Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Dataset3 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"

Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian".  Contains atomic structures and Hamiltonian matrices of bilayer bismuth telluride.  Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.

opencc-by-4.0Apr 2023View details →

Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"

These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are: - Python code to construct a neural network model - Saved optimal models (for 8-fold validation) - Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME

Neural network potential for H/Ru(0001).

Neural network potential for H atoms on static Ru(0001) surface in PROPhet (https://github.com/biklooost/PROPhet) LAMMPS format.

SlumberNet: Deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.

Example testing images for PFU detection neural network

This is the example testing images used for the paper of "Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning". The data has been compressed into eight zip files from .001 to .008. To access the data, please follow these steps: 1) Download all eight zipped files. 2) Once the download is complete, click on any one of the zipped files to start the extraction process. 3) The extracted files will automatically be organized into one folder. After extraction, there are three subfolders inside it: 1) Network input: it contained 4 holographic phase images (.mat files) at 12h, 13h, 14h and 15h of incubation for an example postive well and an example negative well. 2) Network output: It contains the network output image (PFU probability map) fot the example postive well and the example negative well. 3) Detection result: It contains the final binary detection result images after thresholding fot the example postive well and the example negative well.

openother-openMay 2023View details →

YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning

This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.

Data of "Multilayer spintronic neural networks with radio-frequency connections"

This dataset corresponds to the open data of the publication "Multilayer spintronic neural networks with radio-frequency connections".

Alkane dataset for reactive chemistry neural network potentials at DFT and CASPT2 level

datasets used for training and evaluating neural networks in the following work: Neural Network Potentials for Reactive Chemistry: CASPT2 Quality Potential Energy Surfaces for Bond Breaking <a href="https://doi.org/10.26434/chemrxiv-2023-13cv6">https://doi.org/10.26434/chemrxiv-2023-13cv6</a>

Reproducibility Package for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding

This archive contains the repro-pack for the paper at <a href="http://github.com/barbagroup/jcs_paper_pinn">https://github.com/barbagroup/jcs_paper_pinn</a>.

Reproducibility Package (Raw Data) for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding

Raw data results from the simulations using PINN methods and PetIBM for the paper at: https://github.com/barbagroup/jcs_paper_pinn

opencc-zeroJun 2023View details →

dryad36/100

Data for: Prediction in cultured cortical neural networks

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing, and is believed to be involved in motor and cognitive control and decision making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short and long-term memory. To answer these questions we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.

Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE

Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a> This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE" You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format. Acknowledgments The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.

Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"

This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper. The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks. Where to Start The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'.  The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file. For more information, please refer to 'README.txt'.

Data of "Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks"

The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.48550/arXiv.2304.11209">https://doi.org/10.48550/arXiv.2304.11209</a>.

Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach

# Emission-Aware Optimization of Gas Networks   This repository collects the Belgium gas network dataset, details on the training procedure, and codes to replicate the results reported in the following paper:   *Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach *   accepted for presentation at the 62nd IEEE Conference on Decision and Control, Dec. 13-15, 2023, Singapore.   Materials are released with the Attribution 4.0 International (CC BY 4.0) license.   The repository contains two folders: * ```operation_planning``` folder containing data and codes for neural network training and operation planning optimization  * ```long_term_planning``` folder containing data and codes for neural network training and long-term planning optimization    The models are implemented in ```Julia-1.6``` Language, using ```JuMP.jl``` using ```Flux.jl``` library for machine learning and JuMP.jl library for mathematical programming. Before running the code, make sure to activate the virtual environment from ```Project.toml``` files stored in each folder, e.g., by running  ``` julia> ] (@v1.6) pkg> activate . (operation_planning) pkg> instantiate ``` For experiment settings, refer to ```exp_settings``` dictionary in file ```main.jl```. For network data, refer to ```.../data/case_BE```.

opencc-by-4.0Aug 2023View details →

Supplemental data for characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks

This is the supplemental data for the manuscript titled Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks submitted to Nano Select. Motivation: Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks. Authors and funding: The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1. Dataset description: Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder. <ul> <li>particle_detection_training.ipynb: This notebook can be used for network training.</li> <li>particle_detection_evaluation.ipynb: This notebook is for evaluation of a trained network with simulated test images.</li> <li>particle_detection_evaluation_experiment.ipynb: This notebook is for evaluation of a trained network with experimental test images.</li> <li>particle_detection_measurement_experiment.ipynb: This notebook is for application of a trained network to experimental data.</li> </ul> In addition, a script titled particle_detection_functions.py is provided which contains functions required by the notebooks. Details can be found within the comments. The zip archive training_data.zip contains the training data. The subfolder HAADF contains the images (sorted as training, validation and test images), the subfolder json contains the annotation (sorted as training, validation and test images). Each file within the json folder provides for each image the following information: <ul> <li>aggregat_no: image id, the number of the corresponding image file</li> <li>particle_position_x: list of particle position x-coordinates in nm</li> <li>particle_position_y: list of particle position y-coordinates in nm</li> <li>particle_position_z: list of particle position z-coordinates in nm</li> <li>particle_radius: list of volume equivalent particle radii in nm</li> <li>particle_type: list of material types, 1: TiO2, 2: WO3</li> <li>particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</li> <li>rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</li> <li>deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</li> <li>cluster_index: list of cluster indices for each particle</li> <li>initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</li> <li>fractal_dimension: the intended fractal dimension of the aggregate</li> <li>fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</li> <li>fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</li> <li>fractal_prefactor: fractal prefactor</li> <li>mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles)</li> <li>mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles)</li> <li>mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume)</li> <li>mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight)</li> <li>particle_1_rho: density of TiO2 used for the calculations</li> <li>particle_1_size_mean: mean TiO2 radius</li> <li>particle_1_size_min: smallest TiO2 radius</li> <li>particle_1_size_max: largest TiO2 radius</li> <li>particle_1_size_std: standard deviation of TiO2 radii</li> <li>particle_1_clustersize: average TiO2 cluster size</li> <li>particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters</li> <li>particle_2_rho: density of WO3 used for the calculations</li> <li>particle_2_size_mean: mean WO3 radius</li> <li>particle_2_size_min: smallest WO3 radius</li> <li>particle_2_size_max: largest WO3 radius</li> <li>particle_2_size_std: standard deviation of WO3 radii</li> <li>particle_2_clustersize: average WO3 cluster size</li> <li>particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters</li> <li>number_of_primary_particles: number of particles within the aggregate</li> <li>gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</li> <li>gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</li> <li>mean_coordination: mean total coordination number (particle contacts)</li> <li>mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</li> <li>mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</li> <li>radius_equiv: list of area equivalent particle radii (in projection)</li> <li>k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</li> <li>polygons: list of polygons that surround the particle (COCO annotation)</li> <li>bboxes: list of particle bounding boxes</li> <li>aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</li> <li>n_pix: number of pixel per image in horizontal and vertical direction (squared images)</li> <li>pixel_size: pixel size in nm</li> <li>image_size: image size in nm</li> <li>add_poisson_noise: 1 if poisson noise was added, 0 otherwise</li> <li>frame_time: simulated frame time (required for poisson noise)</li> <li>dwell_time: dwell time per pixel (required for poisson noise)</li> <li>beam_current: beam current (required for poisson noise)</li> <li>electrons_per_pixel: number of electrons per pixel</li> <li>dose: electron dose in electrons per Å2</li> <li>add_scan_noise: 1 if scan noise was added, 0 otherwise</li> <li>beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</li> <li>scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)</li> <li>add_focus_dependence: 1 if a focus effect is included, 0 otherwise</li> <li>data_format: data format of the images, e.g. uint8</li> </ul> There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training. The zip archive experiment_test_data.zip includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder HAADF includes thirteen images. The subfolder json includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in json/COCO/annotation_COCO.json. The zip archive experiment_measurement.zip includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript. The zip archive particle_detection.zip includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file particle_detection/logs/fit/20230622-222721/iter_60000.pth. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as particle_detection/logs/fit/20230622-222721/config_file.py. There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening. The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.