100+ datasets found
  1. Z

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

    • data.niaid.nih.gov
    • zenodo.org
    Updated Feb 16, 2022
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    Claudia Bergroth; Olle Järv; Henrikki Tenkanen; Matti Manninen; Tuuli Toivonen (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
    Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki
    Department of Built Environment, Aalto University / Centre for Advanced Spatial Analysis, University College London
    Unit of Urban Research and Statistics, City of Helsinki / Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki
    Elisa Corporation
    Authors
    Claudia Bergroth; Olle Järv; Henrikki Tenkanen; Matti Manninen; Tuuli Toivonen
    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. 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
    PLOShttp://plos.org/
    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. n

    Real-World Distribution Network and Loading Data

    • data.ncl.ac.uk
    • resodate.org
    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.

  4. D

    Market Data Distribution Platforms Market Research Report 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 30, 2025
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    Dataintelo (2025). Market Data Distribution Platforms Market Research Report 2033 [Dataset]. https://dataintelo.com/report/market-data-distribution-platforms-market
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    pptx, pdf, csvAvailable download formats
    Dataset updated
    Sep 30, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Market Data Distribution Platforms Market Outlook



    According to our latest research, the market size of the global Market Data Distribution Platforms Market reached USD 8.7 billion in 2024, with a robust growth trajectory supported by a CAGR of 9.1% projected for the period 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 19.1 billion. This remarkable growth is primarily driven by the increasing demand for real-time data analytics and the rising adoption of cloud-based distribution solutions across financial institutions, telecommunications, and other data-intensive sectors. As per our latest research, the proliferation of algorithmic trading, regulatory mandates for transparency, and digital transformation initiatives are further propelling the adoption of advanced market data distribution platforms globally.



    One of the most significant growth factors for the Market Data Distribution Platforms Market is the exponential rise in data volumes generated by financial markets and other industries. The surge in electronic trading, high-frequency trading, and the adoption of algorithmic strategies have necessitated the need for platforms that can distribute large volumes of market data with minimal latency and maximum reliability. Financial institutions, in particular, require real-time access to market data to make informed trading decisions and to comply with stringent regulatory requirements. The increasing complexity of financial instruments and the globalization of trading activities have made efficient data distribution a critical component of the financial services infrastructure. Furthermore, the growing integration of alternative data sources, such as social media sentiment and geospatial data, is pushing market data distribution platforms to evolve, ensuring they can handle diverse data types while maintaining speed and accuracy.



    Another key driver is the widespread adoption of cloud technology and the shift towards hybrid IT environments. Organizations across sectors are recognizing the benefits of cloud-based market data distribution platforms, including scalability, flexibility, and cost efficiency. Cloud deployment allows enterprises to manage and distribute data seamlessly across geographically dispersed teams and trading desks, supporting business continuity and operational agility. Additionally, cloud platforms offer enhanced security features, disaster recovery capabilities, and the ability to integrate with advanced analytics and artificial intelligence tools. These advantages are particularly appealing to small and medium enterprises (SMEs), which may lack the resources to maintain extensive on-premises infrastructure but still require robust market data solutions to remain competitive.



    The increasing regulatory scrutiny and the need for transparency in financial transactions are also fueling the demand for advanced market data distribution platforms. Regulatory bodies worldwide are enforcing rules that mandate accurate and timely dissemination of market data to ensure fair trading practices and to protect investors. Market participants must adhere to regulations such as MiFID II in Europe and the Dodd-Frank Act in the United States, which impose strict requirements on data reporting, order execution, and market surveillance. Compliance with these regulations necessitates the deployment of sophisticated data distribution systems capable of supporting real-time monitoring, audit trails, and secure data sharing. This regulatory landscape is compelling financial institutions and other end-users to upgrade their existing platforms or invest in new solutions that offer enhanced compliance features and reporting capabilities.



    From a regional perspective, North America continues to hold the largest share of the Market Data Distribution Platforms Market, driven by the presence of major financial hubs, advanced IT infrastructure, and early adoption of innovative technologies. The United States, in particular, is home to leading financial institutions, trading firms, and exchanges that rely heavily on real-time data distribution solutions. Europe follows closely, with significant demand stemming from regulatory reforms and the expansion of electronic trading. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitalization of financial services, increasing investments in fintech, and the proliferation of stock exchanges in countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by o

  5. c

    Data from: Remote Sensing Coastal Change Simple Data Distribution Service

    • s.cnmilf.com
    • data.usgs.gov
    • +2more
    Updated Oct 8, 2025
    + more versions
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    U.S. Geological Survey (2025). Remote Sensing Coastal Change Simple Data Distribution Service [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/remote-sensing-coastal-change-simple-data-distribution-service
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    Dataset updated
    Oct 8, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    The Remote Sensing Coastal Change Simple Data Service provides timely and long-term access to emergency, provisional, and approved photogrammetric imagery, derivatives, and ancillary data through a web service via HyperText Transfer Protocol to a folder/file structure organized by data collection platform and survey (collection effort) with metadata sufficient to facilitate both human and machine access. Data are acquired, processed, and published using standardized workflows. Each data type added to the service has a peer-reviewed metadata and data review of sample data generated with standardized methods to ensure compliance with U.S. Geological Survey (USGS) Fundamental Science Practices (FSP).

  6. d

    Data from: Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) Data...

    • catalog.data.gov
    • data.usgs.gov
    Updated Oct 8, 2025
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    U.S. Geological Survey (2025). Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) Data Distribution [Dataset]. https://catalog.data.gov/dataset/pliocene-model-intercomparison-project-phase-3-pliomip3-data-distribution-571c9
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    Dataset updated
    Oct 8, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    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.

  7. d

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

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Nov 19, 2025
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    U.S. Geological Survey (2025). 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
    Nov 19, 2025
    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.

  8. d

    Data from: Distribution and status of five non-native fish species in the...

    • catalog.data.gov
    • search.dataone.org
    • +1more
    Updated Nov 20, 2025
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    U.S. Geological Survey (2025). Distribution and status of five non-native fish species in the Tampa Bay drainage (USA), a hot spot for fish introductions-Data [Dataset]. https://catalog.data.gov/dataset/distribution-and-status-of-five-non-native-fish-species-in-the-tampa-bay-drainage-usa-a-ho
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    Dataset updated
    Nov 20, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    United States
    Description

    This dataset provides supporting information for the species distribution data used in the associated manuscript. Collections of five non-native fish species were made by a number of institutions, and several capture techniques were used. This dataset also includes number of individuals of each species captured at each locality.

  9. e

    Diverse Distribution And Marketing Services Pty L Export Import Data |...

    • eximpedia.app
    Updated Feb 13, 2025
    + more versions
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    Seair Exim (2025). Diverse Distribution And Marketing Services Pty L Export Import 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, Mali, Armenia, Uzbekistan, Togo, United Arab Emirates, Kuwait, Finland, Croatia, 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

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

    • data.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).

  11. d

    Global Landslide Mortality Risks and Distribution

    • catalog.data.gov
    • dataverse.harvard.edu
    • +3more
    Updated Aug 22, 2025
    + more versions
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    SEDAC (2025). Global Landslide Mortality Risks and Distribution [Dataset]. https://catalog.data.gov/dataset/global-landslide-mortality-risks-and-distribution
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    Dataset updated
    Aug 22, 2025
    Dataset provided by
    SEDAC
    Description

    The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide 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 risks due to landslide hazard. Mortality loss estimates per hazard event are caculated 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 landslide hazard are obtained from the Global Landslide Hazard 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 increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-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).

  12. B

    Big Data Processing and Distribution Software Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 10, 2025
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    Data Insights Market (2025). Big Data Processing and Distribution Software Report [Dataset]. https://www.datainsightsmarket.com/reports/big-data-processing-and-distribution-software-1395953
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    pdf, ppt, docAvailable download formats
    Dataset updated
    May 10, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Big Data Processing and Distribution Software market is booming, projected to reach $150 billion by 2033 with a 15% CAGR. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector. Discover regional market shares and growth opportunities in cloud-based solutions and enterprise deployments.

  13. Data from: Summer Steelhead Distribution [ds341]

    • data.ca.gov
    • data.cnra.ca.gov
    • +5more
    Updated Oct 12, 2023
    + more versions
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    California Department of Fish and Wildlife (2023). Summer Steelhead Distribution [ds341] [Dataset]. https://data.ca.gov/dataset/summer-steelhead-distribution-ds3411
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    geojson, html, kml, csv, zip, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Oct 12, 2023
    Dataset authored and provided by
    California Department of Fish and Wildlifehttps://wildlife.ca.gov/
    License

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

    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.

  14. d

    log count and data distribution [optimism]

    • dune.com
    Updated Nov 6, 2025
    + more versions
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    0xrob (2025). log count and data distribution [optimism] [Dataset]. https://dune.com/discover/content/popular?q=author%3A0xrob&resource-type=queries
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    Dataset updated
    Nov 6, 2025
    Dataset authored and provided by
    0xrob
    License

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

    Description

    Blockchain data query: log count and data distribution [optimism]

  15. c

    Data from: Occurrence records and vegetation type data used for species...

    • s.cnmilf.com
    • data.usgs.gov
    • +1more
    Updated Oct 8, 2025
    + more versions
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    U.S. Geological Survey (2025). Occurrence records and vegetation type data used for species distribution models in the western United States [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/occurrence-records-and-vegetation-type-data-used-for-species-distribution-models-in-the-we
    Explore at:
    Dataset updated
    Oct 8, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Western United States, United States
    Description

    These data are species distribution information assembled for assessing the impacts of land-use barriers, facilitative interactions with other species, and loss of long-distance animal dispersal on predicted species range patterns for four common species in pinyon-juniper woodlands in the western United States. The layers in the data release are initial distribution records of two kinds: point occurrence records and a raster layer for the general vegetation types where the species is a co-dominant, compiled from other sources. Both types of data are the baseline information in species distribution models for the associated publication(see Larger Work Citation).

  16. Distribution of companies in Italy 2018, by data protection level

    • statista.com
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    Statista, Distribution of companies in Italy 2018, by data protection level [Dataset]. https://www.statista.com/statistics/1039018/data-protection-level-organizations-italy/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 2018 - Nov 2018
    Area covered
    Italy
    Description

    When facing data protection challenges, the majority of Italian companies were well-equipped in 2018. More than half of the interviewed companies had already adopted good data protection measures, while ** percent were leaders in this field. In this respect, Italy scored better than the global average: according to the source, only ** percent of companies worldwide could be considered leaders in this field.

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

    • gdex.ucar.edu
    • data.ucar.edu
    • +4more
    Updated Oct 31, 2003
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    Unidata/University Corporation for Atmospheric Research (2003). Historical Unidata Internet Data Distribution (IDD) Global Observational Data [Dataset]. http://doi.org/10.5065/9235-WJ24
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    Dataset updated
    Oct 31, 2003
    Dataset provided by
    National Science Foundationhttp://www.nsf.gov/
    Authors
    Unidata/University Corporation for Atmospheric Research
    License

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

    Time period covered
    Jan 1, 1970 - Dec 31, 2029
    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.

  18. U.S. healthcare data breach reporting entity distribution H1 2024, by type

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). U.S. healthcare data breach reporting entity distribution H1 2024, by type [Dataset]. https://www.statista.com/statistics/972231/health-data-breach-distribution-of-affected-entities-by-type/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In the first half of 2024, healthcare providers reported *** data breaches in the U.S. healthcare sector, becoming the entity with the highest number of reported breach incidents. As of the time of the reporting, business associates ranked second with the number of reported data breaches.

  19. d

    Data from: Bed material grain size distributions for surficial samples from...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Nov 26, 2025
    + more versions
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    U.S. Geological Survey (2025). Bed material grain size distributions for surficial samples from Iron Gate, Copco, and J.C. Boyle Reservoirs [Dataset]. https://catalog.data.gov/dataset/bed-material-grain-size-distributions-for-surficial-samples-from-iron-gate-copco-and-j-c-b
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    Dataset updated
    Nov 26, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    This product summarizes the collection and analysis of bed material sample grain size distribution collected from the Iron Gate, Copco, and J.C. Boyle Reservoirs located in Northern California and Southern Oregon on the Klamath River. Samples were collected on June 16, 2020 from cores (less than 1m depth) and processed for the full size distribution.

  20. f

    The data distribution and details of datasets used to train XGBoost models.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Oct 7, 2024
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    Sun, Yan; Liu, Qian; Hu, Pingzhao; Huang, Zi Huai; Chen, Lianghong; Domaratzki, Mike (2024). The data distribution and details of datasets used to train XGBoost models. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001285960
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    Dataset updated
    Oct 7, 2024
    Authors
    Sun, Yan; Liu, Qian; Hu, Pingzhao; Huang, Zi Huai; Chen, Lianghong; Domaratzki, Mike
    Description

    The data distribution and details of datasets used to train XGBoost models.

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Claudia Bergroth; Olle Järv; Henrikki Tenkanen; Matti Manninen; Tuuli Toivonen (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
Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki
Department of Built Environment, Aalto University / Centre for Advanced Spatial Analysis, University College London
Unit of Urban Research and Statistics, City of Helsinki / Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki
Elisa Corporation
Authors
Claudia Bergroth; Olle Järv; Henrikki Tenkanen; Matti Manninen; Tuuli Toivonen
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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