Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
More
  • 60+ Downloads
    Updated March 28, 2020 | Dataset date: Dec 1, 2019-Mar 28, 2020
    This dataset updates: Every day
    Data Overview This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019") 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. 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.
  • 40+ Downloads
    Updated March 28, 2020 | Dataset date: Jan 1, 1992-Dec 31, 1995
    This dataset updates: As needed
    This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days. Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532 Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University
  • 40+ Downloads
    Updated March 28, 2020 | Dataset date: Jan 1, 2008-Dec 31, 2017
    This dataset updates: As needed
    Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "People Displaced" refers to the number of people living in displacement as of the end of each year. "New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.
  • 500+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (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).
  • 300+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Togo: (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).
  • 300+ Downloads
    Updated March 28, 2020 | Dataset date: Oct 1, 2018
    This dataset updates: As needed
    The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information. There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
  • 300+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sao Tome and Principe: (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).
  • 300+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sierra Leone: (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). There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
  • 5800+ Downloads
    Updated March 28, 2020 | Dataset date: Mar 28, 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.
  • 200+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mayotte: (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).
  • 400+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Namibia: (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).
  • 800+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mozambique: (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).
  • 300+ Downloads
    Updated March 28, 2020 | Dataset date: May 20, 2019
    This dataset updates: As needed
    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Lesotho: (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).
  • Updated March 27, 2020 | Dataset date: Jan 1, 2010-Dec 31, 2019
    This dataset updates: Every year
    Indice de Riesgo por Calidad del Agua para Consumo Humano (IRCA) Municipal 2010-2019 en Colombia
  • 500+ Downloads
    Updated March 27, 2020 | Dataset date: Mar 10, 2020-Apr 30, 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
  • 1500+ Downloads
    Updated March 27, 2020 | Dataset date: Jun 1, 2017-Feb 1, 2019
    This dataset updates: Every month
    This dashboard highlights the living situation in Syria by showing the prices of basic market items. How to use this product: The first three pages track price change chronologically on governorate level, with ability to compare between them by choosing one or more. The subsequent pages show the prices of market items on the governorate and sub-district level with an item availability heat map of any selected item on any selected level and period. You can select one of the listed items in one sub-district or more. When you choose a governorate its subdistrict(s) will be highlighted according to the availability of the selected item in the selected governorate(s).
  • 100+ Downloads
    Updated March 27, 2020 | Dataset date: Dec 31, 2019-Mar 23, 2020
    This dataset updates: Every day
    Data collected by the European Centre for Disease Prevention and Control. The downloadable data file is updated daily and contains the latest available public data on COVID-19. Public-use data files allows users to manipulate the data in a format appropriate for their analyses. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes. For further information, visit https://www.ecdc.europa.eu/en/novel-coronavirus-china.
  • 69000+ Downloads
    Updated Live | Dataset date: Jan 22, 2020-Mar 27, 2020
    This dataset updates: Live
    Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn. Pneumonia. 2020, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH). JSU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on github. Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths. On 23/03/2020, a new data structure was released. The current resources are: time_series_covid19_confirmed_global.csv time_series_covid19_deaths_global.csv for the latest time series data ---DEPRECATION WARNING--- The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020: time_series_19-covid-Confirmed.csv time_series_19-covid-Deaths.csv time_series_19-covid-Recovered.csv
  • 50+ Downloads
    Updated March 27, 2020 | Dataset date: Feb 23, 2020-Mar 13, 2020
    This dataset updates: Every three months
    The dataset contains estimates of changes in human mobility during the COVID-19 outbreak. Estimates are reported for three weeks since the start of the outbreak: February 22-28, February 29 - March 6, March 7-13. These data underly the reports published at https://covid19mm.github.io/ If you use these data for your research please cite the following work: COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown Emanuele Pepe, Paolo Bajardi, Laetitia Gauvin, Filippo Privitera, Brennan Lake, Ciro Cattuto, Michele Tizzoni medRxiv 2020.03.22.20039933; doi: https://doi.org/10.1101/2020.03.22.20039933
  • 100+ Downloads
    Updated March 27, 2020 | Dataset date: Feb 29, 2020
    This dataset updates: Every month
    Situation des personnes déplacées internes.
  • 800+ Downloads
    Updated March 27, 2020 | Dataset date: May 16, 2018
    This dataset updates: Every year
    Mongolia administrative level 0 (country), 1 (aimag), 2 (soum), and regional sex and age disaggregated population statistics and gazetteer See caveats These tables are suitable for database of GIS linkage to the Mongolia - Administrative Boundaries (Polygon & Polyline) shapefiles and gazetteer.
  • Updated March 27, 2020 | Dataset date: Jan 1, 2012-Dec 31, 2012
    This dataset updates: As needed
    Contains data from World Health Organization's data portal covering the following categories: Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, World Health Statistics, Health financing, Public health and environment, Substance use and mental health, Tobacco, Injuries and violence, HIV/AIDS and other STIs, Nutrition, Urban health, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Health Equity Monitor, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, Insecticide resistance, Oral health, Universal Health Coverage, Global Observatory for eHealth (GOe), RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, AMR GLASS Coordination, AMR GLASS Surveillance, AMR GLASS Quality assurance, Noncommunicable diseases and mental health, Health workforce, Neglected Tropical Diseases, AMR GASP, ICD For links to individual indicator metadata, see resource descriptions.
  • 2800+ Downloads
    Updated March 26, 2020 | Dataset date: Mar 25, 2020
    This dataset updates: Every day
    This dataset contains key figures (topline numbers) on the world's most pressing humanitarian crises. The data, curated by ReliefWeb's editorial team based on its relevance to the humanitarian community, is updated regularly. The description of the files and columns can be found in the additional metadata spreadsheet file.
  • Updated March 26, 2020 | Dataset date: Mar 26, 2020
    This data is by request only
    This data includes responses to Ground Truth Solutions' perception survey conducted in October 2019 with 1511 refugees in Uganda. Both South Sudanese and Congolese refugees who have received aid and support from humanitarian organisations in the last 12 months are included. Surveys were conducted in Adjumani (Nyumanzi, Baratuku, Elema), Bidibidi (Zone 1 and Zone 3), Imvepi (Zone I and Zone II), Kiryandongo (Ranch 1 and Ranch 37), Palorinya (Belemaling, Chinyi, Morobi), Rhino (Zone 2 – Omugo, Zone 3 - Ocea), Kyaka II (Byabakora, Kakoni, Mukondo), Kyangwali (Kirokole, Maratatu A, Maratatu B), Nakivale (Base Camp), and Rwamwanja (Base Camp, Kaihora, Nkoma).
  • 1300+ Downloads
    Updated March 26, 2020 | Dataset date: Mar 17, 2020
    This dataset updates: As needed
    The COVID-19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories: - Social distancing - Movement restrictions - Public health measures - Social and economic measures - Human rights implications Each category is broken down into several types of measures. ACAPS consulted government, media, United Nations, and other organisations sources. For any comments, please contact us at info@acaps.org Please note note that some measures may not be recorded and the exact date of implementation may not be accurate in some cases, due to the different way of reporting of the primary data sources we used.