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921 results for “neural networks”
First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
Trained models for "Neural mechanisms of working memory accuracy revealed by recurrent neural networks"
<p>There are three trained models:</p> <p>1. data/6tasks_8loc_256neuron_odr3000_seed0: odr(3s delay ) task with 8 input units in a ring and 256 neurons</p> <p>2. data/6tasks_360loc_256neuron: odr(1.5s delay) task with 360 input units in a ring and 256 neurons</p> <p>3. odr_mix_uniform_00_30_01step_6tasks: odr(variable delay from 0 to 3s with 0.1s step) task with 8 input units in a ring and 256 neurons</p>
Convolutional-neural-network-based reflection full-waveform inversion
<p>The data is used by the paper "Convolutional-neural-network-based reflection 1 full-waveform inversion"</p>
Neural Network Radiation Emulator (KMA/NIMS), January
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (January)</p> <p> </p>
Neural Network Radiation Emulator (KMA/NIMS), October
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (February)</p>
Neural Network Radiation Emulator (KMA/NIMS), September
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (September)</p>
Neural Network Radiation Emulator (KMA/NIMS), August
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (August)</p>
Neural Network Radiation Emulator (KMA/NIMS), July
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (July)</p>
Neural Network Radiation Emulator (KMA/NIMS), April
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (April)</p>
Neural Network Radiation Emulator (KMA/NIMS), June
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (June)</p>
Neural Network Radiation Emulator (KMA/NIMS), March
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (March)</p>
Neural Network Radiation Emulator (KMA/NIMS), February
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (February)</p>
Characterization of deep neural network features by decodability from human brain activity
<p>We present a dataset derived through the DNN feature decoding analyses (<a href="https://www.nature.com/articles/ncomms15037">Horikawa and Kamitani, 2017</a>), including true and decoded feature values of DNNs (AlexNet and VGG19) and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual DNN features were highly correlated across subjects, suggesting the systematic differences between the brain and DNNs. The unpreprocessed fMRI data is available from the OpenNeuro (<a href="https://openneuro.org/datasets/ds001246">https://openneuro.org/datasets/ds001246</a>). We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.</p>
Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis
<p>Relevant data for the publication titled: Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis</p>
Code and dataset for publication "Laser Wakefield Accelerator modelling with Variational Neural Networks"
<p>Data and code for reproducing figures in published work.</p> <p> </p> <p>High Power Laser Science and Engineering</p> <p><a href="https://doi.org/10.1017/hpl.2022.47">https://doi.org/10.1017/hpl.2022.47</a></p> <p>Code used various python packages including tensorflow.</p> <p>Conda environment was created with (on 6th Jan 2022)<br> conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge</p>
Microseismic source imaging using physics-informed neural networks with hard constraints
<p>Locating subsurface seismic sources is crucial to both seismic monitoring and seismology. We propose a novel direct source imaging framework based on physics-informed neural networks with hard constraints. In this letter, we present the relevant dataset to the paper. </p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (<em>D</em>, <em>H</em> and <em>Z</em>) in 2021. This reconstruction method has been proved to be effective.</p>
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks
<p>Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
ScienceDex guides
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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.