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1,773
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Dataset results
1,773 results for “Predictive model”
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Data and structures for "How accurately can we predict binding poses with AlphaFold models?
<p>Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB. </p> <p>Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.</p> <p> </p> <p> </p>
Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Manuscript number: <strong><span>2023WR035618R</span></strong></p> <p><strong>Abstract:</strong><strong> </strong>This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. <span>It</span> is then driven by bias corrected general circulation model (GCM) <span>prediction</span>s to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future <span>monthly </span>streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead <span>reservoir </span>inflows <span>for the Danjiangkou Reservoir in China. </span>The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. <span>Additional tests indicate that hybrid</span> model<span>s based on CNN-GRU </span>outperform <span>those based on</span> <span>LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model</span> greatly reduce<span>s</span> the <span>computation </span>burden of rolling prediction<span>. It also </span>saves decision-makers the time and effort of trying different combinations of predictors<span>, which is indispensable when building DL models. Overall</span>, the new hybrid model <span>demonstrates great potential</span> for monthly streamflow prediction <span>where</span> training data are limited.</p> <p><strong><span>Keywords:</span></strong> <span>monthly streamflow prediction; deep learning; </span><span>physically-based distributed hydrological model; </span><span>VIC model; soil moisture; hybrid model </span></p>
DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
<p>test and training data.</p>
Antibody-Antigen Models for McCoy 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"
<p>Up to the top 20 models generated for each method tested in the 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"</p>
Datasets associated with the manuscript Protein Condensate Atlas from predictive models of heteromolecular condensate composition
<p>Datasets associated with the manuscript "Protein Condensate Atlas from predictive models of heteromolecular condensate composition".</p>
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
Open the record for dataset details and reuse information.
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Data for DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection
<p>Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse, and for cafa3 data. Extract and place them in the <em>data </em>folder.</p>
Original features and code of clinical prediction model
Open the record for dataset details and reuse information.
Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</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.