Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
129
datasets available to search
ShareScore release 0.9.0
Dataset results
129 results for “Deep neural network”
Improving rainfall forecast at the district scale over the eastern Indian region using deep neural network
<p>The code contains ANN and CNN models.</p>
Uncovering Stress Fields and Defects Distributions in Graphene Using Deep Neural Networks
<p>The trained neural networks, complete data set, and MATLAB script used to generate molecular dynamics simulation files are available here.</p>
Application of deep neural networks to reconstruct coastal water quality data
<p>In this study ordinary and new integrated deep neural networks were developed for the reconstruction of measured specific conductance (<em>SC</em>) data. Five stations of USGS in the Gulf of Mexico were considered as case study.</p>
Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
<p>Results and code to replicate analysis in "Interpreting Cis-Regulatory Interactions from<br> Large-Scale Deep Neural Networks for Genomics" by Toneyan and Koo.</p>
Assessment of the Breast Cosmesis Using Deep Neural Networks: an Exploratory Study (ABCD)
ClinicalTrials.gov study NCT05450016. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists
ClinicalTrials.gov study NCT04636164. IPD Sharing: NO. Countries: 1. Publications: 7.
Deep Neural Network Stratification for Use Detecting Endometriosis in Women Affected by Chronic Pelvic Pain (EndoCheck)
ClinicalTrials.gov study NCT05245695. IPD Sharing: NO. Countries: 1. Publications: 3.
Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
ClinicalTrials.gov study NCT05303051. IPD Sharing: NO. Countries: 1. Publications: 1.
Identification of Interscalene Brachial Plexus on Ultrasonography Using a Deep Neural Network
ClinicalTrials.gov study NCT04183972. IPD Sharing: NO. Countries: 1. Publications: 1.
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.
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
Open the record for dataset details and reuse information.
Data from: Deep neural networks for accurate predictions of crystal stability
Open the record for dataset details and reuse information.
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>
Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
test data for Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis
<p>Companion python scripts are available in: https://github.com/xin-gou/automatic-ecgdiagnosis</p>
An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
Open the record for dataset details and reuse information.
Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks
<p>This dataset relates to the publications:</p> <p><a href="https://doi.org/10.1038/s41467-024-49378-8">Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.</a> <span>Karunanithy G, Shukla VK, Hansen DF<strong>. </strong></span><span>Nat Commun. 2024 Jun 13;15(1):5073. doi: 10.1038/s41467-024-49378-8.</span></p> <ul> <li>Training data for Deep Neural Networks developed in the manuscript "Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks"</li> <li>Experimental cross-validation data: <ul> <li>2D spectra of HDAC8, MSG, and a7a7 proteasome</li> <li>3D NOESY spectra of MSG</li> </ul> </li> </ul> <p>Please see GitHub (https://github.com/gogulan-k/FID-Net) for additional scripts and details. </p>
Deep Neural Network Surrogate for Surface Complexation Model of Metal Oxide/Electrolyte Interface
<p>These files are the data used in the paper "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ncAYQ4MAAAAJ&sortby=pubdate&citation_for_view=ncAYQ4MAAAAJ:LkGwnXOMwfcC">Deep neural network surrogate for surface complexation model of metal oxide/electrolyte interface</a>".</p> <ul> <li>CSV files are used to train the DNN model.</li> <li>NPZ files are used to train the random forest model. </li> </ul>
Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
Open the record for dataset details and reuse information.
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.