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5,805 results for “Data model”
Understanding the Rare Inflammatory Disease Using Large Language Models and Social Media Data
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Data from: Advancing model-based ecosystem services assessments for policy support.
<p><span>Urban nature is increasingly recognized for its potential to address various societal challenges through the delivery of ecosystem services (ES). However, large-scale and comparative assessments quantifying this potential are hardly available. In this study, we modelled<span> </span>five ES (local temperature regulation, flood protection, global climate regulation, habitat protection, and interaction with nature) across 708 European urban areas. We quantified the ES based on a consistent supply-demand approach, using indicators representing the extent to which urban ecosystems’ capacity to provide a service was sufficient to meet societal demands. <span>We quantify each ES indicator using spatially explicit assessment methods, </span>building upon existing ES modelling tools (the urban InVEST model and GLOBIO-ES model) and publicly accessible data (e.g. Urban Atlas Land Use Land Cover from the European Urban Atlas dataset. </span></p>
Supporting data for 'Modelling of planar germanium hole qubits in electric and magnetic fields' v2
<p>The data must not be reused in other studies or publications without approval of the authors.</p>
Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months
<p>This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:</p> <p>"<em>Observing the evolution of the Sun’s global coronal magnetic field over eight months</em>"</p> <p>by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,<br>Richard Morton, and Cooper Downs</p> <p>Science, 386(6717), 76-82, <strong>2024</strong>, DOI: <a title="Observing the evolution of the Sun&rsquo;s global coronal magnetic field over eight months" href="http://doi.org/10.1126/science.ado2993" target="_blank" rel="noopener">10.1126/science.ado2993</a></p> <p>This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.</p> <p># Contents<br>The subfolders are organized as follows:</p> <p>### source<br>This folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.</p> <p>### runs<br>This folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.</p> <p>### scripts<br>This folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.</p> <p># Additional Notes<br>MAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.</p> <p>The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details. </p> <p>We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.</p> <p>If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.</p>
Macro EEG model data
<p>Data generated using macroscopic EEG modeling with axon propagation delays.</p>
Studies analyzed in the Systematic Review - Data Science Model Canvas for Health Research
<p>Data collected from the SLR on Data Science Model canvas for health research</p>
What Can Generative Modelling Do for Interpolation of Extremely Sparse Wind Farm Seismic Data
<p>2024 Global energy transition abstract about diffusion model data interpolation. </p>
Data table 3 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
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Data Files for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)
<p>These are the data for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)</p> <p>arXiv reference: https://arxiv.org/abs/2405.13164</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.</p>
Model data in support of the E3SMv2.1-Arctic overview paper (2024)
<p>This dataset is in support of the E3SMv2.1-Arctic publication led by Yiling Huo, which is in the process of being submitted to the Journal of Advances in Modeling Earth Systems (JAMES; September 2024). The dataset contains climatologies and time series of the model output data that are presented in the manuscript.</p>
Proposal of Attributes for the Data Model of a European Digital Building Renovation Logbook
<p>This document serves as supplementary material to the paper "Envisaging a European Digital Building Renovation Logbook: Proposal of a Data Model", by Marta Gómez-Gil, Sara Karami, José-Paulo de Almeida, Alberto Cardoso, Almudena Espinosa-Fernández and Belinda López-Mesa. Its purpose is to complement and expand upon the methodology outlined in the paper, with a specific focus on the identification of attributes for the development of a data model for the European Digital Building Logbook, focused on renovation.</p>
Glacioisostatic model with EGMS-data (Geopackage)
<p>This file contains a Glacioisostatic model for the Danish area with Sentinel-1 data from 2015-2021, from Copernicus European Ground Motion Service (EGMS). The model is created by removing non-regional land movements and interpolation with Inverse Distance Weighting (IDW).</p> <p>The file is associated with the article: ”Mit livs dessert”: 100-års jubilæet for Ellen Louise Mertz’ studie af niveauforandringerne i Danmark" by Marie W. Svendsen, Søren M. Kristiansen, Vivi K. Pedersen & Andersen Damsgaard. </p> <p><a href="http://doi:10.5281" target="_blank" rel="cc:attributionURL noopener noreferrer">Glacioisostatic model with EGMS-data (Geopackage) </a>© 2024 by Marie W. Svendsen is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer">CC BY 4.0 </a></p> <p> </p> <p> </p>
Data files for S. Malkaruge Costa et al., "Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger model and its relevance to graphene"
<p>Data files for the paper <em>Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger</em><br><em>model and its relevance to graphene </em>by Sohan Malkaruge Costa, Benjamin Cohen-Stead, and Steven Johnston. </p> <p>Reference: S. Malkaruge Costa, B. Cohen-Stead , and S. Johnston, Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger<br>model and its relevance to graphene. Physical Review B <strong>110</strong>, 115130 (2024). </p> <p>https://journals.aps.org/prb/pdf/10.1103/PhysRevB.110.115130</p> <p>Preprint available at: https://arxiv.org/abs/2407.09366</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.</p> <p> </p>
The Data for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation'
<p>Produced synthetic kilonova spectra for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation' using the Sedona radiative transfer code. Each model is an h5 file with the following groups:</p> <ul> <li>Lnu - Array of length # of timesteps by # of frequency bins that contains the spectral sequence of each kilonova model in cgs units (erg/s/Hz)</li> <li>click - Array of length # of timesteps by # of frequency bins for the number of Monte Carlo particles that make up Lnu</li> <li>mu - Center of polar angular bins (not useful since simulations are 1D)</li> <li>mu_edges - Edges of polar angular bins (not useful since simualtions are 1D)</li> <li>nu - Array of length # of frequency bins that are the central frequencies of a bin (Hz)</li> <li>nu_edges - Array of length # of frequency bins +1 that are the edges of each frequency bin (Hz)</li> <li>phi - Center of azimuthal angular bins (not useful since simulations are 1D)</li> <li>phi_edges - Edges of azimuthal angular bins (not useful since simulations are 1D)</li> <li>time - Times at which each spectrum was generated (Days)</li> <li>time_edges - Edges of time bins (Days)</li> </ul> <p>Each kilonova spectra file is named according to <atomic dataset>_<thermalization prescription>_KN_<lanthanide fraction>X_lan_<characteristic velocity>v_<mass>M_1D_spec.h5 where:</p> <ul> <li>atomic dataset is one of: HULLAC, ATOMIC, or Autostructure</li> <li>thermalization prescription is one of: local or global</li> <li>lanthanide fraction is the fraction of lanthanides in the ejecta by mass</li> <li>characteristic velocity is the kinetic velocity of the ejecta in units of the speed of light</li> <li>mass is the mass of the ejecta in units of solar masses</li> </ul>
Example data and pretrained Translatomer model
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Data Supporting Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model
Open the record for dataset details and reuse information.
Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .
<p>The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files</p>
Dataset accompanying "Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data"
<p>Dataset accompanying the article <a href="https://arxiv.org/abs/2409.10367" target="_blank" rel="noopener">Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data</a>. </p> <p>Contains almost 8000 events that are sampled from the summer months (where convective precipitation events are most likely to occur) of 2019-2023, centred over Austria.</p> <p>Each sample has a temporal span of 4 hours with a spatial extent of 400 x 700 km, with following data streams:</p> <ul> <li>4 MSG infrared channels with central wavelengths of 6.2, 7.3, 8.7, and 10.8 μm</li> <li>Rain rates mosaicked from ground-based radar observations</li> <li>Lightning data from ground-based observations</li> <li>INCA precipitation analysis</li> <li>INCA convective available potential energy (CAPE) estimates</li> </ul> <p>The dataset is accompanied by elevation and coordinate information. </p> <p>Please refer to the manuscript and the <a href="https://github.com/caglarkucuk/earthformer-multisource-to-inca">GitHub repository</a> for further information and helper code for reading the data files.</p>
Reproducibility Test Data of 26 MHub Models
<p>This dataset provides a comprehensive collection of test data for 26 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> <li><strong>Test.yml File:</strong> This file includes the original contributor’s test setup, which has been accepted by the MHub team.</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>Purpose and Utility:</h3> <p>The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.</p> <p>This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.</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>
Data: Limiting hearing loss in transgenic mouse models
<p>A collection of post-processing data chunks used for generating figures for the publication <em>Limiting hearing loss in transgenic mouse models. </em>Each folder corresponds to an individual mouse involved in the study and includes datum generated from widefield and two-photon imaging (ROIs, flourescence values, etc.).</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.