolitan Area
Description

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

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