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1,773 results for “Predictive model”
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data
<p>FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper:</p> <p>"Evaluation of machine learning classifiers for predicting deep convection"</p> <p>by Peter Ukkonen and Antti Mäkelä (to appear in JAMES)</p> <p>The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python. </p>
Wind farm power short-term prediction using WRF model and Kalman filtering
<p>This repository contains the data used and generated in the paper:</p> <p>Mamani, R., & Hendrick, P. (2019). Wind farm power short-term prediction using WRF model and Kalman filtering. ECOS 2019</p>
Status quo in data availability and predictive models of nano-mixture toxicity
<p>Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity.</p> <p>This table contains the list of 183 curated literature used in this study.</p>
"A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset
<p>Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats and FORTRAN code.</p>
Data from: Varyingly hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforest
A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of plant host use in speciose groups like tropical Lepidoptera. Here we used standardised food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of exhaustively sampled forest. For pyraloids we relied on phylogeny alone and predicted 54% of species level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food-web only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity), was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid-plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.
Data from: Impacts of rainfall extremes predicted by climate-change models on major trophic groups in the leaf-litter arthropod community
1. Arthropods in the leaf-litter layer of forest soils influence ecosystem processes such as decomposition. Climate-change models predict both increases and decreases in average rainfall. Increased drought may have greater impacts on the litter arthropod community. In addition to affecting survival or behavior of desiccation-sensitive species, lower rainfall may indirectly lower abundances of consumers that graze drought-stressed fungi, with repercussions for higher trophic levels. 2. We tested the hypothesis that trophic structure will differ between the two rainfall scenarios. In particular, we hypothesized that densities of several broadly defined trophic groupings of arthropods would be lower under reduced rainfall. 3. To test this hypothesis we used sprinklers to impose two rainfall treatments during three growing seasons in roofed, fenced 14-m2 plots; and documented changes in abundance from initial, pre-treatment densities of 39 arthropod taxa. Experimental plots were subjected to either LOW (fortnightly) or HIGH (weekly) average rainfall based upon climate models and the previous 100 years of regional weekly averages. Unroofed open plots, our reference treatment (REF), experienced higher-than-average rainfall during the experiment. 4. The two rainfall extremes produced clear negative effects of lowered rainfall on major trophic groups. Broad categories of fungivores, detritivores and predators were more abundant in HIGH than LOW plots by the final year. Springtails (Collembola), which graze fungal hyphae, were 3x more abundant in the HIGH-rainfall treatment. Taxa of larger-bodied fungivores and detritivores, spiders (Araneae), and non-spider predators were 2x more abundant under HIGH rainfall. Densities of mites (Acari), which include fungivores, detritivores and predators, were 1.5x greater in HIGH rainfall plots. Abundances and community structure of arthropods were similar in REF and experimental plots, showing that effects of rainfall uncovered in the experiment are applicable to nature. 5. This pattern suggests that changes in rainfall will alter bottom-up control processes in a critical detritus-based food web of deciduous forests. Our results, in conjunction with other findings on the impact of desiccation on arthropods and fungal growth, suggest that drier conditions will depress densities of fungal consumers, causing declines in higher trophic levels, with possible impacts on soil processes and the larger forest food web.
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
Predicting subjective liking of bitter and sweet liquid solutions using facial electromyography: mixed model dataset
<p>Hedonic responses to foods are often measured using subjective liking ratings scales. This project investigated the potential use of electromyography as a means to predict subjective liking ratings using affective facial muscle activity recorded at different phases of oral processing while tasting liquids. Using linear mixed models, muscle activity recorded while emptying into the mouth, swirling, and thinking about the taste of bitter and sweet liquid solutions was used to predict subjective liking ratings. During different phases of the tasting, these mixed models demonstrate that zygomaticus major activity predicted increased liking and that corrugator supercilii and levator labii superioris predicted decreased liking. The change in liking ratings predicted by each muscle varied depending on whether participants were emptying, swirling, or thinking about the taste.</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations
<h2>SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations</h2> <p>SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.</p> <p>This Zenodo repository includes a <code>.tar.gz</code> archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a>.</p> <h3>License:</h3> <p>Creative Commons Zero v1.0 Universal (CC0)<br>This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.</p> <h3>Archive Contents:</h3> <p>This compressed file includes:</p> <ul> <li>Datasets<br>- Tissue Datasets: <code>ALMANAC-Breast</code>, <code>ALMANAC-Lung</code>, <code>ALMANAC-Ovary</code>, <code>ALMANAC-Skin</code><br>- Study Datasets: <code>FRIEDMAN</code>, <code>ONEIL</code></li> <li>Supporting Files<br>- Raw and preprocessed data<br>- Feature dictionaries<br>- Hyperparameter configurations<br>- Trained model weights</li> </ul> <h3>Folder Structure:</h3> <blockquote> <p><code>SynProtX/</code><br><code>├── data/ # Raw and preprocessed data</code><br><code>│ ├── export/ # Processed protein/gene expression & drug combinations</code><br><code>│ ├── nps/ # Numpy arrays for all datasets</code><br><code>│ ├── nps_intersected/ # Dataset-specific numpy arrays</code><br><code>│ └── raw/ # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger</code><br><code>├── feature_dicts/ # Feature dictionaries for drug combinations</code><br><code>├── hyperparams/ # Hyperparameter configs for SynProtX-GATFP</code><br><code>│ ├── classification/ # For classification tasks</code><br><code>│ └── regression/ # For regression tasks</code><br><code>├── state_dict/ # Trained model weights</code><br><code>│ ├── classification/ # PyTorch checkpoints for classification</code><br><code>│ └── regression/ # PyTorch checkpoints for regression</code><br><code>└── README_Zenodo.md # This file</code></p> </blockquote> <h3>For more information, please visit:</h3> <p><strong>GitHub:</strong> <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a></p>
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"
<p>Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the <strong><em>Journal of Advances in Modeling Earth Systems</em></strong> (JAMES).</p> <p>Scripts: Python and NCL</p> <p>Data: Netcdf, PNG, Python pickled objects</p>
Partial Atomic Model of the Tailed Lactococcal Phage TP901-1 as Predicted by AlphaFold2
Open the record for dataset details and reuse information.
Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
<p>Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be</p>
Model Output accompanying 'A predicted pause in the rapid warming of the Northwest Atlantic Shelf in the coming decade'
<p>Postprocessed model output and observations necessary for reproducing all figures from the main body of the artcile 'A predicted pause in the rapid warming of the1 Northwest Atlantic Shelf in the coming decade'. Please see the README.txt file </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.