World

Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
More
  • 2800+ Downloads
    Updated Live | Dataset date: Dec 1, 2019-Sep 21, 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.
  • 800+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 21, 2020
    This dataset updates: Every two weeks
    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
  • 80+ Downloads
    Updated September 22, 2020 | Dataset date: Jan 1, 1990-Sep 22, 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.
  • 4500+ Downloads
    Updated September 22, 2020 | Dataset date: Mar 10, 2020-Sep 21, 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
  • 90+ Downloads
    Updated September 21, 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.
  • Updated September 21, 2020 | Dataset date: Aug 27, 2020
    This dataset updates: As needed
    We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook. Details on the underlying data and the construction of the index are provided in the “Facebook Social Connectedness Index - Data Notes.pdf” file. Please also see https://dataforgood.fb.com/ as well as the associated research paper “Social Connectedness: Measurement, Determinants and Effects,” published in the Journal of Economic Perspectives (https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259).
  • 2500+ Downloads
    Updated September 21, 2020 | Dataset date: Jan 1, 2016-Aug 31, 2020
    This dataset updates: Every month
    This dashboard provides aggregated global data on the safety & security incidents affecting NGOs in those countries covered by INSO*. It is intended to improve the visibility of macro-trends in humanitarian safety in order to raise awareness, inform research and strengthen operational practise. All data is sourced from INSO and assumed correct at the time of publishing. Please read below for advanced definitions & meanings. The information contained in this dashboard may be cited or reproduced only with credit to INSO.
  • 3400+ Downloads
    Updated September 20, 2020 | Dataset date: Sep 21, 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.
  • 100+ Downloads
    Updated September 18, 2020 | Dataset date: Sep 18, 2020
    This dataset updates: Every week
    This dataset lists project funding allocations from OCHA's Central Emergency Response Fund (CERF). CERF allocations are made to ensure a rapid response to sudden-onset emergencies or to rapidly deteriorating conditions in an existing emergency and to support humanitarian response activities within an underfunded emergency.
  • 100+ Downloads
    Updated September 18, 2020 | Dataset date: Sep 22, 2020
    This dataset updates: Every week
    This dataset lists all contributions made by donors to the Central Emergency Response Fund (CERF). CERF receives broad support from United Nations Member States, observers, regional governments and international organizations, and the private sector, including corporations, non-governmental organizations and individuals.
  • 100+ Downloads
    Updated September 18, 2020 | Dataset date: Sep 18, 2020
    This dataset updates: Every week
    Raw Global 3W national data visualized in the Global Humanitarian Operational Presence Who, What, Where (3W) Portal as of 18 September 2020 Fields: 'country code' -- example 'UKR' for Ukraine 'sector' -- example 'CCCM' = Camp Coordination / Management 'organization' -- Organization name provided in the field inputs 'type' -- INGO (International NGO), NNGO (National NGO), UN (United Nations), 'Undefined', or 'Other' 'COVID 19' -- 'yes' / 'no' (pertaining to the context) '3W date' -- date of data applicability 'Display Name' -- Organization name - standardized as much as possible Note these intended to be cleaned and standardized, though language variations (such as "Acción Contra el Hambre" / "Action Against Hunger" / "Action Contre La Faim"), and national offices of international organizations (such as "Caritas Internationalis - Nigeria" / "Caritas Internationalis - Switzerland") are preserved. Note also that the names of some organizations working in Protection have been suppressed. Users with questions are encouraged to use the 'Contact the contributor' button below.
  • 900+ Downloads
    Updated September 17, 2020 | Dataset date: Apr 23, 2020
    This dataset updates: As needed
    Global Humanitarian Response Plan COVID-19 administrative level 1 boundaries, gazetteer and population tables for countries covered by the May update of the Global Humanitarian Response Plan COVID-19. 17 SEPTEMBER 2020 UPDATE: The dataset has been updated to reflect the new (2020) Paraguay COD-PS projections and the adjusted Paraguay P-codes. Users should note that the Paraguay P-codes in earlier versions no longer correspond to the current CODs. 26 AUGUST 2020 UPDATE: administrative level 1 total population statistics have been added for the following 11 countries: Benin , Djibouti, Liberia, Pakistan, Panama, Philippines, Paraguay, Sierra Leone, Togo, Uruguay, Zimbabwe. Population statistics are now available for 48 of the 63 countries or territories. Data fields: ADM1_PCODE: Administrative level 1 (various types) P-code ADM0_PCODE: Administrative level 0 (country or territory) P-code alpha_3: ISO 3166-1 Alpha 3 country or territory identifier ADM0_REF: Administrative level 0 (country or territory) reference name (Latin script without special characters) ADM1_REF: Administrative level 1 (various types) reference name (Latin script without special characters) Population: Most recent available total population. PLEASE SEE CAVEATS
  • 2000+ Downloads
    Updated September 17, 2020 | Dataset date: Sep 16, 2020
    This dataset updates: Every month
    This set includes data on fatalities in UN peacekeeping operations. It includes a unique casualty identifier, the incident date, the mission acronym, the type of casualty, the ISO code associated with the country of origin of the personnel, the relevant M49 DESA code, the type pf personnel involved, and the type of incident.
  • 2200+ Downloads
    Updated September 17, 2020 | Dataset date: Sep 16, 2020
    This dataset updates: Every month
    Peacekeeping Uniformed Contributions by Gender, as of end of last calendar month, associated with unique OD, Country ISO Code, M49 DESA code, Country Name of Troop or Police Contributing country, Mission, Description of uniformed category, Gender, and Monthly report Date. This data set will be update monthly.
  • 4200+ Downloads
    Updated September 17, 2020 | Dataset date: Sep 16, 2020
    This dataset updates: Every month
    Peacekeeping Uniformed Contributions by Rank of Troop- or Police-Contributing Country, as of end of last calendar month, associated with unique ID, Country ISO Code, M49 DESA code, Country Name of Troop or Police Contributing country, Rank for the Month, Number of male uniformed personnel, Number of female uniformed personnel, and Monthly report Date. This data set will be updated monthly.
  • 60+ Downloads
    Updated September 16, 2020 | Dataset date: Sep 17, 2020
    This dataset updates: Every week
    Topline figures dataset for the Central Emergency Response Fund's organisation page
  • 5400+ Downloads
    Updated September 16, 2020 | Dataset date: Jan 1, 1960-Dec 31, 2019
    This dataset updates: As needed
    World Bank Indicators of Interest to the COVID-19 Outbreak. This link is to a collection in the World Bank data catalog that contains datasets that may be useful for analysis, response or modelling.
  • 15000+ Downloads
    Updated September 16, 2020 | Dataset date: Mar 17, 2020
    This dataset updates: Every week
    The #COVID19 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 Lockdowns 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 together with non-compliance policies 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.
  • 200+ Downloads
    Updated September 15, 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)
  • 50+ Downloads
    Updated September 14, 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.
  • 90+ Downloads
    Updated Live | Dataset date: May 1, 2019-Dec 31, 2020
    This dataset updates: Live
    This dataset combines two sources of education-insecurity data: Machine-learning-driven counts of tweets from Africa and the Middle East on the topic of education insecurity—in Arabic, English, and French—via the Artificial Intelligence for Disaster Response (AIDR) project Human-curated reports of actual education-insecurity events in Africa and the Middle East, via the Armed Conflict Location & Event Data (ACLED) project.
  • 3200+ Downloads
    Updated September 9, 2020 | Dataset date: Jan 1, 2017-Jun 30, 2020
    This dataset updates: As needed
    The Safeguarding Health in Conflict Coalition (SHCC) is made up of 40 health provider organizations, humanitarian groups, human rights organizations, NGOs, and academic programs to take action to protect health workers and end attacks against them. This page is managed by SHCC member Insecurity Insight.
  • 2000+ Downloads
    Updated Live | Dataset date: Jan 1, 2020-Sep 22, 2020
    This dataset updates: Live
    Governments are taking a wide range of measures in response to the COVID-19 outbreak. The Oxford COVID-19 Government Response Tracker (OxCGRT) aims track and compare government responses to the coronavirus outbreak worldwide rigorously and consistently. The OxCGRT systematically collects information on several different common policy responses governments have taken, scores the stringency of such measures, and aggregates these scores into a common Stringency Index. For more, please visit > https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker
  • 900+ Downloads
    Updated September 5, 2020 | Dataset date: Jan 19, 2015
    This dataset updates: Never
    (DEPRECATED: please use the data at https://data.humdata.org/dataset/global-coordination-groups-beta instead — also available at http://vocabulary.unocha.org). Based on feedback from Humanitarian Information Management community, the Humanitarianresponse.info team in OCHA has released three letters standard clusters codes via APIs to help facilitate interoperability between websites and data from several humanitarian platforms including HumanitarianRespons.info, ReliefWeb, HDX, Online Reporting System, Online Project System, Financial Tracking Service and the developer communities.
  • 300+ Downloads
    Updated Live | Dataset date: Sep 2, 2019
    This dataset updates: Live
    This dataset contains an active archive of flood event records from 1985 to present. Details such as the country affected, the number of people killed, the number of people displaced, the cost of damages, and a measure of the magnitude of the flood are included for each flood event. The archive is updated on an ongoing basis and new flood event are added immediately. The information presented in this Archive is derived from news, governmental, instrumental, and remote sensing sources.