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ShareScore release 0.9.0
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52 results for “Matrix models”
Optical modeling of nonspherical pine and mugwort pollen aerosols using the invariant imbedding T-matrix and improved geometric optics methods
<p><strong>Data for </strong></p> <p><strong><em>Optical modeling of nonspherical pine and mugwort pollen aerosols using the invariant imbedding T-matrix and improved geometric optics methods</em>.</strong></p> <p> </p> <p>Mail:</p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></p>
Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations
<p><strong>Data for publication:</strong></p> <p><em><strong>Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations.</strong></em></p> <p>Mail:</p> <p>sato@riam.kyushu-u.ac.jp </p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></p>
Ill conditioned matrix for Reduced Order Model
<p>Matrix for Random-SVD ROM</p>
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing
<p><span>This is the dataset for the NCST project "</span><span>MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing</span><span>"</span></p> <p> </p> <p><span>The Georgia Tech research team developed MOVES-Matrix 3.0 based on the EPA's MOVES3 (version 3.1.0) energy use and emission rate model by running MOVES3 thousands of times on the PACE supercomputing cluster across all combinations of input variables and storing the output as lookup tables.<span> </span>MOVES-Matrix 3.0 allows on-road energy consumption and emissions modeling to be conducted more than 800 times faster than running MOVES, while it generates the exact same results, as verified in this report. <span> </span>MOVES-Matrix 3.0 was designed similarly to its predecessor, MOVES-Matrix 2014, but required extensive code modifications to accommodate changes in the MOVES3 environment (including a shift from MySQL to MariaDB and incorporation of new vehicle source sub-types and operating parameters). <span> </span>The review of the fuel and I/M scenarios indicated that MOVES3 now defines 122 modeling regions, as compared with 109 regions in MOVES 2014b (different matrices need to be developed each modeling region).<span> </span>The development of matrices for each modeling region takes approximately 15-20 days on the PACE supercomputing cluster given our assigned resources (compared with only 5-7 days to develop matrices for MOVES 2014). <span> </span>A case study of 3,000 roadway links using Atlanta's matrices confirmed that MOVES-Matrix 3.0 produces the exact same energy consumption and emissions results as MOVES3, but execution modules operate 800 times faster using MOVES-Matrix lookups than running MOVES for any single run.</span></p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
Open the record for dataset details and reuse information.
Data from: Matrix models of hierarchical demography: linking group- and population-level dynamics in cooperative breeders
Open the record for dataset details and reuse information.
Model files for AI Matrix benchmarks
<p>The model files for partial AI matrix benchmarks</p>
Overexpression of extracellular matrix proteins, increased cell death and reduced cell proliferation contribute to the pathophysiology of microphthalmia across in vitro patient-derived models
GEO Series GSE265940. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Cell-matrix interactions control biliary organoids polarity,architecture and differentiation and can be exploited to improve modelling of biliary diseases.
GEO Series GSE204960. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
The tissue-engineered human cornea as a model to study expression of matrix metalloproteinases during corneal wound healing
GEO Series GSE75336. Homo sapiens. 27 samples. Type: Expression profiling by array.
Minoxidil alters extracellular matrix gene expression, improving vascular compliance and organ perfusion in a model of chronic vascular stiffness
GEO Series GSE110296. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Differential Expression of Chemokine and Matrix Re-Modelling Genes Explains Contrasting Schistosoma japonicum-induced Hepatopathology in Murine Models
GEO Series GSE25713. Mus musculus. 24 samples. Type: Expression profiling by array.
Transcriptomic analyses of joint tissues during osteoarthritis development in a rat model reveal dysregulated mechanotransduction and extracellular matrix pathways
GEO Series GSE241794. Rattus norvegicus. 144 samples. Type: Expression profiling by high throughput sequencing.
Intraductal Xenografts Model Lobular Carcinoma of the Breast and Reveal Matrix Remodeling by LOXL1 as a Targetable Hallmark II
GEO Series GSE149675. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Intraductal Xenografts Model Lobular Carcinoma of the Breast and Reveal Matrix Remodeling by LOXL1 as a Targetable Hallmark I
GEO Series GSE149671. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Cell-matrix interactions control biliary organoids polarity,architecture and differentiation and can be exploited to improve modelling of biliary diseases [bulk RNA-seq]
GEO Series GSE204958. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Supplementary Data for Paper "Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo"
<p>This dataset contains data needed to reproduce all calculations and plots published in paper "Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo", B. Mazur, L. Firlej, and B. Kuchta, 2024, ACS Appl. Mater. Interfaces, DOI: 10.1021/acsami.4c02616. </p>
ScienceDex guides
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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.