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921
datasets available to search
ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Are neural network potentials trained on liquid states transferable to crystal nucleation? A test on ice nucleation in the mW water model
<p>Dataset used to train the neural network potential for reproducing mW nucleation.</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Test data set</p>
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
<p>This is training dataset for our publication with title "Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics" at arXiv https://doi.org/10.48550/arXiv.2404.14021</p>
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material
<p>Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from <a href="https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial" target="_blank" rel="noopener">https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial</a>.</p> <p> </p>
The Diagnostic Performance of BMO-MRW and RNFL Thickness and Their Combinational Index Using Artificial Neural Network
ClinicalTrials.gov study NCT03257020. IPD Sharing: YES. Countries: 1. Publications: 0.
Understanding the Effects of Transauricular Vagus Nerve Stimulation on Neural Networks and Autonomic Nervous System
ClinicalTrials.gov study NCT05801809. IPD Sharing: NO. Countries: 1. Publications: 0.
Detection of Sleep Stages and Arousals Using Neural Network Classifiers
ClinicalTrials.gov study NCT07136272. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
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Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
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Data from: Recurrent myocardial infarction: mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control
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Data from: Molecular evolution of the neural crest regulatory network in ray-finned fish
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Unexpected high accuracy of landscape genetics inference with convolutional neural networks
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Data from: Estrogen receptor alpha distribution and expression in the social neural network of monogamous and polygynous Peromyscus
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Data from: Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour
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Drones, automatic counting tools and artificial neural networks in wildlife population censusing
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Key generic technology prediction in patent citation using graph neural networks
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Identification of species by combining molecular and morphological data using convolutional neural networks
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TROPICS05 L2B Neural-network Atmospheric Vertical Temperature & Moisture Profiles V0.2
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is the Level-2B Neural Network Atmospheric Vertical Profiles (NNAVP) – Neural Network vertical profile retrieval approach for temperature profiles in Kelvin (K) and water vapor mixing ratio profiles in (kg/kg). Retrievals are done in all non and precipitating conditions, over both land and ocean. Temperature profiles go from surface to 20 km and water profiles from surface to 10-km. The geophysical retrieval of atmospheric vertical temperature is at the larger unified F-band spatial resolution while the retrieval of vertical moisture is at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
TROPICS03 L2B Neural-network Atmospheric Vertical Temperature & Moisture Profiles V1.0
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is the Level-2B Neural Network Atmospheric Vertical Profiles (NNAVP) – Neural Network vertical profile retrieval approach for temperature profiles in Kelvin (K) and water vapor mixing ratio profiles in (kg/kg). Retrievals are done in all non and precipitating conditions, over both land and ocean. Temperature profiles go from surface to 20 km and water profiles from surface to 10-km. The geophysical retrieval of atmospheric vertical temperature is at the larger unified F-band spatial resolution while the retrieval of vertical moisture is at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
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.