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Dataset results
98 results for “CNN”
CNN for image-based sediment detection applied to a large terrestrial and airborne dataset
<p>This repository contains data sets and model used in the manuscript "CNN for image-based sediment detection applied to a large terrestrial and airborne dataset" submitted to Earth Surface Dynamics (2021) by Xingyu Chen, Marwan A. Hassan, and Xudong Fu.</p>
Labelled data for P/S separation with CNN
<p>This is the part of training labelled Dataset-B for the publication "P/S separation of multi-component seismic data at land surface based on deep learning".</p> <p>There are 500-shot data labels:</p> <p>Train/, Val/ & Test/ are the separated file for Training, Validation & Testing</p> <p>The labelled data size are nx*nz=1001*3001 using a IEEE float format</p> <p>One can use the Seisic Unix command to plot for QC: ximage < shotx_mod_1.dat n1=3001 perc=99 & </p> <p> </p> <p>In each directory, the files are named as follows: </p> <p>The horizontal component:</p> <p> shotx_mod_*.dat </p> <p>The vertical component:</p> <p> shotz_mod_*.dat </p> <p>The P-wave label :</p> <p> shotp_*.dat </p> <p>The S-wave label :</p> <p> shots_*.dat </p>
Training and test dataset for CNN-LSTM model for GW waveform extraction
<p>This repository contains training and test samples corresponding to the paper, 'Extraction of binary black hole gravitational wave signals from detector data using deep learning', Chatterjee et al., Phys. Rev. D <strong>104</strong>, 064046 (2021).</p>
Trained CNN for analysis of melanoma whole slide images to automatically assess the infiltration of TILs
<p>The file is the trained convolutional neural network (CNN) developed in "Ugolini F et al.Tumor infiltrating lymphocytes recognition in primary melanoma by deep learning convolutional neural network" submitted to American Journal of Pathology (2023). The CNN is based on a a pre-trained Inception-ResNet-v2 to automatically to recognize areas in the tumor region containing TILs and area without TILs in histopathological digitalized slides of primary melanoma. The file is in a Matlab format (.mat).</p>
Data for training AMSR2-CNN and its corresponding machine learning algorithm
<p>Despite the availability of multiple decades of passive microwave measurements from satellite platforms, their utility for developing quantitative, spatially distributed estimates of snowpack is yet to be realized. A major bottleneck is the use of simple conceptual retrieval model formulations that are ineffective in representing the significant heterogeneity and complexity of snow evolution, particularly over areas with complex topography and forest regions. Here we demonstrate a physics-constrained and interpretable Convolutional Neural Network (CNN) to learn the functional relationship utilizing multi-channel passive microwave brightness temperature measurements from the Advanced Microwave Scanning Radiometer 2 (AMSR2) and in-situ snow depth observations. The machine learning approach with CNN generates vastly improved snow depth estimates relative to the standard AMSR2 estimates. Compared to independent in-situ measurements of snow depth over the Continental United States, the domain averaged Pearson correlation measure is three times higher than that of the standard AMSR2 estimates (R<sup>2</sup>: 0.68 versus 0.21), while the systematic errors are reduced by approximately fourfold. Further, the CNN-based snow depth estimates also exhibit notable enhancements in regions with forests, deep snow, and melting snow, thereby alleviating the limitations faced by traditional algorithms in retrieving accurate snow depths. The interpretation of the CNN framework further indicates that the machine learning approach dynamically leverages both volume scattering and emission components from a suite of measured passive microwave signals to generate more accurate snow depth retrievals. The results of this study provide an important benchmark of high-quality snow retrievals from passive microwave satellite measurements by maximizing their information content.</p>
Data for training AMSR2-CNN and its corresponding machine learning algorithm
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Trained CNN for skin detection - https://arxiv.org/abs/1802.02531
<p>Trained CNN for skin detection</p> <p>https://arxiv.org/abs/1802.02531</p> <p> </p>
Estimation of Ca2+ wet deposition in the Northern Hemisphere by use of CNN deep-learning model
<p>A dataset to estimate long-term and high-resolution gridded Ca2+ wet deposition across the Northern Hemisphere from 2001-2022.</p>
SENSES-ASD-CNN
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Deep Learning-Based Prediction of Global Ionospheric TEC during Storm Periods: Mixed CNN-BiLSTM Method
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Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishs in real time
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ai-matrix CNN_Tensorflow graphs_NCHW
<p>Large files in ai-matrix.</p>
ai-matrix CNN_Tensorflow graphs_NHWC
<p>Large files in ai-matrix.</p>
ai-matrix CNN_Tensorflow
<p>Large files in ai-matrix.</p>
The CNN classifier at the species level for fungal classification
<p>The CNN classifier at the species level based on WI-CBS mould ITS barcode dataset.</p>
The CNN classifier at the class level for fungal identification
<p>The CNN classifier was trained based on the CNN model using the WI-CBS ITS barcode dataset.</p>
Dataset and CNN code for DECam CNN Difference Imaging Artifact Paper
<p>Dataset and code to accompany the paper listed at: https://arxiv.org/abs/2106.11315v1</p>
2022-12-07_CHiMP_Mask_R_CNN_XChem_50eph_VMXi_finetune_DICT_NZ
<p>2022-12-07_CHiMP_Mask_R_CNN_XChem_50eph_VMXi_finetune_DICT_NZ</p>
Soil images in DICOM format including Python programs for data transformation, 3D analysis, CNN traininig, CNN analysis
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Periodic and heterogeneous solid and velocity data used to train and validate CNN models
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.