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60 results for “multiscale modeling”
Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease
<p>MEA recordings from wild-type and 5xFAD mice retinas used for the analyses in the manuscript "Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease".</p>
Multiscale modelling of flow, heat transfer and transformation during thermal treatment of starch suspensions
<p>Multiscale modelling of flow, heat transfer and transformation during thermal treatment of starch suspensions</p>
Databases for "A multiscale approach to identify key factors determining rural WaSH service level and sustainability" (ShinyApp). Application to SIASAR conceptual model v1, outputs obtained from Nicaragua, Honduras, Panama and Dominican Republic SIASAR databases (last accessed: December 21, 2016)".
<p>Attached databases contain the results obtained by applying the SIASAR composite indicators v1 (available at http://upcommons.upc.edu/handle/2117/77587) to data presented in http://doi.org/10.5281/zenodo.571351</p> <p>These files can be directly exploited by using the ShinyApp “A multiscale approach to identify key factors determining rural WaSH service level and sustainability”.</p> <p>Each *.csv file contains one country's database. </p>
dateset for "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)"
<p>Here is the dataset for the paper named "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)".</p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Supplementary Files for Journal -- Using multiscale molecular modeling to analyze possible NS2b-NS3 protease inhibitors from medicinal plants endemic to the Philippines
<p>Table S1. ADMET and toxicity results of the test ligands.</p> <p>Table S2. Docking results of test ligands and references on NS2b-NS3 protease (2FOM) using Autodock 4.2.</p> <p>Table S3. Decomposition of binding free energy (kJ/mol) on a per residue basis of the complex with strongest MM/PBSA energies</p>
Data from: A multiscale approach to detect selection in non-model tree species: widespread adaptation despite population decline in Taxus baccata L.
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Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model
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Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models
<p>This is a set of three multiscale elevation models, each consisting of a set of elevation grids, centered on the same geographic location, with increasing cell sizes and spatial extents. The centers are Gore Range, Colorado, USA; Valdez, Alaska, USA; Churfirsten, Switzerland. The elevation data is from a variety of sources. For the United States, the National Elevation Dataset (NED) by the U.S. Geological Survey (USGS) was used, which itself combines data created with a variety of techniques such as LiDAR and interferometric synthetic aperture radar (IFSAR). Data from www.viewfinderpanoramas.org was used for areas outside the USA and at small scales. The size of each grid is 1500×1500 height samples for the Gore Range and Valdez models and 2500×1500 height values for the Churfirsten model. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats.</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication - Dataset
<p>Data set used for the paper entitled "Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication"</p> <p>The associated paper is archived on an open access aichive: https://hal.archives-ouvertes.fr/hal-03402682</p>
Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata
<p>Genetic and Environmental datasets used to perform population structure, isolation-by-distance, and GLMM analysis. See the paper for abbreviations and units of DEM-derived variables.</p>
Multiscale modelling of droplet collisions in spray draying
<p>Multiscale modelling of droplet collisions in spray draying</p>
Multiscale modeling of oxidation process during deep-frying
<p>Multiscale modeling of oxidation process during deep-frying</p>
Multiscale modeling of oat and wheat stems under wind stress
<p>Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modeling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behavior of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behavior with the physical traits of the plants. To further investigate we created an independent finite element simulation framework integrating our recently developed multiscale material modeling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics, and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multiscale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.</p>
Multiscale modeling of oat and wheat stems under wind stress
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Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (4/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>4 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term Medication
ClinicalTrials.gov study NCT06865755. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Efficient multiscale-model construction using machine learning: introducing the CarveAdornCurate cloud-based platform
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Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF - Erronous
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 2,500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p>
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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.