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921
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Dataset results
921 results for “neural networks”
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
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Efficient sub-pixel fully connected neural network: an intelligent fault diagnosis method for signal resolution enhancement
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Data from: A convolutional neural network for detecting sea turtles in drone imagery
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Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
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Echolocation clicks and anthropogenic detections with neural network labels in Hawaiian Island HARP data from Kona, Kaua`i, and Pearl and Hermes Reef
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Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
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Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network
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Data from: Performance-based Egress safety assessment of underground tunnels: Simulation and artificial neural network approaches
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Impact of background input on memory consolidation in In-Vitro neural networks
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
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Inductive biases of neural network modularity in spatial navigation
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Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila
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PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies
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Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling
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Myoelectric prosthesis control using recurrent convolutional neural network regression mitigates the limb position effect
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MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
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Data from: A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images
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Data from: Testing the equivalency of human “predators” and deep neural networks in the detection of cryptic moths
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Sub-pixel accuracy in electron detection using a convolutional neural network
<p><strong>Abstract</strong></p> <p>Modern direct electron detectors (DEDs) provided a giant leap in the use of cryogenic electron microscopy (cryo-EM) to study the structures of macromolecules and complexes thereof. However, the currently available commercial DEDs, all based on the monolithic active pixel sensor, still require relative long exposure times and their best results have been obtained at 300 keV. There is a need for pixelated electron counting detectors that can be operated at a broader range of energies, at higher throughput and higher dynamic range. Hybrid Pixel Detectors (HPDs) of the Medipix family were reported to be unsuitable for cryo-EM at energies above 80 keV as those electrons would affect too many pixels. Here we show that the Timepix3, part of the Medipix family, can be used for cryo-EM applications at higher energies. We tested Timepix3 detectors on a 200 keV FEI Tecnai Arctica microscope and a 300 keV FEI Tecnai G2 Polara microscope. A correction method was developed to correct for per-pixel differences in output. Timepix3 data were simulated for individual electron events using the package Geant4Medipix. Global statistical characteristics of the simulated detector response were in good agreement with experimental results. A convolutional neural network (CNN) was trained using the simulated data to predict the incident position of the electron within a pixel cluster. After training, the CNN predicted, on average, .39 pixel and 0.42 pixel from the incident electron position for 200 keV and 300 keV electrons respectively. The CNN improved the MTF of experimental data at half Nyquist from 0.39 to 0.70 at 200 keV, and from 0.06 to 0.65 at 300 keV respectively. We illustrate that the useful dose-lifetime of a protein can be measured within a 1 second exposure using Timepix3.</p> <p><strong>Data description</strong></p> <p>Data has been split up in experimental data, simulations, neural net models and ToT correction results. In general: each directory contains individual READMEs with steps how to reproduce the data.</p> <p><strong>Experimental data</strong></p> <p>For each type of data at 200 kV or 300 kV only the input raw data has been added and the resulting image file. Intermediate files have been left out.</p> <p><strong>Models</strong></p> <p>The models are the CNN models generated at 200 and 300 kV.</p> <p><strong>Simulated data</strong></p> <p>The simulated data consists of the dataset used for training the neural network and the indepedently simulated validation set. </p> <p><strong>ToT correction</strong></p> <p>The ToT correction file only contain the resulting correction matrix. The experimental flat field data has been left out, due to its volume. It's about 300 GiB of data for both 200 and 300 kV.</p> <p><strong>Software </strong></p> <p>The software used can be found as related identifiers to this deposit.</p>
Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
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.