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921 results for “neural networks”
Data for "Efficient neural decoding of self-location with a deep recurrent network"
<p>Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the article</p> <p>"Efficient neural decoding of self-location with a deep recurrent network".</p> <p> </p> <p>This data should be used with the code found in https://github.com/NeuroCSUT/RatGPS and should be placed in the Bayesian/Data folder of the codebase.</p> <p> </p>
Datasets for "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"
<p>Zipped datasets files for replicating results of: "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"</p> <p>DATASETS WERE NOT CREATED BY US. DATASETS ARE WORKS OF RESEARCHERS AND PAPERS REFERENCED IN "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"</p>
Data for "Convergent temperature representations in artificial and biological neural networks"
<p>Data for "Convergent Temperature Representations in Artificial and Biological<br> neural networks" by Haesemeyer M, Schier AF and Engert F, 2019</p> <p>The corresponding python code is available at:<br> <a href="https://github.com/haesemeyer/GradientPrediction">https://github.com/haesemeyer/GradientPrediction</a></p> <p>All zip files should be extracted in the same folder as the python files. This<br> will create a sub-folder structure for the model data.<br> ZIP File Contents (Note: These are used by the code and not necessarily useful<br> by themselves):<br> model_data.zip<br> Contains tensorflow checkpoints on all naive and fully trained models, test<br> errors during training as well as evolution weights where applicable.<br> model_cluster_assignments.zip<br> For the trained models in model_data.zip the response cluster assignment<br> for each individual unit.<br> zebrafish_data.zip<br> The zebrafish brain and behavior data used in the paper comparisons. This<br> archive also contains the temperature stimulus file stimFile.hdf5<br> training_data.zip<br> The generated training data used during predictive network training<br> test_data.zip<br> The generated test data used to evaluate predictive network training</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images "
<p>Image-crops of specimens from insect drawers</p> <p>In 2017 we scanned 208 insect drawers containing the collection of british carabids from the Natural History Museum London and extracted crops from the scanned images. This database contain 63.364 specimens that we used to train, validate and test a convolutional neural network.</p> <p>Each folder is named as the gbif id number. E.g. Carabus problematicus is 4470555: https://www.gbif.org/species/4470555</p>
One-Way Wave Propagator driven by pure visual neural network
<p>this repository is used to reproduce the key Figures of our manuscript titled "One-Way Wave Propagator driven by pure visual neural network". </p> <p>The code is running using MATLAB, Recommend using a GPU device.</p>
A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms
<p>The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.</p> <p> </p> <p>The code has been published on GitHub: <a href="https://github.com/cqtian-kk/Lightning_Detection_Location">Lightning_Detection_Location</a> or <a href="https://zenodo.org/records/14048427">DOI 10.5281/zenodo.13350849</a></p>
Reproduction Package for Submission `A Literature Review on Verification and Abstraction of Neural Networks'
<div> <div>This artifact contains the aggregated data for the article "A Literature Review on Verification and Abstraction of Neural Networks".</div> <br> <div>The artifact contains the following data files:</div> <br> <div>|-- phase-1_search-space.csv</div> <div>|-- phase-2-3_process-title-abstract.csv</div> <div>|-- review_sheet.csv</div> <br> <h3>Search space for literature review</h3> <div>The file <code>phase-1_search-space.csv</code> contains the metadata of the papers published in our considered conferences during the observed years.</div> <br> <h3>Filtering process</h3> <div>In the file <code>phase-2-3_process-title-abstract.csv</code>, we include the files that contain our expected keywords.</div> <div>Depending on whether we decided to include the paper in our survey after reading the title and the abstract, we marked each of the papers with `accept` or `decline`.</div> <br> <h3>Review sheet</h3> <div>The file <code>review_sheet.csv</code> contains metadata describing our reviews for each of the publications.</div> <div>It holds information on our final review of each paper, including the group we assigned it to and the reasons for its exclusion.</div> <br> <h3>Generating numbers</h3> <div>The following commands can be executed to reproduce the numbers used in our literature review.</div> <br> <div><code># Change directory to the directory containing the CSV files of this artifact.</code></div> <br> <div><code># Number of papers in our search space.</code></div> <div><code>cat phase-1_search-space.csv | tail -n +2 | wc -l</code></div> <br> <div><code># Number of papers after keyword search.</code></div> <div><code>cat phase-2-3_process-title-abstract.csv | tail -n +2 | wc -l</code></div> <br> <div><code># Number of papers that are excluded based on abstracts and titles.</code></div> <div><code>cut -f9 phase-2-3_process-title-abstract.csv | tail -n +2 | sort | uniq -c</code></div> <br> <div><code># Number of papers in different classes.</code></div> <div><code>cut -f7 review_sheet.csv | tail -n +2 | sort | uniq -c</code></div> <div> </div> </div>
Supplementary data (CC BY-NC-SA 4.0): Germanium Distributions in Zeolites Derived from Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <p>This dataset contains supplementary data related to the Germanosilicate Project titled <br>"Germanium Distributions in Zeolites Derived from Neural Network Potentials". <br>In various subfolders, it hosts database, simulations and post-processing calculations.<br><br>Below is a brief overview of each subfolder:</p> <ol> <li><em>Post_Processing_Calculation:</em> <br>- Contains scripts and data for post-processing calculations such as coordination numbers, <br>- Pair distribution function, and various germanium distribution metrics.<br><br></li> <li><em>NNP_Simulation_DATA:</em> <br>- Stores simulation data for different zeolite structures along with setup files for neural network potentials (NNP) simulations.<br><br></li> <li><em>NNP_files_database</em>: <br>- NNP_files: NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)<br>- GeSiO_training.db: DFT (PBE+D3(BJ)) training database as SchNetPack1.0 database file readable by <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment (ASE)</a> and SchNetPack version 1.0<br><br><em> </em></li> <li><em>DFT_vs_NNP_Data:</em> <br>- ASE traj files storing structures subsampled from MCBH runs along with energies/forces at the PBE+D3(BJ) ("*_dft.traj") and NNP level (*_nnp.traj)<br><br></li> <li><em>Zeolite_Structurers_ALL</em>: <br>- Holds data for various zeolite structures, including optimized structures for both single-cell and supercell configurations.<br><br></li> <li>GSOs_DATA:<br>- The unoptimised Global Structure Optimas (GSOs) are provided<br>- Optimised GSOs are stored inside folders for both various DFT and NNP methods</li> </ol> <p> <br>Please refer to individual readme files in each subfolder for more detailed information.</p>
Data Release: "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function"
<p>This dataset contains results presented in "<strong>A neural network emulator of the Advanced LIGO and Advanced Virgo selection function</strong>" (<a href="https://www.arxiv.org/abs/2408.16828">arXiv: 2408.16828</a>).</p> <p>The code used to generate this data and produce figures in the paper can be found at <a href="https://github.com/tcallister/learning-p-det/">https://github.com/tcallister/learning-p-det/</a>. Specific instructions about the workflow are provided in the <a href="https://tcallister.github.io/learning-p-det/">accompanying documentation</a>.</p> <p>The primary deliverable of this work is a trained neural network emulator for the compact binary selection function during the Advanced LIGO and Advanced Virgo O3 observing run. This emulator is made available in a standalone companion repository, <a href="https://github.com/tcallister/pdet">https://github.com/tcallister/pdet</a>.</p> <p>Additional information:</p> <ul> <li>The files <em>endo3_bbhpop-LIGO-T2100113-v12.hdf5</em>, <em>endo3_bnspop-LIGO-T2100113-v12.hdf5</em>, and <em>endo3_nsbhpop-LIGO-T2100113-v12.hdf5</em>, used for network training, were created and released by the LIGO-Virgo-KAGRA Collaboration at <a href="../records/7890437">https://zenodo.org/records/7890437</a>.</li> <li>The file <em>sampleDict_FAR_1_in_1_yr.pickle</em>, used during hierarchical inference, was created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a>.</li> <li>Inference results (<em>popsummary_standardInjections.h5</em> and <em>popsummary_dynamicInjections.h5</em>) are provided in the <em>popsummary</em> results format; see <a href="https://git.ligo.org/christian.adamcewicz/popsummary">https://git.ligo.org/christian.adamcewicz/popsummary</a>.</li> </ul> <p>Changelog:</p> <ul> <li>v2: Added missing file <em>sampleDict_FAR_1_in_1_yr.pickle</em></li> </ul>
Fourier-neural-networks
<p>This library contains the data set used in the article ' Fast geophysical forward modeling simulation using transfer learning in the Electromagnetic method ' and the code of the Fourier neural network. The Fourier Neural Operator for 2D problems such as the Darcy Flow is discussed in Section 5.2 in the [paper](https://arxiv.org/pdf/2010.08895.pdf). And the original code can be downloaded from <a href="https://github.com/neuraloperator/neuraloperator">neuraloperator/neuraloperator: Learning in infinite dimension with neural operators. (github.com).</a></p>
Dataset for the article "A deep equivariant neural network approach for efficient hybrid density functional calculations"
<p>Dataset files for DeepH-hybrid, containing structures and preprocessed Hamiltonian matrices computed with HSE06 hybrid functional and ABACUS DFT package.</p> <p>Enclosed include:</p> <ol> <li>The four datasets corresponding to the four DeepH-hybrid models mentioned in DeepH-hybrid's paper, each containing material structures and electronic structure properties of </li> <li>Basis set files for the ABACUS DFT package</li> <li>Snapshot of a version of the additional codes utilized to preprocess hybrid DFT Hamiltonians (also available via this <a href="https://github.com/aaaashanghai/DeepH-hybrid">GitHub link</a>)</li> </ol> <p>Note only the additional codes of DeepH-hybrid is provided, and it may be used in combination with DeepH-E3 package for neural-network training. For additional details please refer to the "README" file of the code.</p>
Latent space active learning with message passing neural network: the case of HfO_{2}
<div>A dataset containing the structures obatined from the active learning process and their calculated energies and forces. See more details in our papr "Latent space active learning with message passing neural network: the case of HfO_{2}".</div>
Transferability of Atom-Based Neural Networks Datasets
<p>This repository contains the datasets used in our article "Transferability of Atom-Based Neural Networks".</p> <p>The datasets are in the extxyz format, which can be used to train NequIP models. </p> <p>The extended xyz files contain all necessary information for the training of the models. The first four columns contain the xyz representation (Ångstrom), while the last column contains the atomic energies (kcal/mol), computed using either the AO-based EDA or MO-based IBO/IAO decomposition scheme. The total atomization energy (kcal/mol) is in the line below the number of atoms.</p> <p>The files are named as {dataset}_{decomp}.xyz.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
Exponential Synchronization of BAM Neural Networks with Delay on Arbitrary Time Domains
<p>Code to reproduce the plots presented in our manuscript ``Exponential Synchronization of BAM Neural Networks with Delay on Arbitrary Time Domains''. </p>
Battery Pack Temperature Change Prediction for Running BLDC 1500 W Motor using Artificial Neural Network
<p><span>The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model's effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems.</span></p>
Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"
<p>This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.</p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</p>
Data from: StomataCounter: a neural network for automatic stomata identification and counting
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user friendly, fully-automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.
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.