Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
921
datasets available to search
ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks
<p>This dataset relates to the publications:</p> <p><a href="https://doi.org/10.1038/s41467-024-49378-8">Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.</a> <span>Karunanithy G, Shukla VK, Hansen DF<strong>. </strong></span><span>Nat Commun. 2024 Jun 13;15(1):5073. doi: 10.1038/s41467-024-49378-8.</span></p> <ul> <li>Training data for Deep Neural Networks developed in the manuscript "Solution-State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks"</li> <li>Experimental cross-validation data: <ul> <li>2D spectra of HDAC8, MSG, and a7a7 proteasome</li> <li>3D NOESY spectra of MSG</li> </ul> </li> </ul> <p>Please see GitHub (https://github.com/gogulan-k/FID-Net) for additional scripts and details. </p>
Applying High-Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network
Open the record for dataset details and reuse information.
Data and Software: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted SFR Assemblies using a Convolutional Neural Network
<p>Datasets and code used in upsampling OpenMC SFR simulation neutron flux tallies.</p>
Neural network for predicting Peierls barrier spectrum and its influence of dislocation motion
Open the record for dataset details and reuse information.
Deep Neural Network Surrogate for Surface Complexation Model of Metal Oxide/Electrolyte Interface
<p>These files are the data used in the paper "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ncAYQ4MAAAAJ&sortby=pubdate&citation_for_view=ncAYQ4MAAAAJ:LkGwnXOMwfcC">Deep neural network surrogate for surface complexation model of metal oxide/electrolyte interface</a>".</p> <ul> <li>CSV files are used to train the DNN model.</li> <li>NPZ files are used to train the random forest model. </li> </ul>
Part of the dataset and trained models in "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media" by Meng et al. in Geophysical Research Letters
<p><strong>This repository is created to contain part of the data, codes and trained models in the research project titled "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media".</strong></p> <p> </p>
Data and codes: Automated estimation of bioturbation intensity and ichnodiversity from the core section image using convolutional neural network
Open the record for dataset details and reuse information.
An Intelligent Java Method Name Recommendation Framework via Two-phase Neural Networks
<p>The datasets contain new-100、new-400 and dataset used in rq4.</p>
Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
Open the record for dataset details and reuse information.
dataset where a Convolutional Neural Network controls a part of the TEM optics and OAM sorter
<p>Here are uploaded the data collected when we succesfully connected a Convolutional Neural Network to the Holo-TEM in Julich. In particular the CNN controls a part of the TEM optics (beam shift x and y, C3 intensity) and the OAM sorter (the Sorter1 and Sorter 2 bias, in particular it is able to change the S1 bias to perfectly match the phase profile generated from the second sorting element).</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</p>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
<p>This repository includes the input and output dataset, and python scripts used in the article, "Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)," of J. Chem. Phys. 156, 154108 (2022) [DOI: <a href="http://doi.org/10.1063/5.0087310">10.1063/5.0087310</a>] The repository also includes source data of figures in the article.</p>
Data of Symbolic Quantitative Verification of Quantized Neural Networks
<p>Experimental Results for the Symbolic Quantitative Verification of Quantized Neural Networks Paper</p>
Research Artifact for paper "Decompiling x86 Deep Neural Network Executables"
<p>Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables"</p>
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
<p>Dataset to the paper 'Robust and fast post-processing of single-shot spin qubit detection events with a neural network'</p>
Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code Using Deep Neural Networks
<p><strong>Checkout our Github-Repo for more information, issues, and pull requests: </strong></p> <p><a href="https://github.com/Taremeh/predicting-code-comprehension-eye-tracking/">https://github.com/Taremeh/predicting-code-comprehension-eye-tracking/</a></p> <p> </p> <p>Dataset and Replication Package for our paper "Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code Using Deep Neural Networks"</p>
Data for Evaluating the generalizability of graph neural networks for predicting collision cross section
Open the record for dataset details and reuse information.
Data for "Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad7c14" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citation is:</p> <p>Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 215006</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Ground Energy + Fluctuations</li> <li>Misc</li> <li>Quantum Constraint</li> <li>Volume</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Ground Energy + Fluctuations" contains the data used in Table 1 and Table 2 in the paper while the "Volume" directory contains the data used in Section 4.3 of the paper).</p> <p>Some of these parent directories, which involve simulations solving constraints, contain within them several sub-directories (child directories) corresponding to different produced data. The raw data of the simulation can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>.</p> <p> </p> <p>The dictionary will have <em>at least one</em> parent key called "Energy". The data in the "Energy" key corresponds to the data being minimised. The data in any other parent key correspond to operators which were being observed during the simulation. For example, in the data in the "Quantum Constraint" directory, some .json files will have multiple parent keys such as FG, H, HG, .... Each of these keys correspond to different operators which were observed during that simulation. Each parent key is yet another dictionary in itself. The structure of the dictionaries corresponding to any parent key are always the same and always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code> (or alternatively <code>data["FG"]["Mean"]</code> if you wish to observe the value of the F + G operator during the simulation). The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p><em><strong>The files for the variational arrays are too large to be uploaded to a general repository hosting service. Therefore, they will be provided directly upon request in a direct download link. Please contact the author of the paper (Waleed Sherif, email: waleed.sherif@fau.de) for accessing the data. </strong></em></p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>4.4 Probabilities</h2> <p>The "Misc/Probabilities" directory contains <code>.npy</code> files which should be handled in the same manner as the variational states. These files correspond to the probability simulations conducted in section 4.4.4 in the paper.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></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.