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129 results for “Deep Neural Networks”

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zenodo40/100

Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks

The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal in operando spectroscopies and automated control for the manipulation of nanoscale structures in materials.

opencc-by-4.0Nov 2018View details →

zenodo40/100

Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria

This project contains the following underlying data: <h3>      Software-Related Files</h3>                ·         BacLABNet_script.ipynb (Deep Learning Neural Network for classification of Bacteriocin Sequences)                 ·         embed_proteins.py (Recurrent Neural Network to obtained the embedding vectors)                ·         model_I22.h5 (This file contains the trained weights of the trained model)                ·         model_I22.json (This file contains the structure of the trained model)                ·         rnn_gru.pt (Initial weights of the Recurrent Neural Network to obtain embedding vectors)                ·         List_kmers.csv (List of 5-mers and 7-mers obtained from dataset after it filtered sequences shorter than 50 aa and                           longer than 2000 aa)                     Files Used for Training, Testing, and Validation of the Neural Network                ·         data_nonBacLAB.csv (25000 nonBacLAB amino acid sequences retrieved from Uniprot)                ·         data_BacLAB.csv (24964 BacLAB amino acid sequences retrieved from Uniprot)              Additional Files                 ·         data_BacLAB_and_nonBacLAB.csv (Combination of sequences from data_BacLAB.csv and data_nonBacLAB.csv)                  ·         all k.mers list.xlsx (Table of all k-mers obtained for k=3,5,7,15,20)   Note: Codes are additionally available on GitHub.  Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication)(http://creativecommons.org/publicdomain/zero/1.0/)

opencc-zeroJul 2024View details →

zenodo40/100

Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

This repository contains: <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> Summary: The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted. The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals.  Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3. The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing. For more details, please contact me (wotp12@unist.ac.kr)

opencc-by-4.0Aug 2024View details →

zenodo40/100

A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins

Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding.  More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.

opencc-by-4.0Jan 2023View details →

zenodo40/100

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

Short Description: In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone. Code and More Details: https://github.com/lepotatoguy/aqi

opencc-by-4.0Apr 2023View details →

zenodo40/100

Bone Age Assessment from Articular Surface and Epiphysis using Deep Neural Networks

This is our research dataset of "Bone Age Assessment from Articular Surface and Epiphysis in Hand Radiography using Deep Neural Networks"

opencc-by-4.0May 2023View details →

zenodo40/100

Supporting data for "Reliable interpretability of biology-inspired deep neural networks"

Contents data.tgz contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder data. The archive contains the following subfolders: <ul> <li>dtox DTox results, one subfolder per seed <ul> <li>module_relevance.tsv: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li>test_labels.csv: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)  </li> </ul> </li> </ul> </li> <li>mskimpact_[cancer type]_[experiment] P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer) [experiment] is one of original (original setup) and shuffled (shuffled labels)  </li> <li>pnet_[experiment] P-NET results using the original (prostate cancer) dataset, one subfolder per seed [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li>node_importance.csv: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li>predictions_test.csv: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li>predictions_train.csv: predictions for the training set (same columns as above)</li> <li>link_weights_[layer].csv: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul>   Changelog v1.1.0  – 2023-06-28 <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> v1.0.0 – 2023-03-22 <ul> <li>initial release</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo36/100

Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

<pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>

opencc-by-4.0Apr 2020View details →

dryad36/100

Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling

Deep neural networks (DNNs) are powerful machine learning models that are widely used for classification problems, and have been recently proposed for quartet tree phylogeny estimation (Survorov et al. Systematic Biology 2020 and Zou et al. Molecular Biology and Evolution 2020). Here we present a study evaluating recently trained DNNs (from Zou et al., MBE 2020) in comparison to a collection of standard phylogeny estimation methods, including UPGMA, neighbor joining, maximum parsimony, and maximum likelihood, on a heterogeneous collection of 20-sequence datasets simulated under the same models that were used to train the DNNs, and also under similar conditions but with higher rates of evolution. Our study shows that using DNNs with quartet amalgamation (to combine quartet trees into a tree on the full dataset) is only more accurate than UPGMA, and otherwise is less accurate than all standard phylogeny estimation methods we explore (maximum likelihood, neighbor joining, and maximum parsimony). We further find that while DNNs can provide good quartet tree accuracy, some standard phylogeny estimation methods match or improve on DNNs for quartet accuracy, especially, but not exclusively, when used in a global manner (i.e., the tree on the full dataset is computed and then the induced quartet trees are extracted from the full tree). Thus, our study provides evidence that a major challenge impacting the utility of current DNNs for phylogeny estimation is their restriction to estimating quartet trees which must subsequently be combined into a tree on the full dataset: in contrast, global methods -- i.e., those that estimate trees from the full set of sequences -- are able to benefit from taxon sampling, and hence have higher accuracy on large datasets.

opencc-zeroAug 2020View details →

zenodo36/100

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data

Data from the paper: Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations. Preprint: https://www.biorxiv.org/content/10.1101/840256v1 Marek A. Pedziwiatr marek.pedziwi@gmail.com September 2020

opencc-by-4.0Sep 2020View details →

zenodo36/100

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

Dataset and code used in V Wåhlstrand-Skärström, et al, "DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks", published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks. From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and  experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.

opencc-by-4.0Nov 2020View details →

zenodo36/100

Glottis Analysis Tools - Deep Neural Networks

Netron Overview Diagrams of Deep Neural Networks (DNNs) shipped with Glottis Analysis Tools (GAT) 2020.

opencc-by-4.0Dec 2020View details →

zenodo36/100

Extra-P Version Used for Noise-Resilient Empirical Performance Modeling with Deep Neural Networks

This is the Extra-P source code that was used for the analysis and evaluation of the IPDPS 2021 paper "Noise-Resilient Empirical Performance Modeling with Deep Neural Networks". It also contains the checkpoints and saved models for the DNN part of the adaptive modeler as well as the gathered synthetic evaluation data.

opencc-by-4.0Nov 2024View details →

zenodo36/100

Latent space images and related deep neural network for the BAGLS dataset

In this repository, we provide the latent space images for the BAGLS (<a href="https://www.nature.com/articles/s41597-020-0526-3">Gómez, Kist et al., Sci Data 2020</a>, available at <a href="https://bagls.org/">www.bagls.org</a>) training dataset. We further provide a pre-trained deep neural network for glottis segmentation having only a single latent space, i.e. a latent space image, and no skip connections.

opencc-by-4.0Dec 2021View details →

zenodo36/100

A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment

The DP-HYB and DP-SE2potential and the W training database.

opencc-by-4.0Apr 2022View details →

zenodo36/100

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Benchmark data sets of CDPred as described in Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks Zhiye Guo1, Jian Liu1, Jeffrey Skolnick2, Jianlin Cheng1* 1 Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 2 School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000 *Corresponding author (chengji@missouri.edu) There is four test dataset in this package, each test dataset contains four different folders and one list file. The afpred_pdb includes all the corresponding monomer structures predicted by alphafold. The cdpred_output includes the prediction results of our tool CDPred for each dataset. The pre_gen_a3m includes the multiple sequence alignments file used by CDPred to generate prediction results. And the true_pdb includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure. HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13 HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14 HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14 HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021

opencc-by-4.0Jun 2022View details →

dryad36/100

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. 3. The networks could predict the location of future forest loss to a high degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.

opencc-zeroJul 2022View details →

zenodo36/100

The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data

Dataset used in the research presented in the article: Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" Materials 15, no. 10: 3727. https://doi.org/10.3390/ma15103727 The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.

opencc-by-4.0Jul 2021View details →

zenodo36/100

Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"

Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".

opencc-by-4.0Sep 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record