• Updated Live | Dataset date: December 01, 2019-November 27, 2021
    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.
    6700+ Downloads
    This dataset updates: Live
  • Updated 7 February 2021 | Dataset date: April 22, 2016-May 15, 2016
    As a consequence of the armed conflict in the Gao, Kidal and Timbuktu regions of Mali, an estimated 32,000 Malian refugees have settled in Burkina Faso. Since 2012, UNHCR has been providing protection and assistance to these Malian refugees through multisectoral interventions. In order to assess the levels of vulnerability among these refugees and to identify potential opportunities for increasing their resilience, a quantitative survey was conducted among 6,775 Malian refugee households during April/May 2016.
    This dataset updates: Never
  • Updated 27 November 2021 | Dataset date: January 01, 1960-December 31, 2020
    Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.
    100+ Downloads
    This dataset updates: Every month
  • Updated 6 July 2020 | Dataset date: April 01, 2018-April 01, 2018
    Community facilities surveys for Gihembe, Kigeme and Nyabiheke refugee camps in Rwanda. The surveys contain information on community facility type and operations, energy for lighting and other uses, access to electricity technologies, respondent needs and priorities, and other energy-related issues.
    100+ Downloads
    This dataset updates: Never
  • Updated 6 July 2020 | Dataset date: April 01, 2018-April 01, 2018
    Enterprise surveys for Gihembe, Kigeme and Nyabiheke refugee camp in Rwanda. The datasets contain information on enterprise type and operations, energy for lighting and productive uses, access to electricity technologies, respondent needs and priorities, and other energy-related issues.
    100+ Downloads
    This dataset updates: Never
  • Updated 6 July 2020 | Dataset date: April 01, 2018-April 01, 2018
    Household surveys for Gihembe, Kigeme and Nyabiheke refugee camps in Rwanda. The surveys contain information on household demographics, energy use for lighting and cooking, access to electricity technologies, respondent needs and priorities, and other energy-related issues.
    200+ Downloads
    This dataset updates: Never
  • Updated 26 November 2021 | Dataset date: November 26, 2021-November 30, 2021
    Esta base de datos contiene los cálculos de People in Need (PiN) del clúster de salud (HEALTH) a partir de un análisis de la capacidad del sistema de salud, el estado de salud de la población y el contexto nacional. Estos cálculos fueron elaborados en el marco del Ciclo de Programación Humanitaria de Colombia para 2022.
    10+ Downloads
    This dataset updates: As needed
  • Updated 22 November 2021 | Dataset date: November 17, 2021-November 30, 2021
    Esta base de datos contiene los cálculos de People in Need (PiN) del clúster de Agua, Saneamiento e Higiene (WASH) realizados en el marco del Ciclo de Programación Humanitaria de Colombia para 2022.
    50+ Downloads
    This dataset updates: Never
  • Updated 26 November 2021 | Dataset date: July 30, 2021-November 30, 2021
    Données sur les contraintes d'accès humanitaire collectées auprès des partenaires.
    This data is by request only
  • Updated 26 November 2021 | Dataset date: September 30, 2019-September 30, 2019
    Ce jeu de données donne le total des PDIs, retournés et réfugiés, désagrégé par age et par sexe au niveau admin 2.
    500+ Downloads
    This dataset updates: Every three months
  • Updated 26 November 2021 | Dataset date: March 01, 2021-October 31, 2021
    Schools and learning facilities in Zimbabwe
    This dataset updates: Every year
  • Updated 25 November 2021 | Dataset date: May 20, 2019-May 20, 2019
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Democratic Republic of Congo: (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).
    1900+ Downloads
    This dataset updates: As needed
  • Updated 24 November 2021 | Dataset date: November 01, 2021-November 30, 2021
    Designated institutions to provide learning spaces and learning environments for the teaching of students. This data has been extracted from GRID3 Nigeria site
    10+ Downloads
    This dataset updates: Every year
  • Updated 25 November 2021 | Dataset date: November 25, 2021-November 30, 2021
    Esta base de datos contiene los cálculos de People in Need (PiN) del Clúster de seguridad alimentaria y nutrición (SAN-SP) (FOOD-EN) realizados en el marco del Ciclo de Programación Humanitaria de Colombia para 2022.
    30+ Downloads
    This dataset updates: As needed
  • Updated 25 November 2021 | Dataset date: November 25, 2021-November 30, 2021
    Base de datos con información de las organizaciones y agencias que hacen parte del área de responsabilidad de Violencia Basada en Género.
    10+ Downloads
    This dataset updates: As needed
  • Updated 25 November 2021 | Dataset date: August 31, 2021-August 31, 2021
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed).
    5000+ Downloads
    This dataset updates: Every six months
  • Updated 28 September 2021 | Dataset date: September 24, 2021-September 24, 2021
    Hospitals and Clinics with registration status and Location in Nigeria. This dataset has been publicly provided by the Nigeria Federal Ministry of Health on the NIGERIA Health Facility Registry (HFR) website
    80+ Downloads
    This dataset updates: As needed
  • Updated 30 June 2021 | Dataset date: February 15, 2021-February 15, 2021
    These figures were endorsed as the baseline figure of IDPs residing in government-controlled areas (GCAs) in 2020 by OCHA and other humanitarian partners in the Humanitarian Needs Overview for Ukraine for 2021.
    100+ Downloads
    This dataset updates: Every year
  • This data release includes gridded population estimates (~100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to individual age-sex groups. These population estimates represent the time period 2016 to 2017 corresponding to when the household surveys were conducted. Populations were mapped into areas where residential settlements were detected based on satellite imagery mostly from 2014. The data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom's Department for International Development (OPP1182408). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation. Statistical modellingwas led by Doug Leasure and Chris Jochem with oversight from Andy Tatem. In-country implementation was led by Tracy Adole. Oak Ridge National Laboratories (ORNL), eHealth Africa, and the Bill and Melinda Gates Foundation collected microcensus data and produced the settlement map used as inputs for this work. The whole WorldPop group and GRID3 partners are acknowledged for overall project support. RELEASE CONTENT NGA_population_v1_2_gridded.zip NGA_population_v1_2_admin.zip NGA_population_v1_2_sql.sql NGA_population_v1_2_mastergrid.tif NGA_population_v1_2_tiles.zip NGA_population_v1_2_agesex.zip NGA_population_v1_2_methods.zip LICENSE These data (1-6) may be redistributed using a Creative Commons Attribution Share-Alike 4.0 License. The methods documentation (7) may be redistributed using a Creative Commons Attribution 4.0 License. Recommended citations WorldPop. 2019. Bottom-up gridded population estimates for Nigeria, version 1.2. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00655 WorldPop. 2020. Bottom-up gridded population estimates for individual age-sex groups in Nigeria, version 1.2.1. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00661 Leasure DR, Jochem WC, Weber EM, Seaman V, Tatem AJ. 2020. National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1913050117 For further details, please, read NGA_population_v1_2_README.pdf
    60+ Downloads
    This dataset updates: As needed
  • Updated 16 September 2021 | Dataset date: May 31, 2021-May 31, 2021
    Refugees and Asylum seekers in Nigeria
    40+ Downloads
    This dataset updates: Every year
  • Updated 25 November 2021 | Dataset date: January 01, 2019-November 16, 2021
    These datasets contain information on violent and threatening incidents affecting aid operations, education, healthcare, refugees and IDPs in Sudan to ensure staff safety and better response outcomes.
    300+ Downloads
    This dataset updates: As needed
  • Updated 25 November 2021 | Dataset date: November 30, 2020-November 24, 2021
    Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease. In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women. In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far. For more, please visit: http://globalhealth5050.org/covid19
    1300+ Downloads
    This dataset updates: Every month
  • Updated 25 November 2021 | Dataset date: December 09, 2016-October 21, 2021
    This dataset contains the locations of the Helicopter landing sites currently served by UNHAS in Haiti.
    100+ Downloads
    This dataset updates: Every year
  • Updated 25 November 2021 | Dataset date: August 10, 2021-September 30, 2021
    This Data is about IDP, returnees from CAR (previous IDP) and returnees from other countries repartition by origin and period of displacement and between 2013 and the date of assessment. Evaluation has been run in 6 prefectures (admin1), 16 sub-prefectures (admin2) and 367 localities.
    3800+ Downloads
    This dataset updates: Every six months
  • Updated 24 November 2021 | Dataset date: January 01, 2017-November 16, 2021
    War-Damaged Shelter Rehabilitation data as submitted by humanitarian and development actors undertaking assessment and rehabilitation of war-damaged shelter for returnees across the country.
    This dataset updates: Every six months