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5,805 results for “Data model”
2023_Mw7.8_Kahramanmaraş_Data_model
<p>The MPS models,datasets relevant to BP results, and static and dynamic Coulomb failure stress changes during the 2023 Mw 7.8 Kahramanmaraş earthquake.</p>
SEP list and effective dose of paper "The radiation impact of Solar Energetic Particle Events on the Moon: A statistical study using data-based modeling results"
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Modeling of thin liquid films with arbitrary many layers -- Data
<p>Data for publication Modeling of thin liquid films with arbitrary many layers by Tilman Richter, Paolo Malgaretti, and Jens Harting. Contains Data, Graphics and a julia Pluto notebook for data analysis and plot generation. </p>
Modeled crustal stress data in the Guangdong-Hong Kong-Macao Greater Bay Area, SE China
<p>The file contains the magnitudes and/or orientations (SHazi) of the maximum and minimum horizontal stress (SHmax, Shmin) at depths of 2 km and 10 km, as well as the Regime Stress Ratio (RSR) in the Guangdong-Hong Kong-Macao Greater Bay Area, SE China. It also includes the stress state on the Heyuan fault (F13), Wuchuan-Sihui fault (F32), and Baini-Shawan fault (F23). </p>
Cellular population data (Agent-based model CRC)
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Supplementary data: model results for "Labor market evolution is a key determinant of global agroeconomic and environmental futures"
<p>This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "<strong>Labor market evolution is a key determinant of global agroeconomic and environmental futures</strong>".</p> <p>The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-nc2024-LandBasedCDR-GCAM" target="_blank" rel="noopener">realxinzhao/paper-nc2024-LandBasedCDR-GCAM</a>.</p> <p>In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems" target="_blank" rel="noopener">realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems</a>.</p>
Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
Reproducibility Test Data of 4 MHub Models
<p>This dataset provides test data for 4 models integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. Each model is accompanied by a zip file containing data specific to its "default" workflow.</p> <h3>Dataset Composition:</h3> <ul> <li><strong>Sample Folder:</strong> Contains the input data utilized for testing the model’s functionality.</li> <li><strong>Reference Folder:</strong> Contains the corresponding output provided by the original model contributor.</li> </ul> <h3>Sample Data Source:</h3> <p>The sample images used in this dataset are sourced from public datasets available through the <strong><a href="https://datacommons.cancer.gov/repository/imaging-data-commons" target="_blank" rel="noopener">Imaging Data Commons (IDC)</a></strong>, a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.</p> <h3>About MHub:</h3> <p>MHub (<a href="https://mhub.ai/" target="_new" rel="noopener">mhub.ai</a>) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.</p> <p>For more information on the platform and its capabilities, visit <a href="https://mhub.ai/" target="_blank" rel="noopener">mhub.ai</a>.</p>
MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</p>
Data and Scripts for "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals"
<p>This contains the data and graphing scripts necessary to recreate all figures in the paper "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals". Supplemental Material for the paper is also provided here. Please refer to the README.md for instructions on how to use. The model can be found at: https://github.itap.purdue.edu/ElsjePienaarGroup/LTBINaiveinvitroModel/ along with the uncalibrated parameter files and scripts to run on HPCs.</p>
Data for: "Can Language Models Recognize Convincing Arguments?"
<p>For a description, see: https://go.epfl.ch/persuasion-llm</p>
Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set
<p>Data supporting the original figures 1, 2, 3 and 4 of the related publication.</p>
Biomod2 codes and CSV data for the ensemble model of C. marmorata
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Article: Modeling Mouse ICM - Complete Data Bank - Part II
<p>Our study presents a spatial-stochastic model for the gene regulatory network (GRN) and the signaling pathway governing cell-fate differentiation during early mouse embryogenesis, specifically at the blastocyst stage. Departing from biophysics-based models of gene regulation, we perform stochastic simulations of the biochemical processes driving early mouse embryogenesis both at the cell and tissue level. Combining these simulations with state-of-the-art AI-aided inference techniques, we successfully parameterize our model, replicating key experimental observations and providing mechanistic insights into the biochemical interactions giving rise to them. Thanks to the stochastic nature of our approach, we quantify the high robustness of ICM specification to various kinds of noise, and provide quantitative predictions for the effects of diverse experimentally testable perturbations. Altogether, we provide a deeper understanding of the intricate mechanisms driving early cell-fate decisions in mouse embryogenesis, highlighting the synergy of local cellular and broader tissue-scale interactions that shape development.</p>
Article: Modeling Mouse ICM - Complete Data Bank - Part III
<p>Our study presents a spatial-stochastic model for the gene regulatory network (GRN) and the signaling pathway governing cell-fate differentiation during early mouse embryogenesis, specifically at the blastocyst stage. Departing from biophysics-based models of gene regulation, we perform stochastic simulations of the biochemical processes driving early mouse embryogenesis both at the cell and tissue level. Combining these simulations with state-of-the-art AI-aided inference techniques, we successfully parameterize our model, replicating key experimental observations and providing mechanistic insights into the biochemical interactions giving rise to them. Thanks to the stochastic nature of our approach, we quantify the high robustness of ICM specification to various kinds of noise, and provide quantitative predictions for the effects of diverse experimentally testable perturbations. Altogether, we provide a deeper understanding of the intricate mechanisms driving early cell-fate decisions in mouse embryogenesis, highlighting the synergy of local cellular and broader tissue-scale interactions that shape development.</p>
Article: Modeling Mouse ICM - Complete Data Bank - Part I
<p>Our study presents a spatial-stochastic model for the gene regulatory network (GRN) and the signaling pathway governing cell-fate differentiation during early mouse embryogenesis, specifically at the blastocyst stage. Departing from biophysics-based models of gene regulation, we perform stochastic simulations of the biochemical processes driving early mouse embryogenesis both at the cell and tissue level. Combining these simulations with state-of-the-art AI-aided inference techniques, we successfully parameterize our model, replicating key experimental observations and providing mechanistic insights into the biochemical interactions giving rise to them. Thanks to the stochastic nature of our approach, we quantify the high robustness of ICM specification to various kinds of noise, and provide quantitative predictions for the effects of diverse experimentally testable perturbations. Altogether, we provide a deeper understanding of the intricate mechanisms driving early cell-fate decisions in mouse embryogenesis, highlighting the synergy of local cellular and broader tissue-scale interactions that shape development.</p>
The NIMROD training data for the STFNO model
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Code für die Bearbeitung der Verfügbaren Speleothem and Permafrost-Model Data für die Masterarbeit: "Speläotheme als globale Permafrostproxies"
<p>Der benutzte Code für die Bearbeitung und Darstellung der Speläothem-Datensätze und die Permafrost-Modelle. Die Datengrundlage der Speläothem-Daten und der Permafrost Modelle AWI-ESM und S21 werden für die Bearbeitung benötigt. Authoren, bzw. Ansprechpartner für die jeweilgen Daten sind im Literaturverzeichnis der Arbeit zu finden.</p>
ORCHIDEE-NLAT model code and evaluation data
<p>ORCHIDEE-NLAT model code and model evaluation data (including water discharge and total nitrogen flow)</p>
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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.