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.
558
datasets available to search
ShareScore release 0.9.0
Dataset results
558 results for “Training Data”
Training data for the GitHub repository "buildingsFromSentinel"
<p>Training and testing data for machine learning models predicting the building height and footprint from satellite data in urban areas.</p> <p>Sentinel-1 and -2 data are retrieved from https://scihub.copernicus.eu/ and the GHS built-up grid (here GHSBuilt10) from https://ghsl.jrc.ec.europa.eu/download.php?ds=buS2. GHSBuilt10 is derived from Sentinel-2 global image composite for the reference year 2018 using Convolutional Neural Networks (GHS-S2Net).</p> <p>The dataset contains the following folders:</p> <ul> <li>footprint: PNG images over urban areas with either three or four features: <ul> <li>XXX_labels.png: true-colour images (TCI) retrieved from Sentinel-2 data</li> <li>XXX_labels4.png: TCIs with the band 8 (i.e., near-infrared = NIR) as the fourth dimension in the image.</li> </ul> </li> <li>height: data for different cities <ul> <li>building_height.tif: real building height (only for the training data)</li> <li>sentinel_cropped: satellite images for the same area. Contains Sentinel-1 and -2 data as well as the GHS-Built data with a 10-m resolution.</li> <li>README.txt: information of the origin of the building height data</li> </ul> </li> </ul>
Training Data for NN-WIFF (Horvat and Roach, 2021)
<p>Contains datasets used to train the neural network implementation of WIFF1.0 (github.com/chhorvat/WIFF-Model/). </p> <p>Three files:</p> <p>1) Training_converged.mat - this is a matlab file containing in and out, which are the input and output vectors used to train the NN-WIFF networks. </p> <p>2) 6hourly.zip - contains files used to create training_converged.mat. These are 6-hourly snapshots of sea ice thickness and concentration in 2009 CICE simulations driven by ocean surface waves. </p> <p>3) 6hourlyfrachist.zip - contains files used to create training_converged.mat. These are 6-hourly snapshots of converged (SP-WIFF) fracture histograms in 2009 CICE simulations driven by ocean surface waves. </p>
DEEPICE data training - tutorial - "A little journey in Zenodo"
<p>This tutorial aims to explain how DEEPICE project members can deposit their data on Zenodo. It recalls the specific recommendations in terms of metadata and data description, and describes the steps of the deposit process.</p>
NeuCA Shiny App sc-RNA Training Data
<p>Data to train classifiers for NeuCA shiny app.</p>
GEBCO train data
<p>EDSR training data GEBCO (Land and Ocean DEM inclouded)</p>
Training data and prediction results for predicting water diffusion in various polymers using MD-GAN
<p>Training data and prediction results for predicting water diffusion in various polymers using MD-GAN.</p> <p>MD trajectories used as input, MSDs calculated from MD, predicted MSDs, and diffusion coefficient values.</p> <p>MD trajectories were obtained from the following paper.<br> Kojima, H.; Handa, K.; Yamada, K.; Matubayasi, N. Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions. <em>The Journal of Physical Chemistry B</em> 2021, 125, 9357-9371.</p> <p> </p>
APARENT2 Training Data and Models
<p>Processed training data for the APARENT2 model (measurements from the random MPRA and designed oligo pool originally published by Bogard et al., 2019; see https://doi.org/10.1016/j.cell.2019.04.046 for reference). This repository also contains the APARENT2 model file. For more information on the training procedure, see the <em>Genome Biology</em> article "Deciphering the impact of genetic variation on human polyadenylation using APARENT2" (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02799-4). Two versions of the model are available:</p> <p>(a) aparent_all_libs_resnet_no_clinvar_wt_ep_5.h5: The originally trained APARENT2 model.<br> (b) aparent_all_libs_resnet_no_clinvar_wt_ep_5_var_batch_size_inference_mode_no_drop.h5: Identical weights and predictions as model (a), but the normalization layers have been set to inference mode and the dropout layers have been removed (thus making it compatible with the scrambler pipeline).</p>
Deep learning training data (JOVE)
<p>Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. However, the technique has several limitations that make analyzing the data it generates time-intensive and difficult. Hand-segmenting a single tomogram can take hours to days of human effort, but the microscope can easily generate 50 or more tomograms a day. Current deep learning segmentation programs for cryo-ET do exist but are limited to segmenting one structure at a time. Here multi-slice U-Net convolutional neural networks are trained and applied to automatically segment multiple structures simultaneously within cryo-tomograms. With proper preprocessing, these networks can be robustly inferred to many tomograms without the need for training individual networks for each tomogram. This workflow dramatically improves the speed with which cryo-electron tomograms can be analyzed by cutting segmentation time down to under 30 min in most cases. Further, segmentations can be used to improve the accuracy of filament tracing within a cellular context and to rapidly extract coordinates for subtomogram averaging.</p>
PASTA-ice sea ice image classification: calibration files and training data
<p>PASTA-ice is a Python-based classification algorithm for aerial sea-ice images. The data set contains calibration files for the CANON EOS-1D Mark III cameras that were implemented in helicopters and POLAR aircraft of the Alfred-Wegener-Institute. Furthermore, it contains training data of labeled sea ice surfaces observed during RV Polarstern cruise PS106 that can be used to train the implemented random forest classifier. Files with extension "sediments" were extended with exemplary data of sediment-loaden snow at the MOSAiC expedition. </p> <p>The PASTA-ice algorithm is available under: <a href="https://github.com/nielsfuchs/pasta_ice">https://github.com/nielsfuchs/pasta_ice</a></p>
Youngs Modulus Prediction tfrecord Training Data
<p>Tfrecords containing a microstructure and its youngs modulus simulated along the y-axis. The data_definition.json that explicitly describes the content of the tfrecords is also contained within the zip file.</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
MemBrain-seg training data
<p>This dataset contains training data for segmenting membranes in cryo-electron tomograms.</p> <p>More details will follow.</p>
Reference-based RNA-seq data analysis (training data)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes RNA-Seq data from a study published by <a href="http://genome.cshlp.org/content/21/2/193.long">Brooks <em>et al.</em> 2011</a> to identify genes and exons that are regulated by Pasilla gene.</p>
Automated patent extraction powers generative modeling in focused chemical spaces: Training data and model checkpoints release
<p>Training data and model checkpoints accompanying paper on "Automated patent extraction powers generative modeling in focused chemical spaces". If you use this data, please cite the following manuscript:</p> <pre>@article{subramanian2023automated, title={Automated patent extraction powers generative modeling in focused chemical spaces}, author={Subramanian, Akshay and Greenman, Kevin P and Gervaix, Alexis and Yang, Tzuhsiung and G{\'o}mez-Bombarelli, Rafael}, journal={Digital Discovery}, year={2023}, publisher={Royal Society of Chemistry} }</pre>
Vacuole fusion in fluorescent images training data
<p>Fluorescent image training data for classification tasks associated with the paper ‘<strong><em>Automated quantification of vacuole fusion and lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning</em></strong>’ https://doi.org/10.1101/2023.02.27.530171</p> <p>All images contain a ROI with FM4-64 and BODIPY 493/503 channels. Images are placed in folders corresponding to their respective classes.</p>
Training Data for EM Segmentation in SPonge
<p>Contains training data for semantic and instance segmentation of cells, cilia and microvilli in the EM volume of a sea-sponge. The data comes from the volume in https://doi.org/10.1126/science.abj2949.</p>
Data for training and testing the PIO-Net.
<p>Data for training and testing the PIO-Net proposed in physics-informed deep operator learning based on reduced-order modelling for retrieving the ocean interior density from the surface</p>
Data for training AMSR2-CNN and its corresponding machine learning algorithm
<p>Despite the availability of multiple decades of passive microwave measurements from satellite platforms, their utility for developing quantitative, spatially distributed estimates of snowpack is yet to be realized. A major bottleneck is the use of simple conceptual retrieval model formulations that are ineffective in representing the significant heterogeneity and complexity of snow evolution, particularly over areas with complex topography and forest regions. Here we demonstrate a physics-constrained and interpretable Convolutional Neural Network (CNN) to learn the functional relationship utilizing multi-channel passive microwave brightness temperature measurements from the Advanced Microwave Scanning Radiometer 2 (AMSR2) and in-situ snow depth observations. The machine learning approach with CNN generates vastly improved snow depth estimates relative to the standard AMSR2 estimates. Compared to independent in-situ measurements of snow depth over the Continental United States, the domain averaged Pearson correlation measure is three times higher than that of the standard AMSR2 estimates (R<sup>2</sup>: 0.68 versus 0.21), while the systematic errors are reduced by approximately fourfold. Further, the CNN-based snow depth estimates also exhibit notable enhancements in regions with forests, deep snow, and melting snow, thereby alleviating the limitations faced by traditional algorithms in retrieving accurate snow depths. The interpretation of the CNN framework further indicates that the machine learning approach dynamically leverages both volume scattering and emission components from a suite of measured passive microwave signals to generate more accurate snow depth retrievals. The results of this study provide an important benchmark of high-quality snow retrievals from passive microwave satellite measurements by maximizing their information content.</p>
Zr–O Ab Initio Training Data Created by Molecular Dynamics, Contour Exploration, and Dimer Searches
<p> These density functional theory calculations span a diverse set of structures in the Zr–O system which was used as machine-learned interatomic potential (MLIP) training data. This data set was used to benchmark different structural evolution methods (molecular dynamics, contour exploration, and dimer searches) for the quality and accuracy of MLIPs trained on them. The data is provided in the .traj format from ASE. Along with data set used in our publication, we provide a large set of extra unused data and a small Python script example for parsing the data set. The set contains 120,068 structures which contain a total of 3,154,158 atoms.</p> <p>For more details, please see our paper:<br> Michael J Waters and James M Rondinelli, <em>J. Phys.: Condens. Matter</em> <strong>34</strong> 385901(2022) (<a href="https://dx.doi.org/10.1088/1361-648X/ac7f73">https://dx.doi.org/10.1088/1361-648X/ac7f73</a>)</p>
The Data and Codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)
<p>Note: the monthly averaged NCAM, SPCAM, and CAM5 results are uploaded as *_h0.tar.gz!</p> <p>The data and codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en) are stored in this repositary.</p> <p>This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook.</p> <p>Please make sure to install all python packages used in an environment.</p> <p>Please read the ReadME-2.txt.</p> <p>For the entire training and testing datasets in both the baseline and +4K SST climates. Please download them from Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 and https://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0).</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.