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  • 5700+ Downloads
    Updated Live | Dataset date: December 01, 2019-July 28, 2021
    This dataset updates: Live
    Data Overview This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019") You may not use this data for commercial purposes. If there is a need for commercial use of the data, please contact Metabiota at info@metabiota.com to obtain a commercial use license. The incidence data are in a CSV file format. One row in an incidence file contains a piece of epidemiological data extracted from the specified source. The file contains data from multiple sources at multiple spatial resolutions in cumulative and non-cumulative formats by confirmation status. To select a single time series of case or death data, filter the incidence dataset by source, spatial resolution, location, confirmation status, and cumulative flag. Data are collected, structured, and validated by Metabiota’s digital surveillance experts. The data structuring process is designed to produce the most reliable estimates of reported cases and deaths over space and time. The data are cleaned and provided in a uniform format such that information can be compared across multiple sources. Data are collected at the time of publication in the highest geographic and temporal resolutions available in the original report. This repository is intended to provide a single access point for data from a wide range of data sources. Data will be updated periodically with the latest epidemiological data. Metabiota maintains a database of epidemiological information for over two thousand high-priority infectious disease events. Please contact us (info@metabiota.com) if you are interested in licensing the complete dataset. Cumulative vs. Non-Cumulative Incidence Reporting sources provide either cumulative incidence, non-cumulative incidence, or both. If the source only provides a non-cumulative incidence value, the cumulative values are inferred using prior reports from the same source. Use the CUMULATIVE FLAG variable to subset the data to cumulative (TRUE) or non-cumulative (FALSE) values. Case Confirmation Status The incidence datasets include the confirmation status of cases and deaths when this information is provided by the reporting source. Subset the data by the CONFIRMATION_STATUS variable to either TOTAL, CONFIRMED, SUSPECTED, or PROBABLE to obtain the data of your choice. Total incidence values include confirmed, suspected, and probable incidence values. If a source only provides suspected, probable, or confirmed incidence, the total incidence is inferred to be the sum of the provided values. If the report does not specify confirmation status, the value is included in the "total" confirmation status value. The data provided under the "Metabiota Composite Source" often does not include suspected incidence due to inconsistencies in reporting cases and deaths with this confirmation status. Outcome - Cases vs. Deaths The incidence datasets include cases and deaths. Subset the data to either CASE or DEATH using the OUTCOME variable. It should be noted that deaths are included in case counts. Spatial Resolution Data are provided at multiple spatial resolutions. Data should be subset to a single spatial resolution of interest using the SPATIAL_RESOLUTION variable. Information is included at the finest spatial resolution provided to the original epidemic report. We also aggregate incidence to coarser geographic resolutions. For example, if a source only provides data at the province-level, then province-level data are included in the dataset as well as country-level totals. Users should avoid summing all cases or deaths in a given country for a given date without specifying the SPATIAL_RESOLUTION value. For example, subset the data to SPATIAL_RESOLUTION equal to “AL0” in order to view only the aggregated country level data. There are differences in administrative division naming practices by country. Administrative levels in this dataset are defined using the Google Geolocation API (https://developers.google.com/maps/documentation/geolocation/). For example, the data for the 2019-nCoV from one source provides information for the city of Beijing, which Google Geolocations indicates is a “locality.” Beijing is also the name of the municipality where the city Beijing is located. Thus, the 2019-nCoV dataset includes rows of data for both the city Beijing, as well as the municipality of the same name. If additional cities in the Beijing municipality reported data, those data would be aggregated with the city Beijing data to form the municipality Beijing data. Sources Data sources in this repository were selected to provide comprehensive spatiotemporal data for each outbreak. Data from a specific source can be selected using the SOURCE variable. In addition to the original reporting sources, Metabiota compiles multiple sources to generate the most comprehensive view of an outbreak. This compilation is stored in the database under the source name “Metabiota Composite Source.” The purpose of generating this new view of the outbreak is to provide the most accurate and precise spatiotemporal data for the outbreak. At this time, Metabiota does not incorporate unofficial - including media - sources into the “Metabiota Composite Source” dataset. Quality Assurance Data are collected by a team of digital surveillance experts and undergo many quality assurance tests. After data are collected, they are independently verified by at least one additional analyst. The data also pass an automated validation program to ensure data consistency and integrity. NonCommercial Use License Creative Commons License Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) This is a human-readable summary of the Legal Code. You are free: to Share — to copy, distribute and transmit the work to Remix — to adapt the work Under the following conditions: Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial — You may not use this work for commercial purposes. Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. With the understanding that: Waiver — Any of the above conditions can be waived if you get permission from the copyright holder. Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license. Other Rights — In no way are any of the following rights affected by the license: Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; The author's moral rights; Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. Notice — For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to this web page. For details and the full license text, see http://creativecommons.org/licenses/by-nc-sa/3.0/ Liability Metabiota shall in no event be liable for any decision taken by the user based on the data made available. Under no circumstances, shall Metabiota be liable for any damages (whatsoever) arising out of the use or inability to use the database. The entire risk arising out of the use of the database remains with the user.
  • 7500+ Downloads
    Updated 29 July 2021 | Dataset date: March 10, 2020-May 27, 2021
    This dataset updates: Every day
    This data has been collected from various sources and is displayed in this online dashboard: https://geonode.wfp.org/travel Mobile version: https://geonode.wfp.org/travel_mobile The data is divided in two datasets: COVID-19 restrictions by country: This dataset shows current travel restrictions. Information is collected from various sources: IATA, media, national sources, WFP internal or any other. COVID-19 airline restrictions information: This dataset shows restrictions taken by individual airlines or country. Information is collected again from various sources including WFP internal and public sources. The data displayed is a collaborative effort and anybody with more accurate/updated information is highly encouraged to contact WFP GIS unit for Emergencies at the following email address: hq.gis@wfp.org
  • 900+ Downloads
    Updated 27 July 2021 | Dataset date: July 27, 2021-July 29, 2021
    This dataset updates: As needed
    Equatorial Guinea administrative level 0-2 Subnational Administrative Boundaries
  • 1900+ Downloads
    Updated 27 July 2021 | Dataset date: October 15, 2020-July 29, 2021
    This dataset updates: Every week
    This bucket contains FAIR COVID-19 US county level forecast data
  • 30+ Downloads
    Updated 22 July 2021 | Dataset date: December 15, 2020-July 29, 2021
    This dataset updates: As needed
    Kenya 2019 Cencus ICT Usage data mapped by Subcounty Note KNBS incorrectly used 345 sub counties. This is mapped by best effort to 290 correct sub counties using IEBC boundaries. Acknowledgements to Shel Kariuki who imported KNBS PDFS to CSV and Brian Mwangi who helped with mapping KNBS regions to valid sub county names.
  • 4600+ Downloads
    Updated 22 July 2021 | Dataset date: March 02, 2016-March 02, 2016
    This dataset updates: As needed
    Populated places dataset for Ethiopia endorsed by the Inter-Cluster Information Management Working group (ICMWG) after cleaning and processing done by ITOS. Source: Multiple sources
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: railway IN ('rail','station') Features may have these attributes: railway addr:full source layer operator:type addr:city ele name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office') Features may have these attributes: operator amenity addr:full source addr:city network name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: place IN ('isolated_dwelling','town','village','hamlet','city') Features may have these attributes: population source place is_in name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: amenity building addr:full source capacity:persons operator:type addr:city healthcare:speciality name healthcare This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten','school','college','university') OR building IN ('kindergarten','school','college','university') Features may have these attributes: amenity building addr:full source capacity:persons operator:type addr:city name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity = 'ferry_terminal' OR building = 'ferry_terminal' OR port IS NOT NULL Features may have these attributes: amenity building addr:full source operator:type addr:city port name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site' Features may have these attributes: building addr:full emergency:helipad source capacity:persons operator:type addr:city emergency aeroway name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: addr:street amenity man_made addr:full source beds tourism rooms shop addr:city opening_hours addr:housenumber name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: natural tunnel source width blockage waterway depth covered layer water name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: smoothness source width lanes highway layer oneway surface bridge name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 21 July 2021 | Dataset date: July 21, 2021-July 21, 2021
    This dataset updates: Every day
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: addr:street building building:levels addr:full source office addr:city building:materials addr:housenumber name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 100+ Downloads
    Updated 19 July 2021 | Dataset date: January 01, 2020-July 29, 2021
    This dataset updates: Every week
    English Description - The following contains weekly operational data of hospitals in Venezuela, inventory of health supplies, administrative entity, geographic location and type of hospital. The collection of this data is a crowdsource effort under a survey template that provides an image of the current situation of hospitals in Venezuela Descripción en Español - Esta data contiene data semanal de las operaciones en los hospitales de Venezuela, inventario de suministro de salud, gerencia del hospital, zona geográfica y el tipo de hospital. El método de recolección de esta data es a través de múltiples fuentes voluntarias en el terreno en una encuesta estandarizada. La encuesta tiene como objetivo mostrar la situación actual de hospitales en Venezuela.
  • 90+ Downloads
    Updated 19 July 2021 | Dataset date: December 15, 2020-December 15, 2020
    This dataset updates: Every six months
    ACAPS' Humanitarian Access Dataset puts together all the pieces of information on Humanitarian Access Constraints of the first half of 2021. The report resulting from this dataset is the Humanitarian Access Overview, which outlines how access to humanitarian assistance continues to be restricted for crisis-affected populations in more than 60 countries. Data from the dataset is coded according to access dimensions, indicators and sub-indicators, then scored on a 0 to 5 severity scale. The dataset displays: data collected, coded in "log" tab with its "data dictionary" aggregation of data by country, scored per "indicator" country scores (pillars, overall access score and information gaps) in "Scores" tab gis compatible clean tab with ISO codes According to this data, we identified the following: Extreme access constraints (Level 5) for: Eritrea, Libya, Syria, Yemen. Very high access constraints (Level 4) for: Afghanistan, Bangladesh, Cameroon, Democratic Republic of Congo (DRC), Ethiopia, Iraq, Mali, Myanmar, Nigeria, Palestine, Somalia, South Sudan, Venezuela. High access constraints (Level 3) for: Azerbaijan, Burkina Faso, Central African Republic (CAR), Chad, Colombia, Democratic People’s Republic of Korea (DPRK), Honduras, India, Iran, Lebanon, Mozambique, Nicaragua, Niger, Pakistan, Sudan, Turkey, Ukraine. For more information, download the dataset. To access ACAPS' Humanitarian Access Overview, please visit: http://humanitarianaccess.acaps.org/
  • Updated 15 July 2021 | Dataset date: June 08, 2021-June 08, 2021
    This dataset updates: As needed
    This data contains information on transportation status or constraints in Somalia.
  • 500+ Downloads
    Updated 15 July 2021 | Dataset date: April 28, 2021-July 29, 2021
    This dataset updates: Every year
    This is a DRAFT version of the March 2020 structure footprint, the January 2019 footprint and previous (archived) footprint. The 2020 footprint does not include structures for camps 14, 15 ,16 or 27. These will be added when the final footprintis uploaded in early May. These structures can be found in the 2019 footprint. Digitised areas show covered spaces within the camps. These may be latrines, tubewells, bathing stations, shelters or larger structures (many of which are multiple shelter joined under one continuous roof). This is also the case in previous 'shelter' footprints. Bridges are not included in this dataset, but many can be found in the previous 2019 footprint. Files represent digitization work carried out by UNOSAT and REACH using IOM-NPM drone imagery from. Work is currently ongoing and the file is reflective of efforts up to 28 April 2021. As future iterations and improvements to the footprints are made, additional versions will be released. Limitations: The work has not been ground truthed and is based on expert interpretation of UAV imagery. In addition to relying on imagery interpretation, the footprints are bound by the limitations present in the UAV images that were utilized. Credits: UNOSAT, REACH, 2021
  • 90+ Downloads
    Updated 14 July 2021 | Dataset date: July 13, 2021-July 29, 2021
    This dataset updates: As needed
    VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Finland: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 70+ Downloads
    Updated 13 July 2021 | Dataset date: July 13, 2021-July 13, 2021
    This dataset updates: As needed
    VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sweden: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
  • 1700+ Downloads
    Updated 7 July 2021 | Dataset date: January 01, 2018-January 01, 2019
    This dataset updates: Never
    This dataset contains shapefiles for Guinea, Liberia, and Sierra Leone from the OpenStreetMap (OSM) project. Each country has its individual file. The dataset counts with contributions of hundreds of users. This dataset is updated daily. The original dataset can be downloaded from the OSM West Africa Ebola response wiki.
  • 7600+ Downloads
    Updated 6 July 2021 | Dataset date: July 01, 2021-July 29, 2021
    This dataset updates: As needed
    West and Central Africa Administrative boundaries, administrative level 0 to 2. Notice: The boundaries and names shown and the designations used on these shapefiles do not imply official endorsement or acceptance by the United Nations. West and Central Africa settlements with administrative capitals