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356 results for “Machine learning models”
Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model
<h3>Dataset</h3> <p>1. observation data over 14 weather stations</p> <p>Variables: hourly near-surface 2-min average wind speed, wind direction </p> <p>2. ECMWF-IFS forecast data over 14 weather stations</p> <p>Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)</p> <p>Table 1. ECMWF-IFS forecast data at surface level</p> <div> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Temperature at 2 m</p> </td> <td> <p>2t</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Sea surface temperature</p> </td> <td> <p>sst</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Dewpoint temperature at 2 m</p> </td> <td> <p>2d</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Convective precipitation in the past hour</p> </td> <td> <p>cp</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Mean sea level pressure</p> </td> <td> <p>msl</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 10 m</p> </td> <td> <p>10u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 10 m</p> </td> <td> <p>10v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 10 m</p> </td> <td> <p>10ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 10 m</p> </td> <td> <p>10wd</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 100 m</p> </td> <td> <p>100u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 100 m</p> </td> <td> <p>100v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 100 m</p> </td> <td> <p>100ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 100 m</p> </td> <td> <p>100wd</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> </div> <div> </div> <p>Table 2. ECMWF-IFS forecast data at upper level</p> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Relative humidity at xxx hPa</p> </td> <td> <p>r_Lxxx</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Temperature at xxx hPa</p> </td> <td> <p>t_Lxxx</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Vertical velocity of wind at xxx hPa</p> </td> <td> <p>w_Lxxx</p> </td> <td> <p>Pa s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Zonal component of wind at xxx hPa</p> </td> <td> <p>u_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind at xxx hPa</p> </td> <td> <p>v_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at xxx hPa</p> </td> <td> <p>ws_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at xxx hPa</p> </td> <td> <p>wd_Lxxx</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> <div> </div> <p>3. key variables constructed by feature engineering</p> <p>(1) sort-term statistics, including <em>maximum, minimum, mean </em>and <em>variance</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during the next 48 hours,</p> <p> (2) long-term statistics, including <em>mean </em>and <em>deviation</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),</p> <p> (3) thermodynamic factors, including the low-level wind shear between <em>10ws</em> and <em>100ws</em>, vertical wind shear between 200 hPa and 850 hPa<em>, </em>the differences between <em>sst</em><em> </em>and <em>2t</em><em>.</em></p> <h3>Scripts</h3> <p>1. Random Forest model training code</p> <p>2. LightGBM model training code</p> <p>3. XGBoost model training code</p> <p>4. TabNet-MTL model training code</p> <p> </p>
Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis
<p>This zip file includes the dataset 'two_reactions_022624.csv,' which is used for training and testing ML/DL models in the paper 'Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis,' as well as trained models and some files used for training the model. When running the model downloaded from GitHub, copy and paste the files downloaded from here into the subfolder with the same name and path as the one downloaded from GitHub.</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>
A Dataset for Applying Machine Learning and Eddy Covariance Approaches to Model Mangrove Carbon Production (ML-MCP)
<p>The Mangrove Carbon Production (ML-MCP) dataset (daily time scale) encompasses comprehensive measurements of carbon production in mangrove ecosystems from four EC tower station in the USA and China, derived using advanced machine learning models and eddy covariance techniques. This dataset includes various variables such as carbon fluxes, environmental factors. By integrating machine learning algorithms, the dataset enhances the accuracy of carbon productivity estimations, facilitating better understanding and management of mangrove ecosystems' role in carbon sequestration and climate regulation.</p>
Construction of Perioperative Medical Data Platform and Its Typical Practice to Predict Postoperative Acute Moderate to Severe Pain With Machine Learning Models
ClinicalTrials.gov study NCT05569460. IPD Sharing: NO. Countries: 1. Publications: 0.
A Machine Learning Predictive Model for Sepsis
ClinicalTrials.gov study NCT04771429. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Prospective Validation of the Model Predicting Postoperative Delirium Occurrence With Machine Learning-based Analysis of Intraoperative Biological Signals During Anesthesia in Cardiac Surgery
ClinicalTrials.gov study NCT05320965. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Prediction Model for Postoperative AKI in Patients Undergoing Lung Transplantation Using Machine Learning
ClinicalTrials.gov study NCT06218745. IPD Sharing: NO. Countries: 1. Publications: 0.
A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer.
ClinicalTrials.gov study NCT06841653. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Research of Constructing a Risk Assessment Model for Gastric Cancer Based on Machine Learning
ClinicalTrials.gov study NCT04957407. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Machine-learning Model to Predict Lymph Node Metastasis of Intrahepatic Cholangiocarcinoma
ClinicalTrials.gov study NCT06290739. IPD Sharing: YES. Countries: 1. Publications: 0.
Prospective Validation of Machine Learning Model to Predict Platinum Induced Nephrotoxicity in Cancer Patients
ClinicalTrials.gov study NCT07114276. IPD Sharing: NO. Countries: 1. Publications: 0.
Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
ClinicalTrials.gov study NCT06380049. IPD Sharing: NO. Countries: 1. Publications: 0.
Establishment of Tongue Image Database and Machine Learning Model for Malignant Tumors Diagnosis
ClinicalTrials.gov study NCT04793672. IPD Sharing: NO. Countries: 1. Publications: 0.
BACTERIUM: Study for a Machine-learning-based Model to Predict Bloodstream Infections
ClinicalTrials.gov study NCT05876598. IPD Sharing: NO. Countries: 1. Publications: 0.
Establishment of a Feasibility Model for NOSE Surgery Based on Machine Learning
ClinicalTrials.gov study NCT05797064. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Development of Machine Learning Models for the Prediction of BMI and Complications After Bariatric Surgery (CABS-Study)
ClinicalTrials.gov study NCT05710913. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation of a Machine Learning Model Based on MR for the Prediction of Prostate Cancer
ClinicalTrials.gov study NCT06773598. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients
ClinicalTrials.gov study NCT07030166. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Non-invasive Measurement of Glucose with Advanced Spectroscopy Techniques and Machine Learning Models
ClinicalTrials.gov study NCT06406075. IPD Sharing: NO. Countries: 1. Publications: 0.
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.