AndreaBozzo/ceres-open-data-index huggingface dataset:
IGAD Regional Focus of the 2024 Global Report on Food Crises

The IGAD Regional Focus of the 2024 Global Report on Food Crises is the result of a collaboration between the Food Security Information Network (FSIN)  and the Intergovernmental Authority on Development (IGAD), a regional economic community that forms one of the building blocks of the African Union. It provides a comprehensive assessment of acute food insecurity and malnutrition in the eight IGAD countries as well as trends over time, key drivers and populations of highest concern based on a rigorous methodology. It also presents available data and information on forcibly displaced populations and includes a spotlight on the conflict in Sudan and a focus on pastoralism, deep-diving into important issues for the region.

Tags: eastern africa, food security, malnutrition, nutrition
Organization: Food Security Information Network
Colombia: Índice Dptal y Mncpal en Salud en el Contexto del Fenómeno de La Niña (2024-II)

En este ejercicio se llevó a cabo un análisis de componentes principales (PCA, por sus siglas en inglés) con el objetivo de realizar una priorización geográfica a nivel departamental y municipal en Colombia. Se utilizaron variables relacionadas con la salud, el clima y el contexto socio-demográfico para identificar y clasificar áreas prioritarias que podrían verse afectadas por fenómenos climáticos como lluvias, deslizamientos, vendavales y avenidas torrenciales. Esta priorización busca facilitar la toma de decisiones en el marco del posible Fenómeno de La Niña, con un enfoque particular en la capacidad de respuesta sanitaria de las regiones priorizadas.

Tags: climate-weather, health
Organization: 3iS
Colombia - Tasas de fecundidad específica 10-14 y 15-19 años municipal 2015-2024

Esta base de datos ofrece un registro exhaustivo de las tasas de fecundidad específica para adolescentes de 10 a 14 años y de 15 a 19 años en los municipios de Colombia, abarcando el periodo de 2015 a 2024. Esta información es crucial para entender las dinámicas de la salud reproductiva en adolescentes y los desafíos asociados al embarazo en edades tempranas en diferentes regiones del país.

La relevancia de esta base de datos radica en su capacidad para apoyar el desarrollo de políticas públicas dirigidas a la prevención del embarazo adolescente, un tema de gran importancia tanto para la salud pública como para el bienestar social. Además, los datos pueden servir como insumo para investigaciones académicas, análisis sociodemográficos, y la planificación de programas educativos y de salud que busquen reducir las tasas de embarazo adolescente y mejorar las oportunidades para las jóvenes en Colombia.

Al brindar un desglose detallado a nivel municipal, esta base de datos permite identificar áreas de mayor vulnerabilidad y necesidades específicas, facilitando intervenciones más precisas y efectivas. La información contenida también puede ser utilizada en modelos predictivos y estudios longitudinales que exploren las tendencias y factores que inciden en la fecundidad adolescente en el país.

Organization: 3iS
Colombia - Precipitaciones y Eventos de Inundaciones - 1990 - 2020

Esta base de datos compila información detallada sobre los eventos de inundaciones ocurridos en Colombia entre 1990 y 2020, enriquecida con datos de precipitación asociados a cada evento. La precipitación diaria se ha obtenido de la fuente CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), una de las referencias más confiables para datos climáticos a nivel global.

La integración de datos de inundaciones con información precisa de precipitación permite un análisis profundo de los patrones asociados al fenómeno de La Niña, un evento climático que ha tenido un impacto significativo en la región. Esta base de datos es una herramienta invaluable para quienes están involucrados en la investigación y modelado de fenómenos climáticos, así como en la planificación y prevención de desastres.

Además de su utilidad para estudios históricos, esta base de datos puede servir como insumo en modelos predictivos para anticipar futuros eventos relacionados con La Niña, contribuyendo a la toma de decisiones informadas y a la implementación de medidas de mitigación en sectores vulnerables.

Organization: 3iS
Colombia - The Great Integrated Household Survey (GEIH) 2023 - 3iS

La Gran Encuesta Integrada de Hogares (GEIH) 2023 es una de las fuentes de datos más completas y relevantes para el análisis socioeconómico en Colombia. Esta base de datos, desarrollada por el Departamento Administrativo Nacional de Estadística (DANE), ofrece una visión detallada y actualizada sobre las dinámicas del mercado laboral y los ingresos de los hogares en el país.

Con una cobertura anual que incluye aproximadamente 315,000 hogares a nivel nacional, la GEIH permite realizar análisis profundos y diversos que pueden informar tanto políticas públicas como investigaciones en áreas clave como la pobreza, el empleo, y la distribución de ingresos. Los datos recolectados permiten caracterizar a la población según sexo, edad, nivel educativo, afiliación al sistema de seguridad social, y otros indicadores demográficos y socioeconómicos. Además, se abordan temas cruciales como las condiciones de empleo, desempleo, y la población fuera de la fuerza de trabajo.

Esta base de datos es esencial no solo para análisis humanitarios, como la identificación de poblaciones vulnerables y la evaluación de la pobreza monetaria, sino también para estudios más amplios que buscan entender las tendencias económicas y sociales en Colombia.

Tags: livelihoods, socioeconomics, survey
Organization: 3iS
Colombia - IPC, Inflación y Abastecimiento

Esta base de datos proporciona información crucial sobre los precios, el Índice de Precios al Consumidor (IPC), la inflación y el abastecimiento de bienes en Colombia durante el año 2023. Desarrollada para ofrecer un análisis detallado de la economía del país, esta fuente de datos es indispensable para comprender las dinámicas inflacionarias, el costo de vida, y las tendencias de mercado.

La relevancia de esta base de datos radica en su capacidad para apoyar decisiones de política económica, planificación empresarial y análisis humanitarios, especialmente en contextos donde los cambios en los precios y la inflación afectan directamente el bienestar de la población. Además, los datos de abastecimiento ofrecen una visión integral sobre la disponibilidad de productos básicos en distintas regiones del país, lo que es fundamental para estudios sobre seguridad alimentaria y economía regional.

Esta base de datos es esencial para analistas y tomadores de decisiones que necesitan un entendimiento claro de las tendencias de precios y su impacto en el poder adquisitivo de los hogares colombianos.

Tags: economics
Organization: 3iS
GRID3 COD - Settlement Extents v3.1

The GRID3 COD - Settlement Extents v3.1 consists of a geographic representation of settlements in the Democratic Republic of the Congo, in two
forms: 1) settlement polygons, and 2) spatial points depicting the centroids of settled grid cells at 3-arc seconds (or ~100 meters) contained within
settlement polygons. Both layers include attributes as described in the codebooks. Data inputs and methodology to derive the 2 layers are described in this document.

The current version supersedes GRID3 COD - Settlement Extents v3.0; the following changes were made:
● Corrections on imputed values for building count and building areas.
● Corrections on probability values from field-collected data
● Edits in the Data Release Notes


Tags: geodata, populated places-settlements
Organization: GRID3
Philippines - Population Statistics (COD-PS)

  The Common Operational Dataset on Population Statistics (COD-PS) is a set of up-to-date (i.e. reference date within the last year) subnational population projections, disaggregated by sex and age (standard 5-year demographic age groups) to the lowest geographic administrative level possible. The COD-PS is constructed according to “best available humanitarian data” standards and designed to support short-term humanitarian decision-making. The COD-PS is one of 3 mandatory common operational datasets, alongside common operational datasets on administrative boundaries and humanitarian caseloads, that are required in all operational humanitarian response countries by the United Nations. Ensuring the availability, quality and usability of the COD-PS is an institutional responsibility of the United Nations Population Fund (UNFPA) within the United Nations humanitarian system.

Organization: UNFPA Philippines
GRID3 COD - Schools v1.0

The GRID3 COD - Schools v1.0 dataset consists of school points with names and attributes in the following provinces of the Democratic Republic of the Congo (COD):

Province group 1: Haut-Katanga, Kasaï, Kasaï-Oriental, Kinshasa, and Lomami

The GRID3 COD - Schools v1.0 dataset is one of six datasets (Settlements, Health Facilities, Health Area Boundaries, Health Zone Boundaries, Schools and Religious Centers) included in this Version 1.0 release.

This operational dataset has not been fully validated by government officials or ministries.

The data are available for download in OGC Geopackage format.

NOTE: This data release supersedes the dataset "GRID3 DRC Schools - Haut-Katanga, Kasaï, Kasaï-Oriental, Kinshasa, and Lomami Provinces, Version 01".

Funding for the development and dissemination of this dataset was provided by GRID3 Inc, the Bill & Melinda Gates Foundation, and Gavi, the Vaccine Alliance.


Tags: education facilities-schools
Organization: GRID3
GRID3 COD - Religious Centers v1.0

The GRID3 COD - Religious Centers v1.0 dataset consists of religious center points with names and attributes in the following provinces of the Democratic Republic of the Congo (COD):

Province group 1: Haut-Katanga, Kasaï, Kasaï-Oriental, Kinshasa, and Lomami

The GRID3 COD - Religious Centers v1.0 dataset is one of six datasets (Settlements, Health Facilities, Health Area Boundaries, Health Zone Boundaries, Schools and Religious Centers) included in this Version 1.0 release.

This operational dataset has not been fully validated by government officials or ministries.

The data are available for download in OGC Geopackage format. .

NOTE: This data release supersedes the dataset “GRID3 DRC Religious Centers - Haut-Katanga, Kasaï, Kasaï-Oriental, Kinshasa, and Lomami Provinces, Version 01”.

Funding for the development and dissemination of this dataset was provided by GRID3 Inc, the Bill & Melinda Gates Foundation, and Gavi, the Vaccine Alliance.


Tags: points of interest-poi
Organization: GRID3
Ethiopia: IRC Ethiopia Direct Project Intervention Woredas -Jan 2024 By Sectors and Donors

IRC  Directly implemented Projects intervened in the different part of the nation and refugee camps for the  period of January 2024. The data shows the interventions by detail activities, Sectors and Donors. 
IRC’s GIS unit created this database to allow for a more sustained approach to producing relevant mapping products and geospatial analysis for undergoing humanitarian responses and will fill gaps in information sharing and management between Donors (BHA/USAID, ECHO, Irish AID, Charity; Water, EHF, UNHCR & others). IRC’s GIS services provides information and analysis required to monitor the evolution of project / program results and to track project and program impacts across geographic locations. This dataset contains boundaries of Ethiopian administrative Woredas which are roughly consistent with the actual & updated administrative boundaries for the year 2024. The Woreda boundary contains attribute data (, activities, Sector Donors Fields) for IRC Ethiopia for IRC Direct Projects- January 2024

Tags: health, nutrition, protection, water sanitation and hygiene-wash
Organization: International Rescue Committee Ethiopia
Lebanon: Multi-Sector Needs Assessment - Lebanese HHs

The multi-sector needs assessment (MSNA) was the third assessment conducted by REACH in Lebanon. The main objective of this MSNA is to provide a comprehensive overview of the multi-sectoral needs and humanitarian conditions across all governorates for three population groups in Lebanon: Lebanese, migrants, and Palestine Refugees in Lebanon (PRL) residing in camps. This is the summary dataset. The full dataset can be requested on the "[Multi-Sector Needs Assessment Lebanon - Lebanese HHs](https://data.humdata.org/dataset/multi-sector-needs-assessment-lebanese-hhs)" page.

Tags: education, food security, health, livelihoods, needs assessment, shelter, water sanitation and hygiene-wash
Organization: REACH Initiative
Afghanistan - Who does What Where (January to March 2024)

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). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed [here](https://response.reliefweb.int/afghanistan/who-does-what-where-3w?_gl=1*g902oc*_ga*MTYyOTk1NDg4OC4xNjc2NTM2NTk3*_ga_E60ZNX2F68*MTcwNDE3NzMzMC4xNDAuMS4xNzA0MTg0MTQ5LjU4LjAuMA). 

Tags: hxl, operational capacity, operational presence, who is doing what and where-3w-4w-5w
Organization: OCHA Afghanistan
Kenya Children’s Climate Risk Index-Disaster Risk Model (CCRI-DRM) subnational risk assessment. A composite index that includes risk data on child exposure and vulnerability to hazards, shocks and stresses.

This is a Children’s Climate and Disaster Risk model (CCRI-DRM) Kenya model created by UNICEF. The Kenyan CCRI-DRM subnational risk assessment initiative is a joint effort between UNICEF, Kenya national authorities and stakeholders, and international partners to enrich, create and use child-inclusive climate and disaster risk data. The initiative aims to improve the understanding and management of risks that children, young people, families and their communities face from natural hazards, climate and environmental hazards, shocks and stresses, and disasters.
The approach and processes for subnational adaptations of the Index for Risk Management (INFORM) model provide the main reference for the development of the subnational CCRI-DRM. This analysis is composed of two main pillars that are indexed and combined into a final risk score:
- Pillar 1: Exposure to Shocks & Stressors
- Pillar 2: Child Vulnerability
Reference layers have been added as incomplete but vital datasets that can improve contextual understanding of the lives of children where data is available.
Please note that the number values of the shock exposure indicators included in the first pillar of the model are ESTIMATES of children under 18 exposed to the different types of shocks and stresses. These estimates are data derived from a combination of hazard and population layers and should not be treated as official or specific numbers.
The Kenya CCRI-DRM model can also be found on the GeoSight dashboard, which is a geospatial platform intended to provide an easy-to-use and open-source cartographic visualization of the CCRI-DRM Kenya model created by UNICEF. (Link: https://geosight.unicef.org/project/kenya-ccri-drm-dashboard).
The Model could also be found on the Kenya Ministry Of Environment, Climate Change & Forestry. The link: https://environment.go.ke/ccri-drm-portal/. 

Organization: INFORM
Philippines - Open, validated health, climate, environment and socioeconomic data

The **Project Climate Change, Health, and Artificial Intelligence (Project CCHAIN)** dataset is a validated, open-sourced linked dataset containing 20 years (2003-2022) of climate, environmental, socioeconomic, and health dimensions at the barangay (village) level across twelve Philippine cities (Dagupan, Palayan, Navotas, Mandaluyong, Muntinlupa, Legazpi, Iloilo, Mandaue, Tacloban, Zamboanga, Cagayan de Oro, Davao).  

The full documentation can be accessed [here](https://thinkingmachines.github.io/project-cchain).

The tables are designed in a way that users can **choose variables that are most relevant** to their focus city and use case, and **link these variables** to form a single dataset by merging using standard geography codes and calendar dates. This can be done using the provided [linking notebook](https://drive.google.com/drive/folders/123MrMXSVT6149MvJ8hGoDgN1s-YlQoH_), or offline using the user's own code.


Here are some tips on how make most use of this dataset:

- **Focus on one location.** Starting with a detailed analysis of one location allows for a better understanding of the local dynamics, which may differ across locations.

- **Choose one health data source.** Pick one of either a central or local data source. Using two different data health sources is not advised because it will lead to double/overcounting of disease cases.

- **Do not use all variables at once- do a literature review first to identify possible key variables**.  to identify possible key variables. More often than not, using all variables is not necessary and may even yield subpar results.

- **Check data availability** on your focus location and make sure they fit the requirements of your study.

This dataset also includes **household surveys tables** (see schema [here](https://dbdocs.io/lacuna-fund-project-team/Project-CCHAIN?table=informal_settlement_community_surveys_v1) and [here](https://dbdocs.io/lacuna-fund-project-team/Project-CCHAIN?table=informal_settlement_community_surveys_v2))  done on partner informal settlement communities in the cities of Muntinlupa, Davao, Iloilo, and Mandaue and administered on various dates up to 2024. Due to the sensitive nature of surveys and the vulnerability of the subjects involved, requests for access must be submitted for review and approval by the Philippine Action for Community-Led Shelter Initiatives, Inc. (PACSII).
To submit a request, please use [this form](https://forms.gle/Z9rfuJFENAQfbKeM6).



Tags: climate hazards, governance and civil society, health, socioeconomics
Organization: Thinking Machines Data Science
Sudan: Health and Nutrition Assessment

An assessment done between January and February 2024 in Northern state - Wadi Halfa for Health and Nutrition highlighted that Overcrowding, limited-service offerings, and a high rate of disease among IDPs have severely strained local healthcare systems. Efforts should focus on collaborating with local organizations to bolster training programs for medical staff, address supply shortages, and develop both disease control programs and emergency response capabilities. In addition to that, Local CMAM programs are limited in both coverage and resource availability. Their severely depleted stockpiles and limited reach greatly hinder effective malnutrition management. Efforts should focus on expanding coverage and capacity, ensuring the consistent availability of critical supplies, establishing relevant data-collection systems, and promoting cultural awareness and acceptance of healthy nutrition practices.

Tags: affected population, health facilities, internally displaced persons-idp, malnutrition, nutrition, severe acute malnutrition-sam
Organization: Sudanese American Physicians Association
Bahrain - 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)
Philippines Subnational Population and Housing Data Tables with Administrative Boundaries, Based on National Censuses

The geodatabase contains boundaries for the national and first-, second-, third-, and fourth-order administrative divisions, aligned to the Large Scale International Boundaries dataset from the U.S. Department of State. The feature classes are suitable for linking to the attribute data provided.

The tabular data contain total population for 2020 (census), as well as five-year age group and sex, and information relating to ethnicity, citizenship, religion, households, housing units, and overseas workers.

Organization: U.S. Census Bureau
Bangladesh : Population and Housing Census Dataset

Population and housing census dataset as of Census 2022 by Bangladesh Bureau of  Statistics (BBS). The population and Housing census  has two parts that include-
(1) The Population Census, which provides socio-economic and demographic information on every person living in a country at a point in time, down to the smallest geographical unit. 
(2) The Housing Census, which provides data on all dwelling units prevailing in a country, their conditions, and facilities available, down to the smallest geographical unit.
Although it covered smallest geographical unit of Bangladesh; till date it's available up to District (Admin 02) level.  
All information contains this dataset is collated from Final Population and Housing census report published by BBS to ensure meaningful access. For more detail information and further query/questions it’s recommended to check the full report.  

Please click in Below link to access the full report-                                                                               
https://bbs.portal.gov.bd/sites/default/files/files/bbs.portal.gov.bd/page/b343a8b4_956b_45ca_872f_4cf9b2f1a6e0/2024-01-31-15-51-b53c55dd692233ae401ba013060b9cbb.pdf 

Tags: baseline population, demographics, health, socioeconomics, water sanitation and hygiene-wash
Organization: UN in Bangladesh
Afghanistan - Who does What Where (October to December 2023)

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). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed [here](https://response.reliefweb.int/afghanistan/who-does-what-where-3w?_gl=1*g902oc*_ga*MTYyOTk1NDg4OC4xNjc2NTM2NTk3*_ga_E60ZNX2F68*MTcwNDE3NzMzMC4xNDAuMS4xNzA0MTg0MTQ5LjU4LjAuMA). 

Tags: hxl, operational capacity, operational presence, who is doing what and where-3w-4w-5w
Organization: OCHA Afghanistan
Storm Europe: Data for Disaster Risk Assessment (Ukraine Only)

European winter storm [gust in meters per second] footprints for Ukraine. Data presented here is historical, observed data based on the Copernicus WISC project event footprints 1940-2014. Further modelled data are available from the CLIMADA Data API these include 50 probabilistic events per original event, at 4 kilometers resolution. Data available for European countries, and corresponds to the storm hazard today. See key reference Röösli et al. 2021. 2. Winter storm hazards generated from ERA5 (1980-2010) and CMIP6 models (1980-2010 and 2070-2100). In that second case, only Europe wide datasets are available, with lower resolution (depending on the GCM or ERA5 model). See Severino et al. 2023.

Tags: geodata, hazards and risk, humanitarian response plan-hrp, hxl
Organization: ETH Zürich - Weather and Climate Risks