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  • 3500+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Dec 3, 2020
    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.
  • 100+ Downloads
    Updated December 3, 2020 | Dataset date: Dec 2, 2020
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by Governorate due to the Coronavirus pandemic in Palestine.
  • 6000+ Downloads
    Updated December 3, 2020 | Dataset date: Dec 1, 2020
    This dataset updates: Every day
    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak. We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak. The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository. United States Data Data on cumulative coronavirus cases and deaths can be found in two files for states and counties. Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data. State-Level Data State-level data can be found in the us-states.csv file. date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... County-Level Data County-level data can be found in the us-counties.csv file. date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these. Github Repository This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data
  • 100+ Downloads
    Updated December 3, 2020 | Dataset date: Nov 28, 2020
    This dataset updates: Every day
    This dataset contains the number of suspected cases, confirmed cases, and deaths by Département due to the Coronavirus pandemic in Haiti. Released by the Ministry of Public Health and Population of Haiti.
  • 200+ Downloads
    Updated December 3, 2020 | Dataset date: Jan 1, 1990-Dec 2, 2020
    This dataset updates: Every day
    This dataset contains Food Prices data for all countries where the data is available. Food prices data comes from the World Food Programme (WFP) and covers foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 400+ Downloads
    Updated December 3, 2020 | Dataset date: Dec 3, 2020
    This dataset updates: Every day
    Covid-19 Impact on Humanitarian Operations Data Viz inputs
  • 5400+ Downloads
    Updated December 3, 2020 | Dataset date: Mar 10, 2020-Dec 2, 2020
    This dataset updates: Every day
    This data has been collected from various sources and is displayed in this online dashboard: http://arcg.is/uHyuO Mobile version: http://arcg.is/0q8Xfj 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
  • 300+ Downloads
    Updated December 3, 2020 | Dataset date: May 31, 2020
    This dataset updates: Every two weeks
    Aggregated figures for Natural Disasters in EM-DAT More on the EM-DAT database : ( website / data portal ). Each line corresponds to a given combination of year, country, disaster subtype and reports figures for : number of disasters total number of people affected total number of deaths economic losses (original value and adjusted)
  • 6800+ Downloads
    Updated December 3, 2020 | Dataset date: Dec 3, 2020
    This dataset updates: Every day
    FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
  • 1900+ Downloads
    Updated December 3, 2020 | Dataset date: Jan 1, 1970-Dec 31, 2019
    This dataset updates: Every three months
    Education indicators for Zimbabwe. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: Students and Teachers (made 2020 September), SDG 4 Global and Thematic (made 2020 September), Demographic and Socio-economic (made 2020 September)
  • 40+ Downloads
    Updated December 3, 2020 | Dataset date: Apr 29, 2016
    This dataset updates: As needed
    No abstract provided
  • 100+ Downloads
    Updated December 3, 2020 | Dataset date: May 29, 2019
    This dataset updates: Every day
    Reference historic FX rates quoted by the European Central Bank (ECB) converted to USD base currency. There are two resources - one with USD as the quote currency (more standard x/USD) and another with USD as the base currency (USD/x). Note that where the rate is 0 or NaN, it means that the currency existed in the past but no longer exists.
  • 10+ Downloads
    Updated December 3, 2020 | Dataset date: Oct 5, 2017
    This dataset updates: Every week
    This dataset contains distribution tracking data for water, food, NFIs and shelter items in Dominica in the aftermath of Hurricane Irma. The coordination team in Dominica has been working with partners to make the data as accurate as possible. Please share your distribution to any locations directly to the available dataset created by the team in Dominica or send your information or queries to hurricanemaria2017@undac.org
  • 60+ Downloads
    Updated December 3, 2020 | Dataset date: Dec 2, 2020
    This dataset updates: Every week
    Topline figures dataset for the Central Emergency Response Fund's organisation page
  • 300+ Downloads
    Updated December 2, 2020 | Dataset date: Mar 13, 2020-Dec 1, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Mauritania - Infected (new cases), Deceased, Recovered. Please note that the gender data is not available yet, our teams are working on it. Thank you for your understanding.
  • 600+ Downloads
    Updated December 2, 2020 | Dataset date: Mar 25, 2020-Dec 2, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Mali - Infected (new cases, gender if available), Deceased, Recovered.
  • This geodatabase provides a set of settlement points and their names to spatially locate and identify settlement features in the Democratic Republic of the Congo. This work has been undertaken as part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) initiative in Democratic Republic of the Congo. GRID3 works with countries to generate, validate and use geospatial data on population, settlements, infrastructure, and subnational boundaries. For more information, see https://grid3.org/ Suggested Data Set Citation: Bureau Central du Recensement (BCR), Democratic Republic of the Congo, and Center for International Earth Science Information Network (CIESIN), Columbia University. 2020. GRID3 Democratic Republic of the Congo Settlement Point, Version 01 Beta. Palisades NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). https://data.humdata.org/dataset/grid3-democratic-republic-of-the-congo-settlements-points-version-01-beta. Accessed DAY MONTH YEAR
  • 500+ Downloads
    Updated December 2, 2020 | Dataset date: Oct 15, 2020
    This dataset updates: Every week
    This bucket contains FAIR COVID-19 US county level forecast data
  • Updated December 2, 2020 | Dataset date: Dec 2, 2020
    This dataset updates: As needed
    Base que contiene información de un muestreo aleatorio de empresas para las ciudades de Arauca, Barranquilla, Cali, Cúcuta, Medellín y de las regiones de La Guajira y el Oriente Antioqueño. Las variables dispuestas son razón social o nombre del establecimiento, municipio, dirección comercial, actividad comercial e identificador CIIU.
  • 500+ Downloads
    Updated December 2, 2020 | Dataset date: Jan 1, 2019-Nov 29, 2020
    This dataset updates: Every week
    Données hebdomadaires sur les maladies à potentiel épidémiques au Burkina Faso
  • Updated December 2, 2020 | Dataset date: Jan 1, 2020-Apr 30, 2020
    This dataset updates: Every three months
    This dataset contains Who, What, Where and When (4W) data for the province of Kirundo in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). Additionally the dataset includes until when the organisations will have funding to operate their activities. This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.
  • 600+ Downloads
    Updated December 2, 2020 | Dataset date: Jan 1, 2020-Nov 30, 2020
    This dataset updates: Every month
    Number of Refugees returning to Afghanistan for the period of 01 January 2020 to 30 November 2020 by district of destination and origin.
  • 600+ Downloads
    Updated December 2, 2020 | Dataset date: Mar 19, 2020-Dec 1, 2020
    This dataset updates: Every week
    Subnational data about Covid19 in Niger - Infected (new cases, gender), Deceased, Recovered.
  • 200+ Downloads
    Updated December 2, 2020 | Dataset date: Sep 9, 2020
    This dataset updates: As needed
    The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few. A set of around 80 impact indicators anticipated to be impacted by COVID-19 have been identified and organised across 4 pillars and 13 thematic blocks. Additionally, a set of around 25 pre-COVID-19 baseline indicators have been selected for each pillar. The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic is having in more than 190 countries. Data comes from a range of available sources, including international organisations, research centres, and media analysis. Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase, we will keep improving and updating in the coming weeks.
  • Updated December 2, 2020 | Dataset date: Jun 5, 2020
    This dataset updates: As needed
    Click the link to experience the power of tracking change over time: https://drive.google.com/file/d/1SlSCDHhFAS7wRgkgPJxkMl30ze7WcnPs/view?usp=sharing Tracking change over time, Tanzania conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally. Full GeoTIF file available for download on request. Contact globhe@globhe.com for download link. To request drone data on demand at global scale make your request at: https://globhe.com/drone-data-request MORE CROWDDRONING BY GLOBHE Webb: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/