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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,002 datasets
Spectral Outgoing Longwave Radiation (OLR) parameters derived from AIRS version 6 radiances. Data is provided monthly on a global 2x2 degree latitude/longitude grid. The algorithm was developed by Xianglei Huang at the University of Michigan using data from the Atmospheric InfraRed Sounder (AIRS) on NASA's EOS-Aqua spacecraft.
Antarctic Artefacts Database contains records of items recorded in-situ in Antarctica or retrieved to Australia. The archive is organized around three main collections: Antarctic Division Library Artefacts, Cape Denison Artefacts, and Heard Island Artefacts. It was created by the Australian Antarctic Data Centre (AU_AADC) and incorporates the original library artefacts database.
Geoscience Australia Data provides a 2011 snapshot of Australian manganese resources. The dataset includes separate sheets detailing resources by region and by deposit type, presented in HTML and PDF formats.
A dataset likely related to the YOLO (You Only Look Once) object detection model series. It is published on Kaggle. The specific contents, size, and creation details are not provided in the available metadata.
E-BARD extends the original Basketball Action Recognition Dataset with granular annotations for multi-stage computer vision pipelines. The dataset was extracted from 60 full-game recordings from the 2024-2025 NBA season, covering all 30 teams. It was introduced by GabrieleGiudici in a paper addressing the need for player detection, team attribution, and jersey number recognition.
Draft CalEnviroScreen 5.0 results identify California communities disproportionately burdened by pollution, incorporating recent census geography and data. The dataset includes indicator scores and percentiles, introducing two new indicators: Diabetes Prevalence and Small Air Toxic Sites. It is produced by the Office of Environmental Health Hazard Assessment (OEHHA) and was updated in March 2026.
Model_ResNet152v2_TSK_VINDR is a dataset of pre-trained model weights for the ResNet152v2 architecture. It was published on the Kaggle platform. The specific task, training data, and performance metrics are not detailed in the available metadata.
A Kaggle dataset titled 'Solar panel _ noise removed + GANs Output'. The title suggests it contains images of solar panels processed to remove noise and includes outputs generated by Generative Adversarial Networks (GANs). The dataset's author, organization, size, and specific content are unknown.
A dataset of images generated by Generative Adversarial Networks (GANs) on the Kaggle platform. The dataset's title suggests a focus on solar panel imagery. Specific details on volume, creator, and update date are unavailable.
Stella Muthuri's dataset contains results from a multivariate logistic regression analysis of factors linked to polyvictimization among 13-24-year-olds. The analysis is based on household surveys from Uganda HVACS 2022 and Ethiopia HVACS 2024. The dataset is 9.5 KB in size and was last updated on March 18, 2026.
Stella Muthuri's dataset provides percentages of 13-24-year-olds who experienced polyvictimization, broken down by gender and background characteristics. The data originates from household surveys conducted in Uganda in 2022 and Ethiopia in 2024. It is a small, 13.5 KB Excel file shared under a CC-BY-4.0 license.
2022 and 2024 data from cross-sectional household surveys (HVACS) in Uganda and Ethiopia. The dataset contains prevalence estimates for victimization, polyvictimization, and mental health outcomes among 13-24-year-old females and males. It was authored by Stella Muthuri and published on figshare in March 2026.
Uganda and Ethiopia household survey data on background characteristics for 13-24-year-old females and males. The dataset is 9.5 KB in size, shared by Stella Muthuri on figshare under a CC-BY-4.0 license, and was last updated in March 2026. It likely contains variables related to household composition, food security, and mental health outcomes.
A LiDAR survey commissioned by Fugro in March 2022 covers four soft sedimentary areas along the north coast of Northern Ireland: Curran Strand, Portrush East Strand, Portstewart Strand, and Downhill Beach to Magilligan. The survey was conducted to assess coastal changes following Storm Dudley, Storm Eunice, and Storm Franklin, with data provided in a format consistent with a 2021 baseline survey. This dataset is the Digital Terrain Model derived from that post-storm LiDAR data.
Andreea V. Florea Toma and colleagues released this curated collection in 2026 to provide a standardized benchmark for cryo-EM map post-processing. The dataset contains average maps and associated metadata partitioned into non-overlapping training, validation, and test subsets for reproducible method evaluation.
The physical and geochemical parameters controlling algal production in Lake Vanda were investigated by SCIOPS. The dataset includes experimental nutrient bioassays, in situ radiotracer incubations, photosynthetic profiles, and measurements of associated physico-chemical variables like nutrients, irradiance, temperature, and salinity. Data was last updated on 1981-01-24.
Geochemical data from a 3-month Sediment Recruitment Experiment at Casey Station examines contamination from station activities. The dataset includes concentrations of petroleum hydrocarbons and heavy metals in sediment samples. It was collected by AU_AADC and last updated in March 1999.
Davis Station, East Antarctica, was the site for monitoring phytoplankton biomass and species composition over three consecutive summer seasons from December to February, 1992-1995. The dataset identifies four distinct phytoplankton assemblages and examines factors influencing their variation, including ice break-out, water column conditions, and wind events. It was aggregated by the AU_AADC organization and last updated in February 1995.
Netherlands National Upper Air is a historical meteorological dataset covering the period from 1945 to 1991. It contains upper air data for one reporting station, with major parameters including pressure, temperature, relative humidity, and wind speed and direction. The dataset was received from the Royal Netherlands Meteorological Institute and archived by the National Climatic Data Center.
Historical upper air meteorological data extracted from the Global Telecommunications System (GTS) under international agreement. The dataset contains mandatory and significant level observations including pressure, height, temperature, relative humidity, and wind speed and direction. It was compiled by the National Meteorological Center (now NCEP) and archived by the National Climatic Data Center, with general collection starting in September 1963 for the Northern Hemisphere and ending in December 1970.