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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
15,983 datasets
Eight stations in Mobile Bay, Alabama were sampled monthly for benthic fauna from April 1980 to February 1981. The Alabama Coastal Area Board funded this baseline survey to monitor changes in coastal resources over time. Fauna were collected via 0.1 m² Peterson grab and identified to the lowest possible taxon.
June to September 1976 data collection focused on histopathology and benthic communities in the Mid-Atlantic Outer Continental Shelf. The dataset contains sea surface temperature, wave, turbidity, gear type, and counts and weights for megabenthic species. It was collected by the Virginia Institute of Marine Science (VIMS) and submitted by Dr. Gerald L. Engel.
1975-10-27 to 1976-08-27 data on benthic species and their parasites collected during the Mid-Atlantic Outer Continental Shelf Studies. The dataset includes sea surface temperature, wave, turbidity, gear type, species, parasite type, and infection site information. Data was submitted by Dr. Gerald L. Engel and collected by the Virginia Institute of Marine Science.
UNITAS provides probabilistic regime classifications for 23,787 country-years from 1800 to 2017. This dataset combines information from multiple existing regime indicators to identify regime types and transitions while incorporating measurement uncertainty. The replication package includes R scripts and pre-computed results for the associated academic paper.
NEGOM CTD data originates from the Northeastern Gulf of Mexico Physical Oceanography Program, conducted by Texas A&M University under contract with the U.S. Minerals Management Service. The dataset contains hydrographic and acoustic Doppler current profiler surveys from two research cruises in 1997-1998, covering 94 and 98 sampling stations respectively. Measurements include conductivity, temperature, pressure, dissolved oxygen, six nutrients, and phytoplankton pigments.
October 1997 through June 1998 data from the Northeastern Gulf of Mexico Physical Oceanography Program. The dataset contains hydrographic and acoustic Doppler current profiler surveys with 94 and 98 sampling stations across two cruises, measuring conductivity, temperature, pressure, irradiance, fluorescence, and water chemistry. It was collected by Texas A&M University components under contract for the U.S. Minerals Management Service.
Two hydrographic surveys, N1 and N2, collected data from 94 and 98 stations respectively in the northeastern Gulf of Mexico. The dataset includes continuous profiles of conductivity, temperature, pressure, irradiance, fluorescence, and light transmission, plus water sample analyses for dissolved oxygen and six nutrients. Data were collected by Texas A&M University researchers under contract for the U.S. Minerals Management Service from October 1997 through June 1998.
Original data characterizing organic matter in the surface microlayer of the South Yellow Sea and East China Sea. The dataset was contributed by author Gui-Peng Yang and last updated in March 2026. Its small file size of 41.5 KB indicates a limited scope.
YOLO 1 is a dataset hosted on Kaggle. Its title suggests a focus on object detection, likely for training or benchmarking computer vision models. The dataset's specific contents, size, and origin are not detailed in the provided metadata.
GOM RS-2 SAR TCNNA Oil 2017-04-25 data were collected by NOAA's Office of Response and Restoration as part of the Deepwater Horizon Lessons Learned Studies. The dataset contains satellite and airborne imagery for estimating oil slick coverage and thickness at the MC20 chronic oil discharge site. This research was funded by the U.S. Department of the Interior and Bureau of Safety and Environmental Enforcement through field work in 2016, 2017, and 2018.
NOAA's Deepwater Horizon Lessons Learned Studies collected satellite and airborne imagery, surface oil characterization, and subsurface data to estimate oil slick coverage and thickness. The research was conducted at the Mississippi Canyon lease block #20 (MC20), a site of a chronic oil discharge since 2004. This dataset represents field research from 2016, 2017, and 2018, funded by the U.S. Department of the Interior and NOAA.
NOAA's Deepwater Horizon Lessons Learned Studies collected these remote sensing data to develop methods for estimating oil slick coverage and thickness. The dataset includes synoptic satellite and airborne imagery from field research at the Mississippi Canyon lease block #20 (MC20) site, undertaken in 2016, 2017, and 2018. This research was funded by the U.S. Department of the Interior, Bureau of Safety and Environmental Enforcement, and NOAA.
NOAA's Deepwater Horizon Lessons Learned Studies collected these data to estimate oil slick coverage and thickness. The research involved synoptic satellite and airborne imagery, surface characterization, and chemistry data from the Mississippi Canyon 20 (MC20) site, which has experienced a chronic oil discharge since 2004. This dataset represents field research from 2016, 2017, and 2018, funded by the U.S. Department of the Interior and NOAA.
Eurosat_resnet50 is a dataset hosted on Kaggle. The title suggests it contains satellite imagery, likely from the EuroSAT dataset, potentially pre-processed for use with a ResNet50 model. The dataset's specific content, size, and origin require verification after download.
Water temperature measurements were collected at five specific locations in Narragansett Bay: Arnold Point, Roger Williams University Dock, Dyer Island, East Passage, and West Passage. Roger Williams University gathered this data using Onset Tidbit water temperature loggers from May to December 2015, in support of a grant investigating diseases affecting blue mussel culture. The dataset's presence across multiple platforms indicates its established use in environmental monitoring.
resnet_4096_256_1_1e_4 is a dataset published on Kaggle. The title suggests it is related to the ResNet architecture, likely containing images for computer vision tasks. Specific details on size, author, and update date are unavailable.
resnet_4096_256_64_1e_3 is a dataset hosted on Kaggle. Its title suggests it is likely related to training or evaluating a ResNet-based deep learning model for computer vision tasks. The dataset's specific content, size, and origin are not detailed in the provided metadata.
An experimental dataset from two studies by John G. Bullock of Yale University, investigating the impact of policy descriptions versus party elite cues on public opinion. The data likely contains measures of policy attitudes, exposure to policy information, party cues, and the extent of policy thinking. The experiments suggest that when citizens have policy information, it affects their attitudes at least as much as elite cues.
This dataset contains time-series measurements assessing the fate and effects of synthetic-based drilling mud cuttings discharged from offshore platforms on the benthic environment of the Gulf of Mexico continental shelf and slope. Collected between July 29, 2000 and May 20, 2002, the data include salinity, temperature, oxygen, sediment characterization, hydrocarbon characterization, heavy metal contaminant concentration, total organic carbon, porewater chemistry, and infaunal taxonomic identifications and counts. The objective was to evaluate the environmental impact of synthetic-based drilling fluids.
The North Pacific Ocean and East China Sea are the geographic scope of this dataset. It contains discrete profile measurements of dissolved inorganic carbon, total alkalinity, pH, temperature, salinity, oxygen, and nutrients. Data were collected by the National Oceanic and Atmospheric Administration during the R/V Keifu Maru II cruise ks201504 from April 15 to May 4, 2015.