100+ datasets found
  1. d

    Photographs and locations of bottom still imagery collected in Little Egg...

    • catalog.data.gov
    • data.usgs.gov
    Updated Oct 2, 2025
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    U.S. Geological Survey (2025). Photographs and locations of bottom still imagery collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activity 2018-049-FA (JPEG images, point shapefile, and CSV file) [Dataset]. https://catalog.data.gov/dataset/photographs-and-locations-of-bottom-still-imagery-collected-in-little-egg-inlet-and-offsho
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    Dataset updated
    Oct 2, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Long Beach Island, Little Egg Inlet, New Jersey
    Description

    The natural resiliency of the New Jersey barrier island system, and the efficacy of management efforts to reduce vulnerability, depends on the ability of the system to recover and maintain equilibrium in response to storms and persistent coastal change. This resiliency is largely dependent on the availability of sand in the beach system. In an effort to better understand the system's sand budget and processes in which this system evolves, high-resolution geophysical mapping of the sea floor in Little Egg Inlet and along the southern end of Long Beach Island near Beach Haven, New Jersey was conducted from May 31 to June 10, 2018, followed by a sea floor sampling survey conducted from October 22 to 23, 2018, as part of a collaborative effort between the U.S. Geological Survey and Stockton University. Multibeam echo sounder bathymetry and backscatter data were collected along 741 kilometers of tracklines (approximately 200 square kilometers) of the coastal sea floor to regionally define its depth and morphology, as well as the type and distribution of sea-floor sediments. Six hundred ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits in the inlet and offshore. These new data will help inform future management decisions that affect the natural and recreational resources of the area around and offshore of Little Egg Inlet. These mapping surveys provide high-quality data needed to build scientific knowledge of the evolution and behavior of the New Jersey barrier island system.

  2. a

    Florida COVID19 12112020 ByCounty CSV

    • hub.arcgis.com
    • covid19-usflibrary.hub.arcgis.com
    Updated Dec 12, 2020
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    University of South Florida GIS (2020). Florida COVID19 12112020 ByCounty CSV [Dataset]. https://hub.arcgis.com/datasets/69016783b5204f6aa85dd679e65a363a
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    Dataset updated
    Dec 12, 2020
    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. 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.

  3. d

    Titanium-vanadium deposits hosted in mafic-ultramafic layered intrusions and...

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Oct 1, 2025
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    U.S. Geological Survey (2025). Titanium-vanadium deposits hosted in mafic-ultramafic layered intrusions and massif anorthosite intrusions from around the world [Dataset]. https://catalog.data.gov/dataset/titanium-vanadium-deposits-hosted-in-mafic-ultramafic-layered-intrusions-and-massif-anorth
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    Dataset updated
    Oct 1, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    This data release includes four .csv files and one Esri shapefile which contain data on titanium-vanadium deposits hosted in mafic-ultramafic layered intrusions and massif anorthosite intrusions from around the world. Some of the data was used to create a grade and tonnage model for titanium-vanadium deposits hosted in mafic-ultramafic layered intrusions. Only deposits with reported grade and tonnage information were included in this data compilation. The Titanium_vanadium_deposits.csv and Titanium_vanadium_deposits.shp files list the deposits and associated information such as the host intrusion, location, grade, and tonnage data, along with other miscellaneous descriptive data about the deposits. The Titanium_vanadium_column_headings.csv file correlates the column headings in the Titanium_vanadium_deposits.csv file with the attribute field names in the Titanium_vanadium_deposits.shp file and provides a brief description of each column heading and attribute field name. The Titanium_vanadium_deposits_concentrate_grade.csv file lists the concentrate grade data for the deposits, when available. The Titanium_vanadium_deposits_references.csv file lists the abbreviated and full references that are cited in the Titanium_vanadium_deposits.csv, and Titanium_vanadium_deposits.shp, and Titanium_vanadium_deposits_concentrate_grade.csv files.

  4. B

    Residential Schools Locations Dataset (Shapefile format)

    • borealisdata.ca
    • dataone.org
    Updated Jun 5, 2019
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    Rosa Orlandini (2019). Residential Schools Locations Dataset (Shapefile format) [Dataset]. http://doi.org/10.5683/SP2/FJG5TG
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 5, 2019
    Dataset provided by
    Borealis
    Authors
    Rosa Orlandini
    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, 1863 - Jun 30, 1998
    Area covered
    Canada
    Description

    The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

  5. a

    Florida COVID19 06032021 ByZip CSV

    • hub.arcgis.com
    • covid19-usflibrary.hub.arcgis.com
    Updated Jun 3, 2021
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    University of South Florida GIS (2021). Florida COVID19 06032021 ByZip CSV [Dataset]. https://hub.arcgis.com/datasets/53bd6521016746c7ab30e2a190b44c23
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    Dataset updated
    Jun 3, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Description

    Florida COVID-19 Cases by Zip Code 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. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/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. 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.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

  6. c

    Location and analysis of sediment samples collected by the U.S. Geological...

    • s.cnmilf.com
    • dataone.org
    • +3more
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Location and analysis of sediment samples collected by the U.S. Geological Survey in 2014 along the Delmarva Peninsula, MD and VA (Esri point shapefile and CSV file, Geographic, WGS 84) [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/location-and-analysis-of-sediment-samples-collected-by-the-u-s-geological-survey-in-2014-a
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Delmarva Peninsula
    Description

    The Delmarva Peninsula is a 220-kilometer-long headland, spit, and barrier island complex that was significantly affected by Hurricane Sandy. A U.S. Geological Survey cruise was conducted in the summer of 2014 to map the inner continental shelf of the Delmarva Peninsula using geophysical and sampling techniques to define the geologic framework that governs coastal system evolution at storm-event and longer timescales. Data collected during the 2014 cruise include swath bathymetry, sidescan sonar, chirp and boomer seismic-reflection profiles, acoustic Doppler current profiler, and sample and bottom photograph data. Processed data in raster and vector format are released here for the bottom photographs and sediment samples. More information about the USGS survey conducted as part of the Hurricane Sandy Response-- Geologic Framework and Coastal Vulnerability Study can be found at the project website or on the WHCMSC Field Activity Web pages: https://woodshole.er.usgs.gov/project-pages/delmarva/ and https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-002-FA

  7. g

    field_685e8fb67dd91 Shapefile

    • geopostcodes.com
    shp
    Updated May 24, 2025
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    GeoPostcodes (2025). field_685e8fb67dd91 Shapefile [Dataset]. https://www.geopostcodes.com/country/uk/shapefile/
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    shpAvailable download formats
    Dataset updated
    May 24, 2025
    Dataset authored and provided by
    GeoPostcodes
    Area covered
    field_685e8fb67dd91
    Description

    Download high-quality, up-to-date field_685e8fb67dd91 shapefile boundaries (SHP, projection system SRID 4326). Our field_685e8fb67dd91 Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

  8. GeoDAR: Georeferenced global Dams And Reservoirs dataset for bridging...

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Jan 19, 2024
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    Jida Wang; Jida Wang; Blake A. Walter; Fangfang Yao; Fangfang Yao; Chunqiao Song; Chunqiao Song; Meng Ding; Abu S. Maroof; Jingying Zhu; Chenyu Fan; Jordan M. McAlister; Md Safat Sikder; Md Safat Sikder; Yongwei Sheng; Yongwei Sheng; George H. Allen; George H. Allen; Jean-François Crétaux; Yoshihide Wada; Yoshihide Wada; Blake A. Walter; Meng Ding; Abu S. Maroof; Jingying Zhu; Chenyu Fan; Jordan M. McAlister; Jean-François Crétaux (2024). GeoDAR: Georeferenced global Dams And Reservoirs dataset for bridging attributes and geolocations [Dataset]. http://doi.org/10.5281/zenodo.6163413
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    zipAvailable download formats
    Dataset updated
    Jan 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Jida Wang; Jida Wang; Blake A. Walter; Fangfang Yao; Fangfang Yao; Chunqiao Song; Chunqiao Song; Meng Ding; Abu S. Maroof; Jingying Zhu; Chenyu Fan; Jordan M. McAlister; Md Safat Sikder; Md Safat Sikder; Yongwei Sheng; Yongwei Sheng; George H. Allen; George H. Allen; Jean-François Crétaux; Yoshihide Wada; Yoshihide Wada; Blake A. Walter; Meng Ding; Abu S. Maroof; Jingying Zhu; Chenyu Fan; Jordan M. McAlister; Jean-François Crétaux
    License

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

    Description

    Documented March 19, 2023

    !!NEW!!!

    GeoDAR reservoirs were registered to the drainage network! Please see the auxiliary data "GeoDAR-TopoCat" at https://zenodo.org/records/7750736. "GeoDAR-TopoCat" contains the drainage topology (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).

    Documented April 1, 2022

    Citation

    Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.

    Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.

    Contact

    Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu

    Data description and components

    Data folder “GeoDAR_v10_v11” (.zip) contains two consecutive, peer-reviewed versions (v1.0 and v1.1) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:

    • GeoDAR_v10_dams (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; https://www.icold-cigb.org; last access on March 13th, 2019).
    • GeoDAR_v11_dams (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).
    • GeoDAR_v11_reservoirs (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.

    As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:

    • GRanD_v13_issues.csv: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include:
      • “Issue”: main issue(s) of this record
      • “Description”: more detailed explanation of the issue
      • “Lat_corrected”: suggested correction for latitude (if any) in decimal degree
      • “Lon_corrected”: suggested correction for longitude (if any) in decimal degree
      • “Correction_source”: correction source(s)
      • “Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.
    • Wada_et_al_2017_harmonized.csv: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include:
      • Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization
      • Revised_lat: Revised latitude in decimal degree
      • Revised_lon: Revised longitude in decimal degree
      • Verification_notes: Description of the issues, verification sources, and other information used for harmonization.

    Attribute description

    Attribute

    Description and values

    v1.0 dams (file name: GeoDAR_v10_dams; format: comma-separated values (csv) and point shapefile)

    id_v10

    Dam ID for GeoDAR version 1.0 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.

    lat

    Latitude of the dam point in decimal degree (type: float) based on datum World Geodetic System (WGS) 1984.

    lon

    Longitude of the dam point in decimal degree (type: float) on WGS 1984.

    geo_mtd

    Georeferencing method (type: text). Unique values include “geo-matching CanVec”, “geo-matching LRD”, “geo-matching MARS”, “geo-matching NID”, “geo-matching ODC”, “geo-matching ODM”, “geo-matching RSB”, “geocoding (Google Maps)”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.

    qa_rank

    Quality assurance (QA) ranking (type: text). Unique values include “M1”, “M2”, “M3”, “C1”, “C2”, “C3”, “C4”, and “C5”. The QA ranking provides a general measure for our georeferencing quality. Refer to Supplementary Tables S1 and S3 in Wang et al. (2022) for more explanation.

    rv_mcm

    Reservoir storage capacity in million cubic meters (type: float). Values are only available for large dams in Wada et al. (2017). Capacity values of other WRD records are not released due to ICOLD’s proprietary restriction. Also see Table S4 in Wang et al. (2022).

    val_scn

    Validation result (type: text). Unique values include “correct”, “register”, “mismatch”, “misplacement”, and “Google Maps”. Refer to Table 4 in Wang et al. (2022) for explanation.

    val_src

    Primary validation source (type: text). Values include “CanVec”, “Google Maps”, “JDF”, “LRD”, “MARS”, “NID”, “NPCGIS”, “NRLD”, “ODC”, “ODM”, “RSB”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.

    qc

    Roles and name initials of co-authors/participants during data quality control (QC) and validation. Name initials are given to each assigned dam or region and are listed generally in chronological order for each role. Collation and harmonization of large dams in Wada et al. (2017) (see Table S4 in Wang et al. (2022)) were performed by JW, and this information is not repeated in the qc attribute for a reduced file size. Although we tried to track the name initials thoroughly, the lists may not be always exhaustive, and other undocumented adjustments and corrections were most likely performed by JW.

    v1.1 dams (file name: GeoDAR_v11_dams; format: comma-separated values (csv) and point shapefile)

    id_v11

    Dam ID for GeoDAR version 1.1 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.

    id_v10

    v1.0 ID of this dam/reservoir (as in id_v10) if it is also included in v1.0 (type: integer).

    id_grd_v13

    GRanD ID of this dam if also included in GRanD v1.3 (type: integer).

    lat

    Latitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.

    lon

    Longitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.

    geo_mtd

    Same as the value of geo_mtd in v1.0 if this dam is included in v1.0.

    qa_rank

    Same as the value of qa_rank in v1.0 if this dam is included in v1.0.

    val_scn

    Same as the value of val_scn in v1.0 if this dam is included in v1.0.

    val_src

    Same as the value of val_src in v1.0 if this dam is included in v1.0.

    rv_mcm_v10

    Same as the value of rv_mcm in v1.0 if this dam is included in v1.0.

    rv_mcm_v11

    Reservoir storage capacity in million cubic meters (type: float). Due to ICOLD’s proprietary restriction, provided values are limited to dams in Wada et al. (2017) and GRanD v1.3. If a dam is in both Wada et al. (2017) and GRanD v1.3, the value from the latter (if valid) takes precedence.

    har_src

    Source(s) to harmonize the dam points. Unique values include “GeoDAR v1.0 alone”, “GRanD v1.3 and GeoDAR 1.0”, “GRanD v1.3 and other ICOLD”, and “GRanD v1.3 alone”. Refer to Table 1 in Wang et al. (2022) for more details.

    pnt_src

    Source(s) of the dam point spatial coordinates. Unique values include “GeoDAR v1.0”, “original

  9. r

    Florida COVID19 10302020 ByCounty CSV Partial

    • opendata.rcmrd.org
    • hub.arcgis.com
    • +1more
    Updated Oct 31, 2020
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    University of South Florida GIS (2020). Florida COVID19 10302020 ByCounty CSV Partial [Dataset]. https://opendata.rcmrd.org/datasets/03b4947d595d471b836409d846463be6
    Explore at:
    Dataset updated
    Oct 31, 2020
    Dataset authored and provided by
    University of South Florida GIS
    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. 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.

  10. d

    Data from: Data and Results for GIS-Based Identification of Areas that have...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Oct 2, 2025
    + more versions
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    U.S. Geological Survey (2025). Data and Results for GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska [Dataset]. https://catalog.data.gov/dataset/data-and-results-for-gis-based-identification-of-areas-that-have-resource-potential-for-lo
    Explore at:
    Dataset updated
    Oct 2, 2025
    Dataset provided by
    U.S. Geological Survey
    Description

    This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

  11. SA Permanent Marks Dataset. - Dataset - SARIG catalogue

    • pid.sarig.sa.gov.au
    Updated Jun 16, 2025
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    pid.sarig.sa.gov.au (2025). SA Permanent Marks Dataset. - Dataset - SARIG catalogue [Dataset]. https://pid.sarig.sa.gov.au/dataset/mesac29673
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    Dataset updated
    Jun 16, 2025
    Dataset provided by
    Government of South Australiahttp://sa.gov.au/
    Description

    This download contains the complete Permanent Mark database for South Australia. PM information provided in both Shapefile and CSV format. Data provided is in GDA2020 Datum. This download contains the complete Permanent Mark database for South Australia. PM information provided in both Shapefile and CSV format. Data provided is in GDA2020 Datum.

  12. d

    Geospatial Data from the Alpine Treeline Warming Experiment (ATWE) on Niwot...

    • search.dataone.org
    • knb.ecoinformatics.org
    • +1more
    Updated Jul 7, 2021
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    Fabian Zuest; Cristina Castanha; Nicole Lau; Lara M. Kueppers (2021). Geospatial Data from the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA [Dataset]. http://doi.org/10.15485/1804896
    Explore at:
    Dataset updated
    Jul 7, 2021
    Dataset provided by
    ESS-DIVE
    Authors
    Fabian Zuest; Cristina Castanha; Nicole Lau; Lara M. Kueppers
    Time period covered
    Jan 1, 2008 - Jan 1, 2012
    Area covered
    Description

    This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

  13. g

    Congo Shapefile

    • geopostcodes.com
    shp
    Updated May 28, 2025
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    GeoPostcodes (2025). Congo Shapefile [Dataset]. https://www.geopostcodes.com/country/congo-shapefile
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    shpAvailable download formats
    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    GeoPostcodes
    Area covered
    Democratic Republic of the Congo
    Description

    Download high-quality, up-to-date Congo shapefile boundaries (SHP, projection system SRID 4326). Our Congo Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

  14. USGS USWTDB - U.S. Wind Turbine Database

    • zenodo.org
    json, zip
    Updated Jan 31, 2025
    + more versions
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    Catalyst Cooperative; Catalyst Cooperative (2025). USGS USWTDB - U.S. Wind Turbine Database [Dataset]. http://doi.org/10.5281/zenodo.14783215
    Explore at:
    zip, jsonAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Catalyst Cooperative; Catalyst Cooperative
    Description

    The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. Wind turbine records are collected and compiled from various public and private sources, digitized and position-verified from aerial imagery, and quality checked. The USWTDB is available for download in a variety of tabular and geospatial file formats, to meet a range of user/software needs. Dynamic web services are available for users that wish to access the USWTDB as a Representational State Transfer Services (RESTful) web service. Archived from https://energy.usgs.gov/uswtdb/

    This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

  15. Global Administrative Areas of Spain

    • kaggle.com
    zip
    Updated Nov 5, 2016
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    JSimonD (2016). Global Administrative Areas of Spain [Dataset]. https://www.kaggle.com/datasets/juliansimon/esp_adm_shp
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    zip(0 bytes)Available download formats
    Dataset updated
    Nov 5, 2016
    Authors
    JSimonD
    Area covered
    Spain
    Description

    Global Administrative Areas of Spain

    Url with data from any country in the world http://www.gadm.org/country Format file: zip Inside zip files: shp, shx, csv, cpg, dbf, prj

    Fields: OBJECTID ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 HASC_2 CCN_2 CCA_2 TYPE_2 ENGTYPE_2 NL_NAME_2 VARNAME_2

    URL zip http://biogeo.ucdavis.edu/data/gadm2.8/shp/ESP_adm_shp.zip

  16. U

    Satellite Data, Golden Eagles (Aquila chrysaetos), Western North America,...

    • data.usgs.gov
    • gimi9.com
    • +1more
    Updated Nov 3, 2020
    + more versions
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    Erica Craig; Mark Fuller; Thomas Zarriello; Tim Craig; Kyle Enns; Tara Bell; Kirk Bates; Cristiana Falvo; Linda Schueck; Anthony Everette (2020). Satellite Data, Golden Eagles (Aquila chrysaetos), Western North America, 1993-1997 [Dataset]. http://doi.org/10.5066/P9QP0TDH
    Explore at:
    Dataset updated
    Nov 3, 2020
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Erica Craig; Mark Fuller; Thomas Zarriello; Tim Craig; Kyle Enns; Tara Bell; Kirk Bates; Cristiana Falvo; Linda Schueck; Anthony Everette
    License

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

    Time period covered
    1993 - 1997
    Description

    The satellite data consist of 9,253 estimated locations of 21 golden eagles that were satellite-tagged in either east-central Idaho (Salmon, Idaho) or southwestern Idaho (Snake River National Conservation Area) and tracked between 1993 and 1997 via the Argos satellite system. The raw eagle tracking data provided by Argos were filtered one time using a version of the Douglas Argos-Filter Algorithm and converted into XLS spreadsheet form. This preservation project preserved the geospatial and satellite information from the XLS spreadsheet and released it in shapefile format (Satellite_Data.shp) and CSV format (Satellite_Data.csv). Each tagged bird in this dataset has a unique PTT number that is consistent across the three datasets in this release. Each of the 21 golden eagles (with 23 total PTT IDs, due to recaptures) have satellite location information (provided in the Satellite_Data shapefile and CSV) and 11 of these birds have behavioral observations taken from the ground (see CS ...

  17. d

    Location and grain-size analysis results of sediment samples collected in...

    • catalog.data.gov
    • res1catalogd-o-tdatad-o-tgov.vcapture.xyz
    Updated Sep 12, 2025
    + more versions
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    U.S. Geological Survey (2025). Location and grain-size analysis results of sediment samples collected in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey, University of Connecticut, and University of New Haven during field activities 2017-056-FA and 2018-018-FA (simplified point shapefile and CSV files) [Dataset]. https://catalog.data.gov/dataset/location-and-grain-size-analysis-results-of-sediment-samples-collected-in-long-island-soun
    Explore at:
    Dataset updated
    Sep 12, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Long Island Sound, Long Island, New York, New Haven, Connecticut, United States
    Description

    Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey (USGS), University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.

  18. r

    Florida COVID19 05262021 ByCounty CSV

    • opendata.rcmrd.org
    • covid19-usflibrary.hub.arcgis.com
    • +1more
    Updated May 26, 2021
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    University of South Florida GIS (2021). Florida COVID19 05262021 ByCounty CSV [Dataset]. https://opendata.rcmrd.org/datasets/10f330c721584bd6995abce090980075
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    Dataset updated
    May 26, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Florida
    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. 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.

  19. DRP Y1 Biosecurity Shapefile and Data

    • zenodo.org
    Updated Jan 12, 2023
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    Emma Verstraete; Emma Verstraete (2023). DRP Y1 Biosecurity Shapefile and Data [Dataset]. http://doi.org/10.5281/zenodo.7526294
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    Dataset updated
    Jan 12, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Emma Verstraete; Emma Verstraete
    Description

    Shapefile and associated csv of the Y1 deliverable mapping the locations of possible biosecurity hazards in Alaska

  20. a

    Florida COVID19 05232021 ByZip CSV

    • hub.arcgis.com
    Updated May 24, 2021
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    University of South Florida GIS (2021). Florida COVID19 05232021 ByZip CSV [Dataset]. https://hub.arcgis.com/datasets/882321d1eb4d47a382d22ac048218d09
    Explore at:
    Dataset updated
    May 24, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Description

    Florida COVID-19 Cases by Zip Code 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. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/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. 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.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

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U.S. Geological Survey (2025). Photographs and locations of bottom still imagery collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activity 2018-049-FA (JPEG images, point shapefile, and CSV file) [Dataset]. https://catalog.data.gov/dataset/photographs-and-locations-of-bottom-still-imagery-collected-in-little-egg-inlet-and-offsho

Photographs and locations of bottom still imagery collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activity 2018-049-FA (JPEG images, point shapefile, and CSV file)

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Dataset updated
Oct 2, 2025
Dataset provided by
United States Geological Surveyhttp://www.usgs.gov/
Area covered
Long Beach Island, Little Egg Inlet, New Jersey
Description

The natural resiliency of the New Jersey barrier island system, and the efficacy of management efforts to reduce vulnerability, depends on the ability of the system to recover and maintain equilibrium in response to storms and persistent coastal change. This resiliency is largely dependent on the availability of sand in the beach system. In an effort to better understand the system's sand budget and processes in which this system evolves, high-resolution geophysical mapping of the sea floor in Little Egg Inlet and along the southern end of Long Beach Island near Beach Haven, New Jersey was conducted from May 31 to June 10, 2018, followed by a sea floor sampling survey conducted from October 22 to 23, 2018, as part of a collaborative effort between the U.S. Geological Survey and Stockton University. Multibeam echo sounder bathymetry and backscatter data were collected along 741 kilometers of tracklines (approximately 200 square kilometers) of the coastal sea floor to regionally define its depth and morphology, as well as the type and distribution of sea-floor sediments. Six hundred ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits in the inlet and offshore. These new data will help inform future management decisions that affect the natural and recreational resources of the area around and offshore of Little Egg Inlet. These mapping surveys provide high-quality data needed to build scientific knowledge of the evolution and behavior of the New Jersey barrier island system.

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