Loading...
Loading...
Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
15,870 datasets
2000 to 2024 hourly global surface PM2.5 mass concentrations, bias-corrected using a convolutional neural network (CNN) method. The dataset is derived from MERRA-2 data and developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). It includes two parameters: PM2.5 concentration in µg/m³ and a quality flag for each grid point.
AAA-HiTSR is a multimodal time series understanding and reasoning dataset created by author November-Rain. It contains time series data paired with visual representations and natural language instructions, organized into three levels of complexity with corresponding train/test splits. The dataset was last updated on Hugging Face on April 17, 2026.
A NOAA laboratory experiment measured dissolved inorganic carbon, pH, and other variables to study ocean acidification effects on winter flounder. The experiment used a system capable of mimicking estuarine and oceanic conditions by manipulating pCO2, temperature, dissolved oxygen, and salinity. Data collection followed guidelines from the Ocean Carbon and Biogeochemistry Program to ensure accuracy and precision.
PACE OCI Level-2 Regional Ocean Biogeochemical Properties Data provides satellite-derived biogeochemical properties of the upper ocean from the PACE_OCI instrument. The Ocean Color Biogeochemical suite characterizes carbon pools and ecosystem status. Geophysical variables include phytoplankton carbon biomass, chlorophyll-a concentration, and particulate organic carbon.
This dataset tracks humanitarian operational presence (3W) in the Antique Province of the Philippines, detailing organization activities by sector and location. Maintained by Fields Data, the records provide a snapshot of aid distribution and coordination as of March 2026.
Fields Data provides this 3W (Who, What, Where) dataset tracking humanitarian and development activities in Maguindanao Province, Philippines. Updated in March 2026, the data identifies active organizations, their specific sectors of work, and their geographic locations within the province.
Fields Data provides this 3W (Who, What, Where) operational presence data for the Nueva Ecija Province in the Philippines, updated as of March 2026. The dataset tracks organizational activities by sector and location to facilitate humanitarian coordination and resource mapping.
This dataset tracks the 3W (Who, What, Where) operational presence of humanitarian organizations within the Aklan Province of the Philippines. Produced by Fields Data and updated in March 2026, it maps organizational activities across various sectors at the provincial level. The data is formatted using Humanitarian Exchange Language (HXL) standards for interoperability.
This dataset tracks the operational presence of humanitarian organizations across sectors in Negros Oriental Province, Philippines, as of March 2026. Produced by Fields Data, it follows the 'Who, What, Where' (3W) framework to map organizational activity at the province level.
Who, What, and Where (3W) operational presence data for Guimaras Province in the Philippines as of March 2026. Produced by Fields Data, it tracks organizational activities across various sectors at the provincial level to facilitate humanitarian coordination.
Fields Data provides this 3W (Who, What, Where) dataset detailing the operational presence of humanitarian organizations within the Capiz Province of the Philippines. Updated in March 2026, the data maps specific organizations to their respective sectors and geographic locations at the province level.
Fields Data provides this 3W (Who, What, Where) dataset detailing organizational activity across Cebu Province, Philippines, updated in March 2026. It records which organizations are active in specific sectors and locations at the provincial level to facilitate humanitarian coordination.
Operational presence data (Who, What, Where) for six provinces in Burundi identifies organizations and their sectors of activity at the province and commune levels. Produced by Fields Data and updated in March 2026, the records cover specific administrative areas including Giteranyi, Muyinga, and Rumonge.
Fields Data provides this 3W (Who, What, Where) operational presence data for the Dinagat Islands Province in the Philippines, updated as of March 2026. The dataset tracks humanitarian organizations, their active sectors, and their specific geographic locations at the province level.
This dataset maps the operational presence of humanitarian and development organizations in Siquijor Province, Philippines, as of March 2026. Produced by Fields Data, it utilizes the 'Who, What, Where' (3W) framework to document organizational activity across various sectors at the province level.
This dataset details the 3W (Who, What, Where) operational presence of organizations across Pangasinan Province, Philippines, categorized by sector and location. Produced by Fields Data and updated in March 2026, the records follow the Humanitarian Exchange Language (HXL) standard for tabular data.
Fields Data provides this 3W (Who, What, Where) dataset detailing organizational presence within the Oriental Mindoro Province of the Philippines. Updated in March 2026, the data tracks humanitarian and development actors across various sectors and administrative locations. It is formatted as an XLSX file and utilizes Humanitarian Exchange Language (HXL) standards.
WildDet3D Bench is a 3D object detection benchmark created by AllenAI, last updated in April 2026. It provides a validation split of 2,470 images with 9,256 annotations across 785 categories, aggregated from COCO, LVIS, and Objects365 datasets. The test split is held out for hidden evaluation on a dedicated server.
Delivering operational presence data (3W) for Cotabato Province, Philippines, detailing humanitarian organizations, sectors, and locations. Maintained by Fields Data and updated as of March 2026, it maps the specific interventions of various agencies within the province. The data is formatted with Humanitarian Exchange Language (HXL) tags to facilitate interoperability between humanitarian platforms.
Fields Data maintains this 3W (Who, What, Where) dataset documenting humanitarian activities in Leyte Province, Philippines, as of March 2026. It records the operational presence of various organizations categorized by sector and geographic location at the provincial level.