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921
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ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"
<p>Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: <a href="https://doi.org/10.5281/zenodo.8387558">10.5281/zenodo.8387558</a></p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
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Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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Data from: Dorsoventral comparison of intraspecific variations in the butterfly wing pattern using a convolutional neural network
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Data from: Multiplexed subspaces route neural activity across brain-wide networks
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Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations
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Using convolutional neural networks to efficiently extract immense phenological data from community science images
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Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect
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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
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Data for: Brain control of bimanual movement enabled by recurrent neural networks
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BOLD5000 Additional ROIs and RDMs for neural network research
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Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks
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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>
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
<p>Three temporal graph datasets for node classification under distribution shift.</p> <p>DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset.</p> <p>Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio).</p> <p>The task is to classify the vertices of the graph into the respective conference/journal venues (DBLP) or journal categories (PharmaBio). In the DBLP datasets, new classes may appear over time.</p> <p>Each dataset follows the structure:</p> <p>- adjlist.txt -- the graph structure encoded as adjacency lists: in each row, the first entry is the source vertex, the remaining entries are adjacent vertices</p> <p>- X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt</p> <p>- y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt</p> <p>- t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt</p> <p>A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv: <a href="https://arxiv.org/abs/2006.14422">https://arxiv.org/abs/2006.14422</a>).</p> <p>If you use these datasets in your research, please cite the corresponding paper:</p> <pre><code>@inproceedings{galke2021lifelong, author={Galke, Lukas and Franke, Benedikt and Zielke, Tobias and Scherp, Ansgar}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={Lifelong Learning of Graph Neural Networks for Open-World Node Classification}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/IJCNN52387.2021.9533412} }</code></pre> <p><br> </p>
Tagged original datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged, non-formatted, original datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems', T. Murovič, A. Trost.</p> <p>Available from the original authors:</p> <p>1. <a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a> (UNWS-NB15 dataset)</p> <p>2. <a href="https://www.unb.ca/cic/datasets/nsl.html">https://www.unb.ca/cic/datasets/nsl.html</a> (NSL-KDD dataset)</p>
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
<p>Deep neural networks have shown promising success towards the classification and retrieval tasks for images and text data. While there have been several implementations of deep networks in the area of computer graphics, these algorithms do not translate easily across different datasets, especially for shapes used in product design and manufacturing domain. Unlike datasets used in the 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D models do not yield themselves to neat distinct classes. The current study looks at an improved form of the 3D shape deep learning algorithm for classification and retrieval through the use of techniques such as relaxed classification, use of prime angled camera angles for capturing feature detail and transfer learning for reducing the amount of data and processing time needed to train shape recognition algorithms. The proposed algorithm (MVCNN++) builds on top of multi-view convolutional neural network (MVCNN) algorithm, improving its efficacy for manufacturing part classification by enabling use of part metadata, yielding an improvement of almost 6% over the original version. With the explosive growth of 3D product models available in publicly available repositories, search and discovery of relevant models is critical to democratizing access to design models.</p>
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.
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
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.