AndreaBozzo/ceres-open-data-index huggingface dataset:
Ethiopia: Level 1 Exposure Data

This data was developed as part of the Modelling Exposure Through Earth
Observation Routines (METEOR) project and is a Level 1, or a global-quality
exposure data set. Minimal country-specific data was collected. The data is
intended for CAT modeling and loss estimation. Repurposing this data for any
reason other than assessing risk is not recommended. The data presents the
estimated number of buildings, building area, and rebuilding value at a
15-arcsecond grid resolution (approximately 500 meters at the equator). This
data set is in point shapefile format where the points represent the centroids
of the 15-arcsecond grid. The results were created through a process of
spreading the number of buildings to the 15-arcsecond level by a statistical
assessment of moderate resolution EO data, which is described in more detail
in the dasymetric mapping lineage processing step. The estimated building count
at any given area is a result of statistical processes and should not be
mistaken as a building count. The structural classes of buildings used for risk
assessment are estimated given the building wall, floor, and roof material
classes surveyed through 2002 Population and Housing Census - Volume 1.
Analytical report.  Additionally, the data is provided in Open Exposure Data
(OED) import format, as a pair of CSV files. One CSV file contains the
location details, and the other is an "account" file that is filled with
default information to satisfy OED format requirements. The OED input files
are set to use "All perils" (i.e. "AA1"). All required OED account-related
fields are populated with "1" by default (such as PortNumber, AccNumber,
PolNumber).

If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes:
        https://forms.gle/DQjhE89CRegNKB3X8

Please see the METEOR project page for information about the METEOR Project:
        http://meteor-project.org/

Please see the METEOR map portal for interactive maps:
        https://maps.meteor-project.org/

For more information about the Open Exposure Data (OED) standard, please see
        https://github.com/OasisLMF/OpenDataStandards

Tags: disaster risk reduction-drr, hazards and risk
Organization: Global Earthquake Model Foundation
Bangladesh: Level 1 Exposure Data

This data was developed as part of the Modelling Exposure Through Earth
Observation Routines (METEOR) project and is a Level 1, or a global-quality
exposure data set. Minimal country-specific data was collected. The data is
intended for CAT modeling and loss estimation. Repurposing this data for any
reason other than assessing risk is not recommended. The data presents the
estimated number of buildings, building area, and rebuilding value at a
15-arcsecond grid resolution (approximately 500 meters at the equator). This
data set is in point shapefile format where the points represent the centroids
of the 15-arcsecond grid. The results were created through a process of
spreading the number of buildings to the 15-arcsecond level by a statistical
assessment of moderate resolution EO data, which is described in more detail
in the dasymetric mapping lineage processing step. The estimated building count
at any given area is a result of statistical processes and should not be
mistaken as a building count. The structural classes of buildings used for risk
assessment are estimated given the building wall, floor, and roof material
classes surveyed through 2002 Population and Housing Census - Volume 1.
Analytical report.  Additionally, the data is provided in Open Exposure Data
(OED) import format, as a pair of CSV files. One CSV file contains the
location details, and the other is an "account" file that is filled with
default information to satisfy OED format requirements. The OED input files
are set to use "All perils" (i.e. "AA1"). All required OED account-related
fields are populated with "1" by default (such as PortNumber, AccNumber,
PolNumber).

If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes:
        https://forms.gle/DQjhE89CRegNKB3X8

Please see the METEOR project page for information about the METEOR Project:
        http://meteor-project.org/

Please see the METEOR map portal for interactive maps:
        https://maps.meteor-project.org/

For more information about the Open Exposure Data (OED) standard, please see
        https://github.com/OasisLMF/OpenDataStandards

Tags: disaster risk reduction-drr, hazards and risk
Organization: Global Earthquake Model Foundation
Burundi- Muyinga Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Muyinga in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). Additionally the dataset includes until when the organisations will have funding to operate their activities. This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Tags: hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Burundi - Rumonge Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Rumonge in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). Additionally the dataset includes until when the organisations will have funding to operate their activities. This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Tags: hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Burundi - Ruyigi Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Ruyigi in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field. 

Tags: funding, hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Burundi - Kirundo Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Kirundo in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field. 

Tags: hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Burundi - Bujumbura Mairie Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Bujumbura Mairie in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Tags: funding, hxl, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Burundi- Kayanza Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Kayanza in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Tags: hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Mapping Education Insecurity, May 2019 to Feb 2021

This dataset and visualisation show the results of a trial conducted by the Centre for Humanitarian Data and the [Artificial Intelligence for Disaster Response](http://aidr.qcri.org/) (AIDR) project from May 2019 to February 2021, using machine learning to identify Twitter posts relevant to education insecurity in English, French, and Arabic for countries in Africa and the Middle East. The visualisation combines the Twitter results with reports on education insecurity from the [Armed Conflict Location & Event Data](https://acleddata.com) (ACLED) project, to test for correlations.

Tags: community engagement, conflict-violence, education, hxl, social media data
Organization: Education and Conflict Monitor
Guinea Ebola EVD 2021 subnational cases, deaths, hospitalisations, contact tracing, and vaccinations

Subnational dataset on the 2021 Ebola outbreak in Guinea including cases, deaths, hospitalisations, contact tracing, and vaccinations. The data is down to sub-prefecture level and updated approx 1-3 days since 15/02/21. The data has been extracted from the Government of Guinea situation reports, available from Relief Web. 

The dataset contains the following information:
- Cas: Distribution of Ebola cases by health district, including suspected, confirmed, and probable cases.
- Patients et des décès: Situation of patients and deaths in epidemiological treatment centres, including hospitalisations, discharges, and deaths. 
- Suivi des contacts: Status of contact tracing including number of contacts followed up, number not followed up, reasons for not following up, follow-up rate, contacts still remaining to follow up, and number of contacts who have become suspected cases.
- Vaccinations: Vaccination results and targets in N’Zérékoré, including target contacts, daily vaccine contacts, number vaccinated (cumulative), and percent of target contacts who have been vaccinated.

For more information, please see the README sheet of the dataset. 
For any questions, please contact ah@acaps.org.

Tags: africa, disease, health
Organization: ACAPS
Post Tropical Storm Chalane Mapping, Chikwawa, Malawi

Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/

MORE CROWDDRONING BY GLOBHE

Webb: https://globhe.com/

Facebook: https://www.facebook.com/Crowddroning

Twitter: https://twitter.com/globhedrones

Instagram: https://www.instagram.com/globhedrones/

LinkedIn: https://www.linkedin.com/company/globhedrones/

Tags: cyclones-hurricanes-typhoons
Organization: Globhe Drones
Global Secondary Impacts of COVID-19

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. 

Tags: covid-19, indicators
Organization: ACAPS
Democratic Republic of Congo (DRC)  Health Facilities (OpenStreetMap Export)

République démocratique du Congo (RDC), Établissements de santé

Points + Polygons : Interactive  Online Map and Tabular spreadsheet (For multipolygons lat,lon=centroid)

* First Column *Thematic* describes line Feature content : hospital, doctors, clinic, pharmacy, dentist,veterinary
* Objets Institutions de la santé (hopitaux, médecins, cliniques, pharmacies, dentiste)
* OSM  amenity IN ('doctors','clinic','hospital','pharmacy','dentist')

Columns  :thematic,  osmtype, lat (latitude),  lon (longitude) osm_id (unique point id), [amenity](http://wiki.openstreetmap.org/wiki/amenity),  [name](http://wiki.openstreetmap.org/wiki/name),  [name:fr](http://wiki.openstreetmap.org/wiki/name:fr),  [name:en](http://wiki.openstreetmap.org/wiki/name:en),  [health_facility:type](http://wiki.openstreetmap.org/wiki/health_facility:type),  beds, health_services:description, heliport, operator, operator:type, private, address, addr:housenumber, addr:street, addr:city, building,  [building:levels](http://wiki.openstreetmap.org/wiki/building:levels), [building:material](http://wiki.openstreetmap.org/wiki/building:material), [building:condition](http://wiki.openstreetmap.org/wiki/building:condition), building:status, note, layer, other_tags

* [OpenStreetMap data extraction from](https://download.geofabrik.de)
*[ETL2HDX Procedures](https://github.com/opendatalabrdc/OSM_Exports)

Tags: facilities-infrastructure, geodata, health facilities
Organization: OpenStreetMap RDC
Democratic Republic of Congo (DRC) Transport Infrastructures (OpenStreetMap Export)

République démocratique du Congo (RDC), Transport

Points + Lines + Polygons  OSM Features Interactive Online Map - 

First Column Thematic Variable shows line content : Airport facility, Airport runway, Helipad, Harbour, Shipyard, Rail line, Rail yard, Transport Station & Stop, Ferry Terminal, Transport Ferry Route, Transport Route Itinaries
 
[Aeroways (Airports)](https://wiki.openstreetmap.org/wiki/Key%3Aaeroway)
[Railways](https://wiki.openstreetmap.org/wiki/Key:railway)
[Aerialways](https://wiki.openstreetmap.org/wiki/Key:aerialway)
[Railway, Tram Routes](https://wiki.openstreetmap.org/wiki/Relation:route)

Variables : 
name, name:en, name:fr, operator, operator:type, natural, aerialway, aerodrome, aeroway, runway, taxiway, apron, helipad, heliport, iata, icao, public_transport, rail, railway, ref, route, network, station, service, shelter, harbor, bus, highway, bridge, tunnel, landuse, surface, building, building:levels, building:material, building:condition, building:status, building:min_level, address, addr:housenumber, addr:street, addr:city, layer, private, source_ref, source, source:name, source:date, source:data, source_ref:1, survey:date, timezone, toilets, type, voltage, weelchair, wikidata, wikipedia

OSM Filter

    OSM features :
    thematic in (
    'Rail yard',
    'Rail line',
    'Transport Route Itinary',
    'Helipad', 'Airport Runway',
    'Airport Facility',
    'Shipyard', 'Ferry Terminal',
    'Transport Station, Stop')


Tags: facilities-infrastructure, geodata, transportation
Organization: OpenStreetMap RDC
Democratic Republic of Congo (DRC) Major Roads Network (OpenStreetMap Export)

République démocratique du Congo (RDC), Réseau routier principal


Lines + Polygons : Main roads Network Interactive Online Map

[OSM Roads](https://wiki.openstreetmap.org/wiki/Key:highway)
Columns: osm_id,  highway,  name,  layer,  bridge,  tunnel,  oneway,  ref ,  route,  junction,  lanes,  width,  maxspeed,  surface,  practicability, smoothness, condition, geometry

OSM Filter

    WHERE highway in
     ('motorway','motorway_link','trunk','trunk_link','primary','primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') 
    OR route='ferry' OR amenity in ('ferry_terminal')
    OR ( (bridge is not null OR tunnel is not null ) AND highway in ('motorway','motorway_link','trunk','trunk_link','primary', 'primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') ) 


The second part of the Road network is available from the  [Local Roads dataset](https://data.humdata.org/dataset/democratic-republic-of-congo-drc-local-roads-openstreetmap-export)

Tags: geodata, roads, transportation
Organization: OpenStreetMap RDC
Democratic Republic of Congo (DRC) Local Roads (OpenStreetMap Export)

République démocratique du Congo (RDC), Réseau routier local

Lines + Polygons : Local roads Interactive Online Map

[OSM Roads](https://wiki.openstreetmap.org/wiki/Key:highway)
Columns: osm_id,  highway,  name,  layer,  bridge,  tunnel,  oneway,  ref ,  route,  junction,  lanes,  width,  maxspeed,  surface,  practicability, smoothness, condition, geometry

OSM Filter

    highway in ('residential', 'living_street', 'path', 'footway', 'pedestrian', 'steps', 'cycleway', 'track', 'service', 'bridleway', 'construction', 'road')

* highway=road attribute for roads not yet classified.

The  Major Roads dataset is available from the  [Major Roads network dataset](https://data.humdata.org/dataset/democratic-republic-of-congo-drc-major-roads-network-openstreetmap-export)

Tags: geodata, roads, transportation
Organization: OpenStreetMap RDC
Democratic Republic of Congo (DRC) Infrastructures, Mining and Fabrication (OpenStreetMap Export)

République démocratique du Congo (RDC), Infrastructures, Mines et Fabrication

Points + Lines + Polygons  OSM Features Interactive Online Map - 

    NOTE  : WORK IN PROGRESS - Will be revised with the thematic variable describing  more in detail group of facilities + adding Water facilities  

Thematic Variable = Infrastructures, Mining, Fabrication
The file contains Industrial facilities, Mining, quarry, pipelines, Power lines and stations, communication towers and piers.
The mkeys variable describes which OSM major keys are used to describe the facility.

Variables : 
osmtype(node, way, relation)  geomtype(point, line, polygone) , osm_id, lat, lon,  thematic,  mkeys (osm major keys),
man_made, landuse, power, industrial, embankment, content, product, substance, 
location, name, operator, operator:type,  private, address, addr:housenumber,  addr:street, addr:city,
building, building:levels, building:material, building:condition,  building:status, note,  layer, other_tags

name, name:en, name:fr, operator, operator:type, natural, aerialway, aerodrome, aeroway, runway, taxiway, apron, helipad, heliport, iata, icao, public_transport, rail, railway, ref, route, network, station, service, shelter, harbor, bus, highway, bridge, tunnel, landuse, surface, building, building:levels, building:material, building:condition, building:status, building:min_level, address, addr:housenumber, addr:street, addr:city, layer, private, source_ref, source, source:name, source:date, source:data, source_ref:1, survey:date, timezone, toilets, type, voltage, weelchair, wikidata, wikipedia

OSM Filter

    OSM features :
    thematic in (   'Infrastructures, Mining, Fabrication');


Tags: facilities-infrastructure, geodata
Organization: OpenStreetMap RDC
Democratic Republic of Congo (DRC) Education Facilities (OpenStreetMap Export)

République démocratique du Congo (RDC), Établissements éducatifs

Points + Polygons : Interactive  Online Map and Tabular spreadsheet (For multipolygons lat,lon=centroid)

* First Column *Thematic* describes line Feature content : school, college, university
* Objets Institutions de la santé (écoles, universités, collèges)

Columns :  thematic, osmtyhpe, lat (latitude),  lon (longitude),  osm_id (unique point id),  [amenity](http://wiki.openstreetmap.org/wiki/amenity),  [school:FR](http://wiki.openstreetmap.org/wiki/school:FR), [name](http://wiki.openstreetmap.org/wiki/name),  [name:fr](http://wiki.openstreetmap.org/wiki/name:fr),  [name:en](http://wiki.openstreetmap.org/wiki/name:en),  [operator:type](http://wiki.openstreetmap.org/wiki/operator:type),  [addr:housenumber](http://wiki.openstreetmap.org/wiki/addr:housenumber),  [addr:street](http://wiki.openstreetmap.org/wiki/addr:street),  [addr:city](http://wiki.openstreetmap.org/wiki/addr:city),  [phone](http://wiki.openstreetmap.org/wiki/phone),  [building](http://wiki.openstreetmap.org/wiki/building), [source](http://wiki.openstreetmap.org/wiki/source), [source:data](http://wiki.openstreetmap.org/wiki/source:data), [source:date](http://wiki.openstreetmap.org/wiki/source:date), [source:name](http://wiki.openstreetmap.org/wiki/source:name), [source_ref](http://wiki.openstreetmap.org/wiki/source_ref), [survey:date](http://wiki.openstreetmap.org/wiki/survey:date), [note](http://wiki.openstreetmap.org/wiki/note), [attribution](http://wiki.openstreetmap.org/wiki/attribution)

* [OpenStreetMap data extraction from](https://download.geofabrik.de)
* [ETL2HDX Procedures](https://github.com/opendatalabrdc/OSM_Exports)


Tags: education, education facilities-schools, facilities-infrastructure, geodata
Organization: OpenStreetMap RDC
Virgin Islands, British - Requirements and Funding Data

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](https://fts.unocha.org/), is encoded as utf-8 and the second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Tags: funding, hxl
Organization: OCHA Financial Tracking System (FTS)
Tokelau - Requirements and Funding Data

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](https://fts.unocha.org/), is encoded as utf-8 and the second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Tags: covid-19, funding, hxl
Organization: OCHA Financial Tracking System (FTS)
Sint Maarten (Dutch part) - Requirements and Funding Data

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](https://fts.unocha.org/), is encoded as utf-8 and the second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Tags: funding, hxl
Organization: OCHA Financial Tracking System (FTS)
Burundi - Ngozi Province: Operational Presence

This dataset contains Who, What, Where and When (4W) data for the province of Ngozi in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Tags: hxl, operational partners, operational presence, who is doing what and where-3w-4w-5w
Organization: Fields Data
Saint Helena, Ascension and Tristan da Cunha - Requirements and Funding Data

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](https://fts.unocha.org/), is encoded as utf-8 and the second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Tags: funding, hxl
Organization: OCHA Financial Tracking System (FTS)