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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
14,111 datasets
Uranium concentrations in organic-rich shales range from less than 5 to over 500 parts per million. This dataset from Geoscience Australia examines correlations between uranium, total organic carbon, and pyrolysis oil yield across Proterozoic to Cretaceous shales in Australian basins. It is intended for testing the hypothesis that uranium-TOC correlations can calibrate hydrocarbon potential.
Bo Li published results from ablation experiments for a dropout method on 2026-04 -29. The dataset is 5.5 KB in size and is stored in an XLS file. The experiments were part of a study proposing a novel encoder combining CNN and Swin Transformer for medical image segmentation, evaluated on the Synapse Multi-Organ and Aortic Vessel Tree datasets.
An ablation study for the AOW-YOLO model, a novel lightweight object detection system designed to identify smoking behavior in complex construction site environments. The dataset, shared by Ruishi Liang on figshare, contains experimental results showing the model's performance improvements over baseline models. It was last updated on May 6, 2026.
AOW-YOLO is a novel model designed to detect smoking behavior in complex construction site environments, where objects are typically small and backgrounds cluttered. The model, proposed by Ruishi Liang, reportedly surpasses existing lightweight models in mAP metrics while achieving roughly 31.6% faster inference speed than YOLO11n. The underlying dataset, shared on figshare under a CC-BY-4.0 license, was last updated in May 2026.
A 5.5 KB Excel file by Ruishi Liang, last updated on 2026-05-06, comparing novel loss functions for object detection models. The dataset likely contains performance metrics from experiments evaluating the AOW-YOLO model against other lightweight models like PP-LCNet and YOLO11n. The research focuses on detecting smoking behavior in complex construction site environments.
Ruishi Liang's research dataset, last updated May 6, 2026, compares the performance of the novel AOW-YOLO model against other lightweight models for detecting smoking behavior in complex construction site environments. The dataset likely contains tabular experimental results, including metrics like mAP50 and inference speed. It is shared under a CC-BY-4.0 license on figshare.
AOW-YOLO is a novel model achieving a roughly 31.6% faster inference speed than YOLO11n for detecting smoking behavior on construction sites. The dataset, shared by Ruishi Liang on figshare in May 2026, contains the experimental hardware and software configuration details for this model. It is a small 5.5 KB file in XLS format.
Ruishi Liang published details on the AOW-YOLO model architecture on 2026-05-06 via figshare. The 5.5 KB XLS file describes a novel object detection model designed to identify smoking behavior in complex construction site environments. The model reportedly outperforms existing lightweight models in mAP metrics and achieves faster inference speeds.
770 inter-clan alliances documented through 906 marriages among 623 'Ndrangheta clans form a network analyzed for power and cohesion. Maurizio Catino published this dataset on figshare in May 2026. The data likely includes clan identifiers, marriage ties, and centrality metrics derived from judicial records.
Supplementary file 1_Advancing emergency medical team classification in MENA region: a qualitative study.docx contains qualitative interview data from a study on Emergency Medical Team (EMT) classification. The study involved 17 stakeholders from five low- and middle-income countries in the MENA region and the IFRC regional office. The dataset was authored by Mohamed Abdelaziz and last updated on 2026-05-19.
Leila Gamonal-Pajares translated and validated the Internal Communication Satisfaction Questionnaire (ICSQ) for Spanish-speaking organizational contexts. The dataset includes responses from 509 Peruvian workers from public and private sectors, collected via a quantitative, non-experimental, cross-sectional method. Confirmatory factor analysis indicated excellent model fit for the eight-factor scale, with CFI > 0.95, SRMR < 0.08, and RMSEA < 0.06.
A study by Rina Shinjo, last updated in May 2026, investigates the effects of the endophytic bacterium Burkholderia vietnamiensis RS1 on nitrogen metabolism in rice. The dataset likely contains quantitative measurements from experiments using 15N-labeled ammonium nitrate, metabolic analysis via LC-MS/MS, and RT-qPCR gene expression data from rice roots. Findings indicate RS1 preferentially promotes nitrate uptake and assimilation, improving nitrogen use efficiency and biomass production under nitrate fertilization.
Rina Shinjo published a dataset on figshare in May 2026. The data likely contains quantitative results from experiments measuring the effect of the endophytic bacterium Burkholderia vietnamiensis RS1 on nitrate uptake and nitrogen metabolism in rice plants. The study includes transcriptomic, metabolic, and field experiment data comparing nitrate- and urea-based fertilization.
Transcriptomic, metabolic, and field experiment data investigate how the endophytic bacterium Burkholderia vietnamiensis RS1 promotes nitrate uptake and assimilation in rice. The dataset includes results from 15N-labeled ammonium nitrate experiments, LC-MS/MS metabolic analysis, RT-qPCR gene expression, and field trials comparing nitrate- and urea-based fertilization. Author Rina Shinko published the data on figshare under a CC-BY-4.0 license in May 2026.
Additional file 1 from a study on enhancing Korshinsk peashrub as ruminant feed via fungal pretreatment. The dataset comprises 12 Excel tables covering diet compositions, chemical analyses of decayed biomass, genomic data for the white rot fungus Dichomitus squalens, and comparative enzyme analyses. It was authored by Jiale Liao and last updated on May 12, 2026.
A 2026 meta-analysis by Zhihong Li synthesizes 125 independent studies on ethics and organizational citizenship behavior (OCB). The work aggregates data from 158,336 participants, analyzing 28 ethical factors and 331 effect values. It proposes a theoretical framework for the relationship between ethics and OCB, identifying significant positive and negative associations.
Benthic sediment sampling of Inner Darwin Harbour (GA0358) and shallow water areas in and around Bynoe Harbour (GA0359) was undertaken between May 29 and June 19, 2017. The dataset comprises total sediment metabolism, carbonate, organic isotope and organic and inorganic element measurements on seabed sediments. This work forms part of a four-year (2014-2018) science program aimed at collecting baseline data to create thematic habitat maps for marine resource management.
50 samples of surface seabed sediments from Jervis Bay, NSW, collected in August 2008 and February 2009. The dataset includes percentages of total organic carbon (%TOC) and total nitrogen (%TN), TOC/TN ratios, and carbon and nitrogen isotopic ratios. Data were acquired by Geoscience Australia during marine surveys focused on mapping seabed bathymetry and characterising benthic environments.
A Delft3D model simulates hydrodynamics and sediment transport for intensive site 322 in the Terrebonne Basin, Mississippi River Delta. The dataset includes all required simulation files and outputs water velocity, depth-averaged sediment concentrations, and annual inorganic mass accumulation rates for the Delta-X Spring and Fall deployments in 2021. Data is provided by NASA in netCDF and ENVI formats.
Delta-X: Delft3D Sediment Model, Site 294, Terrebonne Basin, MRD, Louisiana, USA is a geospatial model output from NASA. It simulates hydrodynamics and sediment transport for an intensive site in the Mississippi River Delta during the 2021 Spring and Fall deployments. The dataset provides water velocity, depth-averaged sediment concentrations, and derived annual inorganic mass accumulation rates in netCDF format.