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Dataset results
13,064 results for “predictions”
Identification of Potential Biomarkers for Cerebral Palsy and the Development of Prediction Models
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Prediction of the ground state for indenofluorene-type systems with Clar's π-sextet model
<p>This dataset contains the computational data associated with "Prediction of the ground state for indenofluorene-type systems with Clar's π-sextet model". </p>
Anatomy of the 2022 scorching summer in the Yangtze River Basin using the SINTEX-F2 seasonal prediction system
<p>The above documents are used in the article of the same name.</p>
Incidence and Predictive Factors of Hyperventilation Syndrome in Patients after COVID 19 pneumonia: A Prospective Cohort Study
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Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
Data and code for, "Predicting self-assembly of sequence-controlled copoly- mers with stochastic sequence variation"
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Varity predictions
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Predicting Bug-Inducing Commits Using Software Quality Metrics
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Cumulative arrival time distribution data for "Upscaling transport in heterogeneous media featuring local-scale dispersion: flow channeling, macro-retardation and parameter prediction"
<div> <div>This archive contains arrival time CDF data for a variety of transport simulations in heterogeneous Darcy flow fields, alongside metadata describing the flow fields. The flow fields were spatially periodic, intersected by uniformly-spaced imaginary planes. Arrival times represent length of time from particle departure from one plane until arrival at the next.</div> <div> </div> <div>Consult the README.md file at the top level of the archive for more information. The file format used to store the CDF data is documented in the Python script at the top level of the archive.</div> </div>
The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"
<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into The histology images were classified as “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) according to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer”, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>
An adaptive and interpretable modeling architecture assisted rapid and reliable consensus prediction for hazardous properties of chemicals
<p>*the computational results of interpretable cases are available in the supporting information for interpretable case.xlsx ;</p> <p>*the training dataset is utilized for model training while the validation dataset is utilized for evaluating. </p>
Cell features for "Datasets for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model""
<p>This repository contains features and corresponding coordinates of cells extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) and corresponded to the article dividing the training and test sets.<br><br></p> <p>The features of all cells were extracted by Hovernet and Transnuseg at 40x objective magnification for extraction masking and further feature extraction</p>
Galaxy Histories with in silico mass spectra of Mirex, Ethylene, Benzophenone and Enilconazole predicted via QCxMS
<p>Galaxy histories containing spectra predicted using the QCxMS software. The molecules are Mirex, Ethylene, Benzophenone and Enilconazole. Calculations have been carried our using Galaxy and histories exported as ROCrates.</p>
Source data of Mirtronstructdb - A comprehensive database of mirtrons with predicted secondary structure
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Multimodal contrastive learning for spatial gene expression prediction using histology images
<p>we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.</p>
Implementation of a Diabetes Status Prediction Application Using a Machine Learning Algorithm Approach
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Supplementary materials of The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia
<p>Supplementary materials of <em><strong>The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia</strong> </em>published in <em><strong>Islamic Guidance and Counseling Journal (IGCJ) Vol. 7, No. 2, 2024</strong></em></p>
Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks
<p>Scripts and data for the publication</p>
A Model Predicting The Relationships Between Seed Size, Yield, and Actual Yield in Dry Field Peas
<p>This is a model that predicts yield and actual yield in dry pea based on seed size. Seeding rate, seed cost, pod length, and expected grain yield can be varied in the model. The ideal seed size is displayed at the vertex of the curve in three different graphs. </p>
Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: Importance of Parametric Uncertainty in Predicting Probability Distributions for Burst Wait-Times in Fissile Systems.</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.