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117 results for “Machine-learning”
Data from: Biogeographic and anthropogenic correlates of Aleutian Islands plant diversity: a machine-learning approach
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Calibration of probability predictions from machine-learning and statistical models
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Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?
<p>This is a research compendium (RC) for the publication</p> <blockquote> <p>Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?</p> </blockquote> <p>Code, figures, appendices and the manuscript can be found in the corresponding <a href="https://github.com/pat-s/2019-feature-selection">GitHub repository</a>.</p> <p>This RC is a static snapshot at the time of submission. The GitHub repository holds the latest version and may see changes after the publication was accepted.</p> <p><strong>Data sources and description</strong></p> <ul> <li><em>aoi.gpkg</em><strong>:</strong> Area of interest for downloading Sentinel-2 images. <em>Not used in the publication<strong>. </strong></em>Source:<em><strong> </strong></em>Custom.</li> <li><em>forest_mask.gpkg</em>: A forest/non-forest mask of the Basque Country. <em>Not used in the publication</em>. Source:<em><strong> </strong></em>Custom.</li> <li><em>hyperspectral.zip: </em>Hyperspectral remote sensing data used to extract reflectance values on the tree level. Source:<em><strong> </strong></em>Custom.</li> <li><em>plot-locations.gpkg: </em>Spatial location of the plots used in the study. Source:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data-corrected.zip: </em>Corrected in-situ data containing defoliation information on the tree level. A correction of the spatial location was applied by the creators of the data. Source:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data.zip: </em>First version of in-situ data containing defoliation information on the tree level.<em> Not used in the publication. </em>Source:<em><strong> </strong></em>Custom.</li> </ul> <p><strong>Licenses</strong></p> <p>All files are licensed under CC BY 4.0.</p>
Data from: Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity
Parametric and nonparametric supervised machine learning techniques were used to estimate saturated and near saturated hydraulic conductivities (Ks, K10) from easily measurable soil properties including name of pedological horizon (HOR), soil texture (sand, silt & clay), organic matter (OM), bulk density (BD) and water contents (θpF1, θpF2, θpF3 and, θpF4.2) measured at four different matric heads (-10, -100, -1000, and -15848 cm). Using a stepwise linear model (SWLM) and the Lasso regression as parametric methods with 316 data in training and 135 data in testing phase, four pedotransfer functions (PTFs) were obtained in which water contents for both methods play an important role compared to other variables. SWLM showed better performance than Lasso in the testing phase for log(Ks) and log(K10) prediction with RMSE of 0.666 and 0.551 cm d-1 and R2 of 0.26 and 0.65. Nonparametric supervised machine learning methods trained and tested with similar data set significantly improved the accuracy of Ks prediction with R2 of 0.52, 0.36 and 0.53 for Gaussian regression process (GPR), support vector machine (SVM) and Ensemble (ENS) method in the testing stage. These methods also described 74.9, 66.7 and 72.5% of the variation of log(K10). Bootstrapping method validated the strong performance of nonparametric techniques. Feature selection capability of GPR determined that instead of using a model with all predictors, HOR, silt, θpF1 and θpF3 are sufficient for the prediction of log(Ks) or log(K10), HOR, silt, and OM can predict as accurate as the comprehensive model with all variables.
A Comparison of Machine-Learning Assisted Optical and Thermal Camera Systems for Beehive Activity Counting
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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>
Data Repository for: Machine-learning the spectral function of a hole in a quantum antiferromagnet
<p>The machine-learning dataset of 51^3 ~1.3 × 10^5 density of states (DOS) of a mobile hole in the t-t'-t''-J model theoretically generated by using the self-consistent Born approximation in the three-dimensional parameter space of t′ ∈ [−0.5, 0.5], t′′ ∈ [−0.5, 0.5] and J ∈ [0.2, 1.0], with each parameter sampled on a 51-point uniform grid. The dataset is randomly partitioned into an 80/10/10 training (T), validation (V), and testing T split. Note that each DOS A(ω) was calculated on a 1201-point uniform grid of ω ∈ [−6t, 6t], then it was resampled on a 301-point uniform grid for the forward problem and on a 354-point uniform grid for the inverse problem. The dataset used in the inverse problem is limited to the parameter space of t′ ∈ [−0.5, 0], t′′ ∈ [0, 0.5] and J ∈ [0.2, 1.0]. To open the enclosed .npz files, use numpy.load() in python3.</p>
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
ClinicalTrials.gov study NCT06728059. IPD Sharing: YES. Countries: 1. Publications: 0.
Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study
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Data from: Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity
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Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability
GEO Series GSE254461. Homo sapiens. 57 samples. Type: Expression profiling by high throughput sequencing.
Radiogenomics of glioblastoma: Machine-learning based classification of molecular characteristics using multiparametric and multiregional MRI features
GEO Series GSE85539. Homo sapiens. 152 samples. Type: Methylation profiling by array.
Machine-learning Directed Conversion of Glioblastoma to Dendritic Cell-like Antigen Presenting Cells as Cancer Immunotherapy
GEO Series GSE270855. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Machine-learning Classification Identifies Early Systemic Sclerosis Patients that Improve with Abatacept treatment by Modulating CD28-Pathways
GEO Series GSE217067. Homo sapiens. 233 samples. Type: Expression profiling by high throughput sequencing.
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The data is the monthly NO3- concentrations across China during 2005-2015. These data was obtained using a novel ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms based on satellite data, assimilated meteorology, and other geographical covariates.</p> <p>In the datasets, XX-YY denote the XX month in YY year.<br> For instance, January-05 denotes the January in 2005.<br> NaN in the data denote the missing values.</p>
Machine-learning method for predicting the scanning parameters influence on random measurement error
<p>Data acompaniing the study.</p>
Research data for "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials"
<p>This dataset supports the paper: "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials". The paper is online here: https://doi.org/XXXXXXXXXXXXXXXXX.</p>
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>
Health, socioeconomic and genetic predictors of COVID-19 vaccination uptake: a nationwide machine-learning study
<p>Reduced participation in COVID-19 vaccination programs is a key societal concern. Understanding factors associated with vaccination uptake can help in planning effective immunization programs. We considered 2,890 health, socioeconomic, familial, and demographic factors measured on the entire Finnish population aged 30 to 80 (N=3,192,505) and genome-wide information for a subset of 273,765 individuals. Risk factors were further classified into 12 thematic categories and a machine learning model was trained for each category. The main outcome was uptaking the first COVID-19 vaccination dose by 31.10.2021, which has occurred for 90.3% of the individuals.</p> <p>The strongest predictor category was labor income in 2019 (AUC evaluated in a separate test set = 0.710, 95% CI: 0.708-0.712), while drug purchase history, including 376 drug classes, achieved a similar prediction performance (AUC = 0.706, 95% CI: 0.704-0.708). Higher relative risks of being unvaccinated were observed for some mental health diagnoses (e.g. dissocial personality disorder, OR=1.26, 95% CI : 1.24-1.27) and when considering vaccination status of first-degree relatives (OR=1.31, 95% CI:1.31-1.32 for unvaccinated mothers)</p> <p>We derived a prediction model for vaccination uptake by combining all the predictors and achieved good discrimination (AUC = 0.801, 95% CI: 0.799-0.803). The 1% of individuals with the highest risk of not vaccinating according to the model predictions had an average observed vaccination rate of only 18.8%.</p> <p>We identified 8 genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor of vaccination status in an independent subset (AUC=0.612, 95% CI: 0.601-0.623). Genetic effects were replicated in an additional 145,615 individuals from Estonia (genetic correlation=0.80, 95% CI: 0.66-0.95) and, similarly to data from Finland, correlated with mental health and propensity to participate in scientific studies. Individuals at higher genetic risk for severe COVID-19 were less likely to get vaccinated (OR=1.03, 95% CI: 1.02-1.05).</p> <p>Our results, while highlighting the importance of harmonized nationwide information, not limited to health, suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also those less likely to uptake COVID-19 vaccination. The results can support evidence-informed actions for COVID-19 and other areas of national immunization programs.</p>
Use of Machine-learning Algorithms, Biomarkers and Measures of Quality of Life to Personalize Medical Management of Liver and Heart Transplant Recipients
ClinicalTrials.gov study NCT06774768. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.