Missing Migrants Project draws on a range of sources to track deaths of migrants along migratory routes across the globe. Data from this project are published in the report “Fatal Journeys: Tracking Lives Lost during Migration,” which provides the most comprehensive global tally of migrant fatalities for 2014, and estimates deaths over the past 15 years.
May 27, 2020
| Dataset date: Jan 1, 1997-Dec 31, 2020
This dataset updates: Every week
The ACLED project codes reported information on the type, agents, exact location, date, and other characteristics of political violence events, demonstrations and select politically relevant non-violent events. ACLED focuses on tracking a range of violent and non-violent actions by political agents, including governments, rebels, militias, communal groups, political parties, external actors, rioters, protesters and civilians. Data contain specific information on the date, location, group names, interaction type, event type, reported fatalities and contextual notes.
| 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.
| Dataset date: Jun 1, 2018-Jun 1, 2028
This dataset updates: Live
Subset of the full list of crisis identifiers available at GLIDEnumber.net. OCHA provides these GLIDE numbers for interoperability purposes only, so that partners will know what identifiers we are using internally. Please refer to GLIDEnumber.net for the complete and most up-to-date list.
This data and report examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, changes in food market operability and food security, consumer behavior changes, and trust in governments to prevent the spread of coronavirus. This dataset includes data from 10 of the markets. Please contact us for access to data from all markets, the questionnaire, and with any other questions.
| Dataset date: Dec 1, 2019-May 30, 2020
This dataset updates: Live
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 email@example.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 (firstname.lastname@example.org) 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:
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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.
May 26, 2020
| Dataset date: Dec 31, 2019-May 26, 2020
This dataset updates: Every week
The Database of Government Actions on COVID-19 in Developing Countries collates and tracks national policies and actions in response to the pandemic, with a focus on developing countries.
The database provides information for 20 Global South countries – plus 6 Global North countries for reference – that Dalberg staff are either based in or know well. The database content is drawn from publicly available information combined, crucially, with on-the-ground knowledge of Dalberg staff.
The database contains a comprehensive set of 100 non-pharmaceutical interventions – organized in a framework intended to make it easy to observe common variations between countries in the scope and extent of major interventions. Interventions we are tracking include:
• Health-related: strengthening of healthcare systems, detection and isolation of actual / possible cases, quarantines
• Policy-related: government coordination and legal authorization, public communications and education, movement restrictions
• Distancing and hygiene: social distancing measures, movement restrictions, decontamination of physical spaces
• Economic measures: economic and social measures, logistics / supply chains and security.
We hope the database will be a useful resource for several groups of users: (i) governments and policymakers looking for a quick guide to actions taken by different countries—including a range of low- and middle-income countries, (ii) policy analysts and researchers studying the data to identify patterns of actions taken and compare the effectiveness of different interventions in curbing the pandemic, and (iii) media and others seeking to quickly access facts about the actions taken by governments in the countries covered in the database.
Comments on the data can be submitted to email@example.com
Questions can be submitted to firstname.lastname@example.org
In conjunction with OpenStreetMap, we are creating tools that we hope will enable the mapping community to enjoy a faster & more accurate mapping experience. To this end, we used artificial intelligence to predict features on high-resolution satellite imagery; these features are then populated in our RapiD map editing tool. When combined with global-scale humanitarian efforts, we hope RapiD can help realize our vision of a more connected world. Find out more: https://mapwith.ai/
West and Central Africa Administrative boundaries, administrative level 0 to 2. Notice: The boundaries and names shown and the designations used on these shapefiles do not imply official endorsement or acceptance by the United Nations.
West and Central Africa settlements with administrative capitals
Chile administrative level 0 (country), 1 (region), 2 (province), and 3 (municipality) boundaries
These boundaries are suitable for database or GIS linkage to the Chile - Subnational Population Statistics.
May 26, 2020
| Dataset date: Mar 1, 2020-Apr 30, 2020
This dataset updates: As needed
This dataset includes the latest available information on COVID-19 developments impacting the security of aid work and operations to help aid agencies meet duty of care obligations to staff and reach people in need.