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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
15,870 datasets
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.
Fields Data provides this 3W (Who, What, Where) dataset detailing the operational presence of organizations across sectors in Eastern Samar, Philippines. Updated in March 2026, the data identifies active humanitarian and development entities and their specific geographic locations at the province level.
Fields Data maintains this 3W (Who, What, Where) dataset documenting the operational presence of organizations within the Occidental Mindoro province of the Philippines. Updated in March 2026, the records categorize activities by sector and specific geographic locations to facilitate humanitarian coordination. The data is provided in XLSX format and includes HXL standard tagging.
Fields Data provides this 3W (Who, What, Where) dataset detailing humanitarian operational presence across Bohol Province, Philippines. Updated in March 2026, the data maps organizations to specific sectors and locations to facilitate aid coordination. It serves as a snapshot of the humanitarian landscape within this specific geographic region.
Fields Data provides this 3W (Who, What, Where) dataset tracking humanitarian operational presence within Samar Province, Philippines. Updated in March 2026, the records identify organizations active in specific sectors and geographic locations using the Humanitarian Exchange Language (HXL) standard.
This 3W (Who, What, Where) dataset from Fields Data tracks humanitarian organizations and their sectoral activities in Camarines Sur, Philippines. Updated in March 2026, it provides provincial-level operational presence data formatted with HXL tags for interoperability.
Fields Data provides this 3W (Who, What, Where) dataset tracking humanitarian and development organization activities in Southern Leyte, Philippines. Updated as of March 2026, it maps operational presence by sector and location at the province level. The data is formatted in XLSX and includes HXL tags for standardized humanitarian data exchange.
This dataset tracks humanitarian operational presence (3W) across Northern Samar Province, Philippines, as of March 2026. Produced by Fields Data, it maps specific organizations to their active sectors and geographic locations at the province level. The data is formatted using Humanitarian Exchange Language (HXL) standards for interoperability.
Negros Occidental Province 3W data tracks organizational presence across sectors and locations in the Philippines as of March 2026. Produced by Fields Data, the records identify which organizations (Who) are performing specific activities (What) in particular areas (Where).
A structural biology dataset on the ฮฒ-barrel assembly machinery (BAM) complex, which folds outer membrane proteins in diderm bacteria. The data is provided in an XLSX file of 22.2 KB, authored by Daniel Birtles and last updated in March 2026.
Daniel Birtles provides a dataset on the ฮฒ-barrel assembly machinery (BAM) complex and its role in folding outer membrane proteins (OMPs) in diderm bacteria. The data, stored in a 23.5 KB XLSX file, supports research into structural biology and antimicrobial target discovery. It includes information on BAM conformational cycling and its interactions with substrate OMPs.
The Scripps Center for Coastal Studies' Data Zoo aggregates data from over 20 distinct oceanographic studies and programs. It includes the Santa Barbara Channel-Santa Maria Basin Study, the Central and Northern California Circulation Studies, and experiments like CODE, CAMP, and CalCOFI. The collection is hosted on NASA's Earthdata platform.
A long-term record from 1953 to the present, this dataset contains monthly mean zonal wind components for the equatorial stratosphere. It was compiled by Freie Universitรคt Berlin from radiosonde observations at stations including Canton Island, Gan/Maledive Islands, and Singapore. The data is intended to be representative of the equatorial belt for studying the Quasi-Biennial Oscillation.
Operational near-real-time ocean surface wind vector retrievals are optimized for coastal regions, allowing wind computation as close as 15 km from shore. Data is produced by the EUMETSAT Ocean and Sea Ice Satellite Application Facility and provided through the Royal Netherlands Meteorological Institute. Each file contains one full orbit derived from 3-minute orbit granules with a latency of approximately 2 hours from the latest measurement.
Operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on the MetOp-C satellite. The product is provided by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSI SAF) via the Royal Netherlands Meteorological Institute (KNMI). Data is processed using the CMOD7.n geophysical model function with a Hamming filter on Sigma-0 data.
Operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer on the MetOp-B satellite. The dataset is produced by the EUMETSAT Ocean and Sea Ice Satellite Application Facility and provided through the Royal Netherlands Meteorological Institute. It uses a spatial box filter on Level 1B Sigma-0 retrievals to enable wind computation as close as ~15 km from the coast.
Operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on the MetOp-B satellite. The dataset is produced by the EUMETSAT Ocean and Sea Ice Satellite Application Facility and processed by the Royal Netherlands Meteorological Institute. Data is provided in full-orbit swaths with approximately 2-hour latency from measurement.