AndreaBozzo/ceres-open-data-index huggingface dataset:
Indonesia: Earthquake - 6.9M - Nov 2023

**ADAM ID: eq\_us7000l9h2**  Magnitude 6.9 earthquake at 10.0 depth occurred on Nov 08 2023 in Banda Sea. It impacted 736 people. The epicentre was at latitude -6.3878 longitude 129.7638.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Indonesia: Earthquake - 6.7M - Nov 2023

**ADAM ID: eq\_us7000l9ku**  Magnitude 6.7 earthquake at 10.0 depth occurred on Nov 08 2023 in Banda Sea. It impacted 0 people. The epicentre was at latitude -6.1455 longitude 129.9137.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Indonesia: Earthquake - 6.0M - Nov 2023

**ADAM ID: eq\_us7000l85t**  Magnitude 6.0 earthquake at 27.656 depth occurred on Nov 01 2023 in 19km NW of Oelamasi. It impacted 182248 people. The epicentre was at latitude -10.0109 longitude 123.7132.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Fiji: Earthquake - 6.4M - Oct 2023

**ADAM ID: eq\_us7000l7qy**  Magnitude 6.4 earthquake at 547.578 depth occurred on Oct 31 2023 in 19km SE of Mago Island. It impacted 1953 people. The epicentre was at latitude -17.538 longitude -179.0119.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Vanuatu: Earthquake - 6.0M - Oct 2023

**ADAM ID: eq\_us7000l7b6**  Magnitude 6.0 earthquake at 79.858 depth occurred on Oct 29 2023 in 51km W of Imanaka. It impacted 10 people. The epicentre was at latitude -19.415 longitude 168.7704.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Bangladesh: Cyclone - Category 1 - Oct 2023

**ADAM ID: 1001029\_6**  Cyclone (category 1) during the period Oct 23 2023-Oct 24 2023 in Bangladesh, India, Myanmar. It impacted 3946522 people.

Tags: affected population, cyclones-hurricanes-typhoons, geodata
Organization: WFP Automated Disaster Analysis & Mapping
Vanuatu: Cyclone - Tropical depression - Oct 2023

**ADAM ID: 1001027\_8**  Cyclone (tropical depression) during the period Oct 21 2023-Oct 26 2023 in Vanuatu, Solomon Islands. It impacted 49248 people.

Tags: affected population, cyclones-hurricanes-typhoons, geodata
Organization: WFP Automated Disaster Analysis & Mapping
Antigua and Barbuda: Cyclone - Tropical storm - Oct 2023

**ADAM ID: 1001025\_12**  Cyclone (tropical storm) during the period Oct 18 2023-Oct 29 2023 in Virgin Islands  British, Antigua and Barbuda, Montserrat, Guadeloupe, Bermuda, Barbados, Dominica. It impacted 35248 people.

Tags: affected population, cyclones-hurricanes-typhoons, geodata
Organization: WFP Automated Disaster Analysis & Mapping
Afghanistan: Earthquake - 6.3M - Oct 2023

**ADAM ID: eq\_us6000lfn5**  Magnitude 6.3 earthquake at 8.219 depth occurred on Oct 15 2023 in 22km SW of Koshk. It impacted 1185926 people. The epicentre was at latitude 34.6171 longitude 62.0517.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Afghanistan: Earthquake - 6.3M - Oct 2023

**ADAM ID: eq\_us6000len8**  Magnitude 6.3 earthquake at 10.0 depth occurred on Oct 11 2023 in 25km SW of Koshk. It impacted 996999 people. The epicentre was at latitude 34.5798 longitude 62.0717.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Argentina: Earthquake - 6.0M - Oct 2023

**ADAM ID: eq\_us6000legn**  Magnitude 6.0 earthquake at 247.255 depth occurred on Oct 10 2023 in 57 km WSW of Abra Pampa, Argentina. It impacted 3132 people. The epicentre was at latitude -22.8844 longitude -66.2242.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Afghanistan: Earthquake - 6.2M - Oct 2023

**ADAM ID: eq\_us6000ldpg**  Magnitude 6.2 earthquake at 10.0 depth occurred on Oct 07 2023 in 30km SW of Koshk. It impacted 983360 people. The epicentre was at latitude 34.6259 longitude 61.926.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Vanuatu: Earthquake - 6.0M - Sep 2023

**ADAM ID: eq\_us6000lbdi**  Magnitude 6.0 earthquake at 125.735 depth occurred on Sep 28 2023 in 19km SW of Loone. It impacted 12399 people. The epicentre was at latitude -15.5748 longitude 167.712.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Vanuatu: Earthquake - 6.1M - Sep 2023

**ADAM ID: eq\_us7000kx7j**  Magnitude 6.1 earthquake at 188.419 depth occurred on Sep 21 2023 in 31km W of Vétuboso. It impacted 4814 people. The epicentre was at latitude -14.0039 longitude 167.1898.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Philippines: Earthquake - 6.3M - Sep 2023

**ADAM ID: eq\_us7000kv90**  Magnitude 6.3 earthquake at 41.281 depth occurred on Sep 12 2023 in 29km W of Calayan. It impacted 11275 people. The epicentre was at latitude 19.2687 longitude 121.2178.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Indonesia: Earthquake - 6.0M - Sep 2023

**ADAM ID: eq\_us7000kv0k**  Magnitude 6.0 earthquake at 162.544 depth occurred on Sep 11 2023 in 0km SE of Akelamo. It impacted 506970 people. The epicentre was at latitude 1.1251 longitude 127.4792.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Indonesia: Earthquake - 6.0M - Sep 2023

**ADAM ID: eq\_us7000kulj**  Magnitude 6.0 earthquake at 9.854 depth occurred on Sep 09 2023 in 71km SW of Tinombo. It impacted 157836 people. The epicentre was at latitude 0.0054 longitude 119.7709.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Morocco: Earthquake - 6.8M - Sep 2023

**ADAM ID: eq\_us7000kufc**  Magnitude 6.8 earthquake at 18.535 depth occurred on Sep 08 2023 in 26km SW of Amizmiz. It impacted 380662 people. The epicentre was at latitude 31.1095 longitude -8.4404.

Tags: affected population, earthquake-tsunami, morocco-earthquake
Organization: WFP Automated Disaster Analysis & Mapping
Russian Federation: Earthquake - 6.1M - Sep 2023

**ADAM ID: eq\_us7000ksrl**  Magnitude 6.1 earthquake at 141.248 depth occurred on Sep 01 2023 in 13 km ENE of Severo-Kuril’sk, Russia. It impacted 2019 people. The epicentre was at latitude 50.7026 longitude 156.316.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Aruba: Cyclone - Tropical storm - Jun 2023

**ADAM ID: 1000977\_4**  Cyclone (tropical storm) during the period Jun 19 2023-Jun 24 2023 in Colombia, Venezuela, Aruba, Netherlands Antilles, Grenada, Saint Vincent and the Grenadines, Martinique, Saint Lucia, Barbados. It impacted 0 people.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: WFP Automated Disaster Analysis & Mapping
Vanuatu: Earthquake - 6.5M - Aug 2023

**ADAM ID: eq\_us7000knq0**  Magnitude 6.5 earthquake at 192.955 depth occurred on Aug 16 2023 in 33km W of Vétuboso. It impacted 3825 people. The epicentre was at latitude -13.8814 longitude 167.1582.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Haiti: Cyclone - Category 1 - Sep 2023

**ADAM ID: 1000996\_7**  Cyclone (category 1) during the period Aug 20 2023-Sep 01 2023 in Bermuda, Turks and Caicos Islands, Dominican Republic, Haiti. It impacted 0 people.

Tags: affected population, cyclones-hurricanes-typhoons, geodata
Organization: WFP Automated Disaster Analysis & Mapping
Argentina: Earthquake - 6.2M - Aug 2023

**ADAM ID: eq\_us7000kqbl**  Magnitude 6.2 earthquake at 568.794 depth occurred on Aug 23 2023 in Santiago del Estero, Argentina. It impacted 11956 people. The epicentre was at latitude -26.9212 longitude -63.3389.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping
Papua New Guinea - Education Facilities

This spatial dataset is a national dataset of education facilities across Papua New Guinea. Edits were made to the original dataset to include provincial, district and LLG attribute information.

The original dataset was provided to the UN-OCHA Regional Office for the Asia-Pacific by the National Statistics Office. 

Tags: education, education facilities-schools, geodata
Organization: OCHA Regional Office for Asia and the Pacific (ROAP)
Papua New Guinea - Health Facilities

This spatial dataset of health facilities is a national dataset of 705 facilities across Papua New Guinea. Edits were made to the original dataset to include provincial, district and LLG attribute information.

The original dataset was provided to  the UN-OCHA Regional Office for the Asia-Pacific by the National Statistics Office. 

Tags: geodata, health
Organization: OCHA Regional Office for Asia and the Pacific (ROAP)
UN Collective Results on the COVID-19 Socioeconomic Response in 2020

Under the leadership of UNDP and DCO, an inter-agency task team developed the UN framework for the immediate socio-economic response to COVID-19 (adopted in April 2020) to govern its response over 12 to 18 months. To measure the UN’s support to the socio-economic response and recovery, UN entities developed a simple monitoring framework with 18 programmatic indicators (endorsed by the UNSDG in July 2020). Lead entities – based on their mandate and comparative advantage – were nominated to lead the development of methodological notes for each indicator and lead the collection of data at the country level. These lead entities reported through the Office of the Resident Coordinators the collective UN results on a quarterly basis through UN Info. All 2020 data was reported by March 2021. This is the UN development system’s first comprehensive attempt at measuring its collective programming contribution and results. 

These programmatic indicators enabled the UN system to monitor the progress and achievements of UNCT’s collective actions in socio-economic response. In support of the Secretary-General’s call for a "… single, consolidated dashboard to provide up-to-date visibility on [COVID-19] activities and progress across all pillars” all data was published in real time on the COVID-19 data portal, hosted by DCO. The data is disaggregated by geography (rural/urban), sex, age group and at-risk populations -- to measure system-wide results on the socio-economic response to the pandemic, in order to ensure UNDS accountability and transparency for results.  

Tags: covid-19, health, indicators, socioeconomics
Organization: United Nations Development Coordination Office (inactive)
Global LSIB Polygons Detailed

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data.  The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data.  The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology:
Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the  island data provided by the State Department to polygons.  The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources.
The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset.  The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines.  The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles.  Once all topological errors were fixed, the lines were converted to polygons.  Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset.  The polygons were then dissolved by country name.
Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly.  Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS.  The same procedure was performed for every individual island contained in the islands from other USG sources.  
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset.  The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.


Tags: administrative boundaries-divisions, geodata
Organization: U.S. Department of State - Humanitarian Information Unit (inactive)
Democratic People's Republic of Korea : Humanitarian Needs Overview

This dataset contains the affected populations, people in need and targeted populations by sector and disaggregated by sex and age. The dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners.

Tags: humanitarian needs overview-hno, hxl, people in need-pin
Organization: OCHA Regional Office for Asia and the Pacific (ROAP)
Indonesia Measles Outbreaks, 2015-2017

This dataset contains the number of a measles outbreak, cases and people died by province (admin 1), 2015-2017. The data was extracted from the Indonesia Health Profile publications (2015-2017) published by the Ministry of Health. The publication is available in PDF format: http://www.pusdatin.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html

Tags: disease, epidemics-outbreaks, health
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Indonesia Rabies cases in human, 2015-2017

This dataset contains the number of a rabies cases in human by province (admin 1), 2015-2017. There are 3 different data available: Rabies infectious animal bite cases (GHPR); Rabies cases with the Anti rabies vaccines (VAR) and Positive rabies and death cases based on the LYSSA test (LYSSA). The data was extracted from the Indonesia Health Profile publications (2015-2017) published by the Ministry of Health. The publication is available in PDF format: http://www.pusdatin.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html

Tags: disease, health
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Indonesia Dengue Fever cases by province, 2015-2017

This dataset contains the number of Dengue Fever cases, incidence rape per 100,000 population, people death and case fatality rate (CFR) by province (admin 1), 2015-2017. The data was extracted from the Indonesia Health Profile publications (2015-2017) published by the Ministry of Health. The publication is available in PDF format: http://www.pusdatin.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html

Tags: disease, fatalities, health
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Indonesia Leptospirosis cases by province, 2015-2017

This dataset contains the number of Leptospirosis cases, people death and case fatality rate (CFR) by province (admin 1), 2015-2017. Leptospirosis is an infectious disease of humans and animals that is caused by pathogenic spirochetes of the genus Leptospira. The data was extracted from the Indonesia Health Profile publications (2017) published by the Ministry of Health. The publication is available in PDF format: http://www.pusdatin.kemkes.go.id/download.php?file=download/pusdatin/profil-kesehatan-indonesia/Profil-Kesehatan-Indonesia-tahun-2017.pdf (Annex 6.27)

Tags: disease, health
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Indonesia Diarrhea cases by province, 2015-2016

This dataset contains the number of Diarrhea cases, people death and case fatality rate (CFR) by province (admin 1), 2015-2016. The data was extracted from the Indonesia Health Profile publications (2015-2017) published by the Ministry of Health. The publication is available in PDF format: http://www.pusdatin.kemkes.go.id/folder/view/01/structure-publikasi-pusdatin-profil-kesehatan.html

Tags: health
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Cyclone Idai - Aerial Survey

This data relates to an aerial survey conducted of Mozambique from the 24-29 March 2019 following tropical cyclone Idai. 
Data was collected by INGC (National Disaster Management Authority of Mozambique), IFRC, UNDAC, MSF, DFID, Save the Children, and MapAction.
Enumerators flew in squirrel helicopters over priority areas (usually at 500 feet) and some fixed wing aircraft for confirmation on outlying areas.
Data contains location, points of interest, severity and estimated population.   
The survey used Kobo for data collection.  Processing was conducted in R and then imported into ArcGIS for final map products.
Data is presented in .xlsx (with and without hxl tags) and .geojson formats.

Tags: cyclones-hurricanes-typhoons, geodata, hxl, logistics, severity
Organization: International Federation of Red Cross and Red Crescent Societies (IFRC)
Maldives - Critical infrastructures

These data-sets include Maldives Critical infrastructures data such as Transportation (Harbour, Airport, Seaplane), Points of interest  (Bank_branch,	ATM, Stadium), Medical Facilities (Regional hospital, Atoll Island Hospital, Health centers), Government Buildings (Police,	MNDF,	Atoll_council_office, Island council office,	Court), Education Facilities (Pre-school, Primary and Secondary School, Colleges, Universities, Campus) and Mosque.  Datasets contains National and sub national levels including p-codes. The data were cleaned by the Faculty of Engineering, Science and Technology within the Maldives National University.


Tags: aviation, economics, education, education facilities-schools, facilities-infrastructure, governance and civil society, health, health facilities, ports, transportation
Organization: OCHA Regional Office for Asia and the Pacific (ROAP)
Southern Teknaf Camps - Foodpaths and Roads

Recognizing an information gap within existing road network datasets in the Southern Teknaf camps of Coxs Bazar District (Camps 24-27 and Nayapara Registered Camp), REACH dispached field teams to the area for a roads and paths mapping exercise from November 14-15, 2018. Areas within and on the edges of the camps were divided into grid cells, through which REACH field teams systematically traversed all paths while carrying GPS tracking devices. 

This dataset was then merged with the existing OpenStreetMap (OSM) roads and paths dataset for the area to provide a comprehensive map of roads and footpaths in and around the camps for mapping and spatial analysis purposes. When present in this dataset, OpenStreetMap attributes and identifiers have been preserved.

This exercise was carried out with the support of European Civil Protection and Humanitarian Aid Operations (ECHO)

Tags: geodata, refugee crisis
Organization: REACH Initiative
REACH-UNICEF WASH Household Survey - Monsoon Follow-up

Supported by UNICEF and in collaboration with the Cox's Bazar WASH sector, REACH collected household-level data on key WASH indicators across 33 out of 34 recognised ISCG camps between 14 August and 2 October. Data is representative at camp level at 95 confidence level/10% margin of error, and representative at aggregate level at 95% confidence level/5% margin of error. A total of 3,563 interviews were conducted for this assessment. This file contains the cleaned, anonymised raw dataset. This assessment is a follow-up to an April 2018 baseline, for which the data is available here: https://data.humdata.org/dataset/reach-unicef-wash-household-baseline-survey

Tags: water sanitation and hygiene-wash
Organization: REACH Initiative
Whole of Afghanistan Assessment - Hard to Reach Dataset - August 2018

The overall objective of the Hard to Reach component of the Whole of Afghanistan Assessment was to supplement of the household data collection with an understanding of the multi-sectoral needs faced by those in Hard to Reach areas. This data ought to inform humanitarian programming in these districts and support the identification of nationwide inter-sectoral needs and how they inter-relate across different geographic locations and population groups outlined in the Humaniarian Needs Overview (HNO). 

The Hard to Reach component of the Whole of Afghanistan (WoA) Assessment utilised Mapping Focus Group Discussions for the mapping process and Key Informant Interviews with knowledgeable community members to identify needs and access constraints. This data complemented the household-level data collection component of the Whole of Afghanistan. The presented dataset here includes the key informant responses across the 70 Hard to Reach districts, conducted to capture inter-sectoral needs of displaced and host households in these areas of Afghanistan.

Tags: affected area, displacement, needs assessment
Organization: REACH Initiative
Rapid Needs Assessment Idleb and Surrounding Areas August 2018

Idleb governorate and the surrounding opposition-held areas of western Aleppo and north-western Hama governorates* host one of the largest IDP populations in Syria. Since late 2017, following an escalation of violence in north-western and southern Syria, the region has witnessed the further arrival of a substantial number of IDPs. Large cross-line displacements from Eastern Ghouta, northern Homs and southern Hama, as well as Da'ra and Quneitra governorates, beginning in March 2018, have led to a significant increase of the IDP population in the region and to a growing strain on resources and services in the region. While humanitarian assistance is reaching both IDP and resident populations, major gaps remain. In order to address such gaps, REACH has conducted a needs assessment to inform humanitarian actors of IDP and resident populations' priority needs and to address critical information gaps on the humanitarian conditions of IDPs and residents living in communities in the region. This data set provides the findings from this assessment.

Findings presented in this data set are based on data collected by REACH as part of a needs assessment focused on IDP and resident populations living in communities of Idleb governorate and surrounding areas. Data was collected in 362 communities across 30 sub-districts between 12 and 20 August 2018. Data was collected at the community level, with enumerators interviewing between 2 and 7 key informants (KIs) per community and with KIs selected based on their knowledge of IDP and resident populations in the community and sector-specific expertise. Communities were assessed based on available data on total populations, focusing on those reported to have large IDP and resident populations and those that have received a high number of IDP arrivals in the two months prior to the start of data collection. Collected primary data was further triangulated through available secondary sources.


Tags: demographics, internally displaced persons-idp, livelihoods
Organization: REACH Initiative