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.

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.

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.

This dataset contains two APIs with daily Symptom Survey data. The University of Maryland API is for accessing global survey data and CMU API is for accessing US survey data.

Incidents of insecurity events pulled from the ACLED data API. ACLED was not directly involved in filtering or selecting these events.

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.

This bucket contains FAIR COVID-19 US county level forecast data

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/

The ACU’s Information Management Unit conducted the 5th version of its annual research “Schools in Syria”, to highlight the impact of the Syrian conflict on the education sector in the Syrian Arab Republic. This is the most representative, nuanced iteration of this study to date, covering 4,016 schools within 78 sub-districts across 6 governorates in all non-governmental areas of the Syrian Arab Republic, building upon 38,538 data e-forms with 34,522 forms on perception surveys with school students, teachers, principals and parents. The research key findings and data represent the time period of February till May 2019 which cover the second semester of the academic year 2018/2019. This report provides a broad view of the current obstacles and most pressing needs of the educational sector, covering a spectrum of issues ranging from facilities to student and teacher life in Syria’s embattled regions. The report consists of 15 sections: Methodology, General Information, Functioning School Buildings, WASH in Schools, School Equipment, School Levels and School Days, Curricula, Certificates, Students, School and Students Needs, Teachers, Disabled Students and Psychosocial Support, Policies and Regulations within Schools, Non-Functional Schools, Priorities and Recommendations. The 5th version of Schools in Syria will be released and published by mid-December 2019.

The ACU’s Information Management Unit conducted the 4th version of its annual research “Schools in Syria”, to highlight the impact of the Syrian conflict on education and the needs of students and school supplies. This is the most representative, nuanced iteration of this study to date, covering 4,079 schools within 99 sub-districts across 10 governorates, building upon 35,925 data e-forms with 31,846 forms on perception surveys. It has significant increase in the number of the functional schools addressed over its first version to the current one by 2,572 schools. Please note to be more specific as possible on the following when you request the data: Why are you requesting the data? What is the intent to use the data? What is your role? Who is the organization that you represent for?

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

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.

Subnational data about Covid19 in Niger - Infected (new cases, gender), Deceased, Recovered.

Subnational data about Covid19 in Senegal- 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. For Senegal, the Not specified infected cases are the contact cases which are not localized.

COvid 19 subnational data for iraq

This dataset contains IDPs, Returnees at sub admin level.

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).

The dataset contains number of IDPs at sub national level.

Shelter Cluster 4W report (Who does What, Where, and When) for typhoon Goni (Rolly) and Vamco (Ulysses) in the Philippines, as of 01 December 2020.

This data is about the humanitarian interventions of local/international organizations, faith based organizations, government, private sectors, UN agencies, and Red Cross in the areas affected by the typhoons.

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.