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33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “machine learning, training data”
Code and Training Data for "Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands based on In-situ Internet of Things Monitoring"
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Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
<p>INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.</p> <p>MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons.</p> <p>RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively.</p> <p>CONCLUSION: In this article, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning.</p>
Machine Learning Potentials for Metal-Organic Frameworks with Thermodynamic Transferability: training data
<p>This dataset contains potential energies, forces, and virial stress for a large set of reference configurations for UiO-66(Zr) and MIL-53(Al), computed at the PBE-D3 level using CP2K 7.1. The basis set contained both TZVP Gaussian basis functions as well as plane waves (cutoff energy 800 Ry for UiO-66(Zr) and 900 Ry for MIL-53(Al)). The sampling of the Brillouin zone was restricted to the gamma point.</p>
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Training data for building a machine learning wildfire model over the CONUS
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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>
Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
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Data for training AMSR2-CNN and its corresponding machine learning algorithm
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Training data set for inverse pyrolysis machine learning
<p>For dataset description see https://doi.org/10.5281/zenodo.6606247</p>
Data Tabels (One-dimensional N-layer thermal modelling as a basis for effective machine learning training data generation for nondestructive testing of composite parts.)
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Data from: Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis
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Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</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.