| Dataset date: December 01, 2019-May 26, 2022
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 firstname.lastname@example.org 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 (email@example.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.
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.
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.
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/
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.
26 May 2022
| Dataset date: March 01, 2022-May 21, 2022
This dataset is part of the Ukraine Data Explorer Consolidated Multi-Purpose Cash (MPC) 4W and 5W data (Who does What, Where) from Ukraine Cash Working Group (CWG), collected from partners involved in the MPC assistance.
26 May 2022
| Dataset date: February 24, 2022-May 27, 2022
Data collected by CBi on cash and in-kind donations made by private sector entities (corporations and corporate-affiliated foundations) to help support the the humanitarian response in Ukraine. This data is used to power the Ukraine Private Sector Donations Tracker.
26 May 2022
| Dataset date: April 14, 2022-April 14, 2022
This dataset includes the names and coordinates of international border crossings of Ukraine with Belarus, Hungary, Moldova, Poland, Romania, Russian Federation and Slovakia. The names of the border crossings are in English and Ukrainian.
26 May 2022
| Dataset date: May 31, 2020-May 31, 2020
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)
26 May 2022
| Dataset date: May 26, 2022-May 26, 2022
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.
25 May 2022
| Dataset date: January 31, 2017-December 31, 2022
This data has been produced by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. The data provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance. It represents a consolidated evidence base and helps inform joint strategic response planning.
25 May 2022
| Dataset date: November 01, 2015-February 28, 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).
25 May 2022
| Dataset date: November 13, 2019-May 27, 2022
With its Global Militarisation Index (GMI), BICC is able to objectively depict worldwide militarisation for the first time. The GMI compares, for example, a country’s military expenditure with its Gross Domestic Product (GDP) and its health expenditure.
It contrasts the total number of military and paramilitary forces in a country with the number of physicians. Finally, it studies the number of heavy weapons available to a country’s armed forces. These and other indicators are used to determine a country’s ranking, which in turn makes it possible to measure the respective level of militarisation in comparison to other countries.
The latest GMI of 2021 covers 153 countries and is based on the latest available figures (in most cases data for 2020). Israel, Oman, Azerbaijan, Kuwait, Armenia, Saudi Arabia, Brunei, Bahrain, Singapore and Russia are the top 10 worldwide. These countries allocate particularly high levels of resources to the military in comparison to other areas of society. See project website for more information
25 May 2022
| Dataset date: January 01, 2021-May 27, 2022
At the time of the Global Humanitarian Overview 2022 launch in December 2021, 274 million people need humanitarian assistance and protection. This number means that 1 in 29 people worldwide needs humanitarian assistance – a significant increase from 1 in 33 in 2020 and 1 in 45 in 2019, which were already the highest figures in decades. The UN and partner organizations aim to assist 183 million people most in need across 30 countries and 7 regions and require a total of $ 41 billion to do so.
24 May 2022
| Dataset date: January 01, 1997-May 20, 2022
A weekly dataset providing the total number of reported civilian targeting events and fatalities broken down by country. Civilian targeting events include violence against civilians events and explosions/remote violence events in which civilians were directly targeted.
Note: These are aggregated data files organized by country-year and country-month. To access full event data, please register to use the Data Export Tool and API on the ACLED website.
24 May 2022
| Dataset date: September 01, 2016-April 01, 2018
More than 200 million businesses use Facebook. Meta partners with various academic and international organizations to survey these businesses throughout the year to learn about their perspectives, challenges and opportunities. With more businesses leveraging online tools each day, these surveys provide a lens into a new mobilized and digital economy.
The Future of Business Survey (FoBS) is a collaboration between Meta, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises or Businesses (SMEs/SMBs). The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. The Future of Business Survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence. In 2020, the Future of Business Survey shifted to do additional monthly waves with questions focused on business challenges related to COVID-19.
Meta also shares information from other SMB surveys, such as those that feed into the Global State of Small Business Reports, which are fielded to active Facebook Page Administrators and the general population of Facebook users. These surveys are conducted in approximately 30 countries across the globe and are also conducted on a roughly bi-annual basis.
Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, reduction of employees and challenges/needs of the business.
Aggregated country level data is available to the public here for each wave and controlled access microdata is available to Data for Good partners.
To request access to survey microdata or to see published Meta reports, please visit: futureofbusinesssurvey.org
Update 12/12/21: We have transitioned all small business survey datasets to have survey weights applied. Files with weighted estimates have now replaced the previously posted unweighted estimates.
Update 10/4/21: Aggregate data in files [gsosb_2021julyaugust_data_aggregate_weighted.csv] and [gsosb_2021february_data_aggregate_weighted.csv] have been updated to provide weighted results and to correct an error found in those two files. These files were discovered to present results for large businesses rather than small and medium businesses and have been updated to provide the information for the latter group. Downloads of these two files that took place between April 1 and September 30 2021 were affected and analysis using downloads from this period may need to be amended. No analyses or reports published by Facebook were affected.
Please contact firstname.lastname@example.org for any questions.
24 May 2022
| Dataset date: August 18, 2021-May 27, 2022
This bi-weekly dataset provides an overview of the latest areas of control in Yemen and includes a map, excel spreadsheet and GIS layer. All datasets are fully PCODED to Admin 2 district level, enabling interoperability with other datasets. Through understanding the latest areas of control, it is possible to enhance analysis in areas such as political-economy through adjusting tools to take into account the current geographic situation. This dataset forms part of the Yemen CrisisInsight product suite which includes the Yemen Core Dataset, Yemen: Crisis Impact Overview and Yemen Risk Overview.