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921
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ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
Convolutional Neural Network in Ovarian Follicle Identification
ClinicalTrials.gov study NCT04545918. IPD Sharing: NO. Countries: 1. Publications: 2.
Improving Dysregulated Neural Networks With EEG-neurofeedback
ClinicalTrials.gov study NCT06587919. IPD Sharing: NO. Countries: 1. Publications: 11.
Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks
ClinicalTrials.gov study NCT05085743. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Development and Validation of Deep Neural Networks for Blinking Identification and Classification
ClinicalTrials.gov study NCT04828187. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Development of a Novel Convolution Neural Network for Arrhythmia Classification
ClinicalTrials.gov study NCT03662802. IPD Sharing: NO. Countries: 1. Publications: 14.
A New Neuroregulatory Technology for the Therapy of AN Based on the Pathological Neural Network of ACC
ClinicalTrials.gov study NCT06152640. IPD Sharing: YES. Countries: 1. Publications: 9.
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
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Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks
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Data from: Deep neural networks for accurate predictions of crystal stability
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Data for assessment of damage to residential dwellings using artificial neural networks
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Data from: StomataCounter: a neural network for automatic stomata identification and counting
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Double attention recurrent convolution neural network for answer selection
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Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages
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Dataset for: Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements by Toshiyuki Bandai and Teamrat A. Ghezzehei
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Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). <sup>1</sup>H spectra (43 MHz) were recorded as single scans.</p> <p> Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (<em>i</em>) Training data based on combinations of measured pure component spectra and (<em>ii</em>) Training data based on a spectral model.</p> <p><strong>Synthetic low-field NMR spectra</strong></p> <p>First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.</p> <p><em>X<sub>i</sub></em> (“pure component spectra dataset”)</p> <p><em>X<sub>ii</sub></em> (“spectral model dataset”)</p> <p><strong>Experimental low-field NMR spectra from MNDPA-Synthesis</strong></p> <p>This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.</p>
Modeling plate and spring reverberation using a DSP-informed deep neural network
<p>Accompanying audio samples for the paper:</p> <p>Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Modeling plate and spring reverberation using a DSP-informed deep neural network” in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.</p> <p>Dry and wet bass and guitar recordings.</p> <p>Bass and Guitar dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html</p> <p>Plate Reverb - Bass - recordings are taken from the IDMT-SMT-Audio-Effects dataset. Plate settings are the following:</p> <ul> <li><strong>Smaertelectronix ambience</strong>: ’Gating Amount - 0’, ’Gating Attack" - 10 ms’, ’Gating Release - 10 ms’, ’Decay Time - 2225 ms’, ’Decay Diffusion - 50%’, ’Decay Hold - off’, ’Shape Size - 16%’, ’Shape Predelay - 0 ms’, ’Shape Width - 100%’, ’Shape Quality - 100%’, ’Shape Variation - 0’, ’EQ Bass Frequency - 43 Hz’, ’EQ Bass Gain - −7.8 dB’, ’EQ Treble Frequency - 5044 Hz’, ’EQ Treble Gain - −3.7 dB’, ’Damping Bass Frequency - 158 Hz’, ’Damping Bass Amount - 87%’, ’Damping Treble Frequency - 8127 Hz’, ’Damping Treble Amount - 32%’, ’Dry - −Inf’, ’Wet - 0dB’.</li> </ul> <p>Spring Reverb - Bass and Guitar - recorded from the spring reverb tank<strong>: Accutronics </strong><strong>4</strong><strong>EB</strong><strong>2</strong><strong>C</strong><strong>1</strong><strong>B</strong>: ’Dry Mix - 0%’, ’Wet Mix - 100%’</p> <p>Plate<em> </em>reverb samples correspond to a VST audio plug-in, while spring<em> </em>reverb samples are recorded using an analog reverb tank which is based on 2 springs placed in parallel.</p> <p>The recordings are downsampled to 16 kHz. Also, since the plate reverb samples have a fade-out applied in the last 0.5 seconds of the recordings, we process the spring reverb samples accordingly.</p>
Data set for Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation
<p>The dataset contains over-the-air measurements on a 64 active antenna array (Anokiwave AWMF-0129) operating at 28 GHz carrier frequency and transmitting a 200 MHz OFDM waveform with FFT size of 4096, 3168 active subcarriers, subcarrier spacing of 60 kHz and 5 times oversampling w.r.t the critical sampling rate. The dataset contains the I/Q samples of the TX waveform as well as the corresponding over-the-air received signals when the electrical beam is steered toward different beamforming directions. This dataset allows to observe and study the so-called beam-dependent load modulation, which is the phenomenon that causes the nonlinear characteristics of the antenna array to change with the beamforming direction. A Matlab script for data visualization is also provided.</p> <p>For further details please refer to the following papers:</p> <p>A. Brihuega <em>et al</em>., "Piecewise Digital Predistortion for mmWave Active Antenna Arrays: Algorithms and Measurements," in <em>IEEE Transactions on Microwave Theory and Techniques</em>, doi: 10.1109/TMTT.2020.2994311.</p> <p>A. Brihuega <em>et al</em>., “Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation,” in <em>IEEE Microwave and Wireless Components Letters, </em>doi: 10.1109/LMWC.2020.3004003</p>
Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
<p>This repository contains the cornerplots of relevant parameters for the 23 protoplanetary disks modeled in the manuscript "Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses" (Ribas et al. 2020).</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.