This dataset contains Who, What, and Where(3W) data for the Maguindanao Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Negros Occidental Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Negros Oriental Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Northern Samar Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Nueva Ecija Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Occidental Mindoro Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Oriental Mindoro Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Palawan Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Pangasinan Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Samar Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Siquijor Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Southern Leyte Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the National Capital Region in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
The dataset provides names and locations of the certified airfields in Ukraine that are included in the State Registry of Civil Airfields of Ukraine and the Journal of Admission to Operation of Permanent Runway Platforms as of 6 July 2022 according to the State Aviation Administration of Ukraine.
Airfield Certificate issued by the State Aviation Administration confirms the airfield compliance with the requirements of Ukrainian aviation rules. Operation of airfield (helidrome) for the purpose of accomplishment of air transportation and/or aviation works in the absence of the certificate or in case of cancellation of the certificate is forbidden.
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.
Dataset has IDPs, Households, challenges faced by IDPs etc. On 11-13 April, severe flooding and landslides caused by heavy rainfall affected southern and south-eastern South Africa. In response to the need for accurate information on internally displaced persons (IDPs) in South Africa, the International Organization for Migration (IOM) in partnership with the South Africa Red Cross Society and in coordination with provincial and local authorities, deployed teams from June 2022 to conduct baseline assessments at ward level. The project is supporting the Government of South Africa and other humanitarian response partners to conduct IDPs assessments in a systematic way as well as to establish a profile of the IDP population.
DTM location assessment is to collect data on population presence in defined locations identified through the baseline area assessment. The assessment identifies where people are living and informs targets sites for more detailed site assessments.
To get the complete data of the assessment round, kindly download the corresponding Site Assessment data here: https://data.humdata.org/dataset/nigeria-site-assessment-data.
Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease.
In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women.
In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.
For more, please visit: http://globalhealth5050.org/covid19
This data is produced by OCHA Somalia in collaboration with humanitarian partners. It provides information on the worsening drought situation in Somalia in 2022. It indicates the drought response by clusters in this period
Schemas describing the core #HXL hashtags and attributes. Starting with version 1.1, the standards documentation listing #HXL hashtags and attributes at hxlstandard.org is generated directly from this dataset.
See the documentation on the #HXL schema format , and the #HXL Proxy validation service. Note that this is just a generic default schema—you can also create your own, project-specific #HXL schemas.