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Dataset results
356 results for “Machine learning models”
Dataset [ref. paper "Predictive modeling of drivers' brake reaction time through machine learning methods"]
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Machine Learning Mid-Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu
<p>This is supporting data for the paper titled "Machine Learning Mid-Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu". Figure S1 compares model performance of nonnegative LSMA and PLS from Pan et al. (2015). Tables S1 through S5 provide XRD data, metadata, MIR spectra using the laboratory conversion, MIR spectra using the OTES conversion and quantitative XRD results for Murchison meteorite. </p>
Machine-learning convection model output file for use in convection pattern estimation.
<p>Output file from the model described in:</p> <p>Bristow, W. A., Topliff, C. A., & Cohen, M. B. (2022). Development of a high-latitude convection model by application of machine learning to SuperDARN observations. <em>Space Weather</em>, 20, e2021SW002920. <a href="https://doi.org/10.1029/2021SW002920">https://doi.org/10.1029/2021SW002920</a></p> <p>The file provides model convection patterns for the period 0000 UT to 2359 UT on 26 March 2014</p> <p> </p>
Trained Machine Learning Model to Detect Surface Events from Seismograms
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Enhancing Streamflow Prediction through Multi-model Ensemble Framework and Machine Learning Techniques
<p>This file contains python code used in this study and data used to plot figures. </p>
Models from "Applying Machine Learning to Characterize and Extrapolate the Relationship Between Seismic Structure and Surface Heat Flow"
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A dataset for establishing a machine learning-based QSAR model to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library
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Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches
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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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Microplastic deposit predictions on sandy beaches by geotech-nologies and machine learning models
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Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Integrated machine learning and GIS-based bathtub models to assess the future flood risk in the Kapuas River Delta, Indonesia
<p>To use the data and the code, please cite the following article: </p> <p>Joko Sampurno, Randy Ardianto, Emmanuel Hanert; Integrated machine learning and GIS-based bathtub models to assess the future flood risk in the Kapuas River Delta, Indonesia. <em><em>Journal of Hydroinformatics</em></em> 2022; jh2022106. DOI: <a href="https://doi.org/10.2166/hydro.2022.106">https://doi.org/10.2166/hydro.2022.106</a></p>
Supplementary data: "Physics-informed machine learning for power grid frequency modelling"
<p>This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" <a href="https://doi.org/10.48550/arXiv.2211.01481">(Preprint)</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">available at github</a>.</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files:</p> <ul> <li><em>tuning </em>: Results of hyper-parameter tuning.</li> <li><em>best_model </em>: Weights of the trained model with best hyper-parameters.</li> <li><em>best_model_<scaling-variation> </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling.</li> <li><em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised.</li> <li><em>shap_values_<parameter>_long.h5</em> : SHAP values for the prediction of the system parameters.</li> </ul>
Efficacy of Bumetanide in Children With Autism Spectrum Disorder Guided by Peripheral Blood Biomarkers and Machine Learning Models
ClinicalTrials.gov study NCT07005414. IPD Sharing: NO. Countries: 1. Publications: 0.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
ClinicalTrials.gov study NCT06646120. IPD Sharing: YES. Countries: 0. Publications: 5.
Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes
ClinicalTrials.gov study NCT04014010. IPD Sharing: NO. Countries: 0. Publications: 1.
Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia
ClinicalTrials.gov study NCT06582407. IPD Sharing: NO. Countries: 1. Publications: 0.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3
ClinicalTrials.gov study NCT06645548. IPD Sharing: YES. Countries: 0. Publications: 6.
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.