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  • Updated September 22, 2020 | Dataset date: Jul 31, 2017
    This dataset updates: Never
    The dataset contains IDPs
  • Updated September 22, 2020 | Dataset date: Mar 7, 2018
    This dataset updates: Never
    The dataset contains IDPs numbers
  • 100+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 22, 2020
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by locations due to the Coronavirus pandemic in Libya.
  • 100+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 22, 2020
    This dataset updates: Every week
    This dataset contains the number of confirmed cases, recoveries and deaths by Governorate due to the Coronavirus pandemic in Palestine.
  • Updated September 22, 2020 | Dataset date: Sep 1, 2017-Sep 30, 2017
    This dataset updates: Never
    The dataset contains number of IDPs!
  • Updated September 22, 2020 | Dataset date: Aug 1, 2020
    This dataset updates: Every month
    This study includes information on the population of all cities and towns outside the control of the Regime in Syria. The study is updated monthly, in that IMU enumerators of ACU track the population in all areas outside the control of the regime, along with movements of displacement and return on a permanent basis. This study also presents the total number of population and gender ratio, the total number of IDPs and the types of shelters in which they are settled, the number of newly displaced people during the last month and the types of shelters in which they are settled, the number of those who left and the reasons that forced them to leave their home towns, the number of returnees during the last month with their most critical needs. The Study presents information on the situation of the local councils in areas to which the residents returned during the past month, availability of basic services in areas of return, evaluation of these services, decision-makers and primary service providers, and sources of income for returnees. The study data can be shown at different levels through the filter bar at the top of the page; it is also possible to display the graphic figures at three levels (district - sub-district - community) through the buttons at the bottom of the figures. Maps can be shown at two levels (district - sub-district) through the two buttons at the bottom of the map. Data can be downloaded from the last page of the study. For more details, please contact us through IMU email address: imu@acu-sy.org
  • Updated September 22, 2020 | Dataset date: Sep 21, 2020
    This dataset updates: Every year
    This data contains aggregated weighted statistics at the regional level by gender for the Survey on Gender Equality At Home fielded in July 2020. Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality.
  • 300+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 22, 2020
    This dataset updates: Every year
    South Sudan administrative levels 0 (country), 1 (state) and 2 (county) 2020 population estimates. REFERENCE YEAR: 2020 This population statistics Common Operational Database (COD-PS) was endorsed by the South Sudan Inter Cluster Working Group (ICWG) and Humanitarian Country Team (HCT) in September, 2019. These tables are suitable for database or GIS link to the South Sudan - Subnational Administrative Boundaries.
  • 1000+ Downloads
    Updated September 22, 2020 | Dataset date: Jan 1, 2019-Jul 4, 2020
    This dataset updates: Every three months
    DTM’s Displacement Tracking tool collects and reports on displaced numbers of households on a daily basis, allowing for regular reporting of new displacements in terms of numbers, geography and needs. More than 3.6 million people are displaced as per August 2018 assessment.
  • 11000+ Downloads
    Updated September 22, 2020 | Dataset date: May 1, 2020-Jun 30, 2020
    This dataset updates: Every three months
    The dataset has displaced location of IDPs & households. Last displacement at Governorates (admin1) level, shelter type and period of last displacement.
  • 3400+ Downloads
    Updated September 22, 2020 | Dataset date: Oct 1, 2019-Dec 31, 2019
    This dataset updates: Every six months
    The Dataset contains IDPs, returnees at sub national level.
  • Updated September 22, 2020
    This dataset updates: Every day
    This survey runs daily and collects information that reflects the economic ability of a household to access a variety of foods. It checks for consumption of the following food groups: Cereals, Plantains/Tubers, Vegetables, Fruits, Meat, Eggs, Fish/Seafood, Grains, Dairy, Oils/Fats, Sugar
  • 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.
  • 1900+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 18, 2020
    This dataset updates: Every year
    Chad administrative level 0-2 2021 projected sex and age disaggregated population statistics REFERENCE YEAR: 2021 See caveats These table are suitable for database or GIS linkage to the Chad - Subnational Administrative Boundaries.
  • 5000+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 20, 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
  • 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.
  • 100+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 22, 2020
    This dataset updates: Every day
    Covid-19 Impact on Humanitarian Operations Data Viz inputs
  • 6600+ Downloads
    Updated September 22, 2020 | Dataset date: Sep 22, 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.
  • 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
  • 30+ Downloads
    Updated September 22, 2020 | Dataset date: Apr 29, 2016
    This dataset updates: As needed
    No abstract provided
  • 2400+ Downloads
    Updated September 21, 2020 | Dataset date: Aug 1, 2020
    This dataset updates: Every month
    The Syrian IDP camps monitoring interactive study is issued by the IMU of the ACU on a monthly basis, to monitor the humanitarian situation of 231 IDp camps in Idleb and Aleppo governorates in Syria’s northwest, shedding light on the needs of the IDPs and the services provided in the camps in the following sectors: Population statistics, WASH, Health, Education, FSL, Shelter and NFI, in addition to the priority needs of IDPs. The study also includes statistics of those who arrive at and leave the camps and the important incidents which took place during the month of the data collection.
  • 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.
  • 300+ Downloads
    Updated September 21, 2020 | Dataset date: Jan 1, 2019-Sep 21, 2020
    This dataset updates: Every day
    This dataset contains the number of confirmed cases, recoveries and deaths by province due to the Coronavirus pandemic in Afghanistan.
  • 4900+ Downloads
    Updated September 21, 2020 | Dataset date: Mar 1, 2020-Aug 31, 2020
    This dataset updates: Every day
    These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day. Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/