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5,805 results for “Data model”
Models and Data for "Approximate Atmospheric Scattering using PINNs"
Open the record for dataset details and reuse information.
SpectralGPT: The first remote sensing foundation model customized for spectral data
<p>SpectralGPT is the first purpose-built foundation model designed explicitly for spectral RS data. It considers unique characteristics of spectral data, i.e., spatial-spectral coupling and spectral sequentiality, in the MAE framework with a simple yet effective 3D GPT network.</p> <p>We will gradually release the trained models (SpectralGPT, SpectralGPT+), the new benchmark dataset (SegMunich) for the downstream task of semantic segmentation, original code, and implementation instructions.</p>
OpenFOAM test result data accompanying 'Numerical representation of mountains in atmospheric models'
<p>OpenFOAM test result data accompanying the PhD thesis, 'Numerical representation of mountains in atmospheric models'</p>
Model Data of NEMO-PISCES
<p>This repository makes available the model output used in the manuscript entitled "The Biological Pump and Seasonal Variability of pCO2 in the Southern Ocean : Exploring the Role of Diatom Adaptation to Low Iron" by Person et al. The output files are in netcdf format and the variables are documented as metadata in the files. Any supplementary informations can be obtained by contacting Renaud Person.</p>
Machine Learning Models for cfMeDIP data from Shen et al.
<p>Contains raw markdowns and knit markdown with plots for machine learning analyses in Shen et al, "<strong>Highly sensitive tumor detection and classification using methylome analysis of plasma cell-free DNA".</strong></p>
X-ray CT based FEM model data of non-crimp fabric based fibre composite
<p>The current data is published alongside the below ECCM 2018 conference contribution:</p> <p>Jespersen, K. M., Asp, L. E., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). X-ray tomography based finite element modelling of non-crimp fabric based fibre composite. In 18th European Conference on Composite Materials (pp. 1–8).</p>
Epithelial restitution defect in neonatal jejunum is rescued by juvenile mucosal homogenate in a pig model of intestinal ischemic injury and repair, Supporting Data Files
<p>Supporting data for PLOS One 2018 manuscript PONE-S-18-23597</p>
Data associated to the study entitled:"Alterations of the nigrostriatal pathway in a 6-OHDA rat model of Parkinson's disease evaluated with multimodal MRI"
<p>Parkinson’s disease is characterized by neurodegeneration of the dopaminergic neurons in the substantia nigra pars compacta. The 6-hydroxydopamine (6-OHDA) rat model has been used to study neurodegeneration in the nigro-striatal dopaminergic system. The goal of this study was to evaluate the reliability of diffusion MRI and resting-state functional MRI biomarkers in monitoring neurodegeneration in the 6-OHDA rat model assessed by quantitative histology.</p> <p>We performed a unilateral injection of 6-OHDA in the striatum of Sprague Dawley rats to produce retrograde degeneration of the dopamine neurons in the substantia nigra pars compacta. We carried out a longitudinal study with a multi-modal approach combining structural and functional MRI together with quantitative histological validation to follow the effects of the lesion. Functional and structural connectivity were assessed in the brain of 6-OHDA rats and sham rats (NaCl injection) at 3 and 6 weeks post-lesioning using resting-state functional MRI and diffusion-weighted.</p> <p>The shared datafile corresponds to the MRI biomarkers extracted from diffusion and functional acquisitions as well as from histological mesaurements within striatum and substantia nigria.</p>
Rapid T1 quantification from high resolution 3D data with model-based reconstruction
<p>In-vivo datasets used in the work "Rapid T1 quantification from high resolution 3D data with model-based reconstruction" with DOI: 10.1002/mrm.27502<br> </p>
Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations
<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p> </p>
Compound data sets for support vector machine and regression modeling
<p>Provided are compound data sets used for support vector machine and support vector regression modeling and associated information.</p>
Data for Characterization of cytokine response to intraperitoneally administered LPS & subdiaphragmatic branch vagus nerve stimulation in rat model
<p>These are the data files used for publication "Characterization of cytokine response to intraperitoneally administered LPS & subdiaphragmatic branch vagus nerve stimulation in rat model" to be published in PLOS ONE.</p>
PROPTI - An Generalised Inverse Modelling Framework - Data Set
<p><strong>Contents</strong></p> <p>Data set supplementary to the conference paper "<a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">PROPTI - A Generalised Inverse Modelling Framework</a>", presented at the <em>3rd European Symposium on Fire Safety Science</em>, ESFSS 2018 in Nancy, France.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis script to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 260 MB.</p>
Simulation data output used in Griffiths and Phelps lightning initiation model, revisited
<p>Simulation data output used in the paper titled "Griffiths and Phelps Lightning Initiation Model, Revisited" submitted for publication in JGR by A. Attanasio, P. R. Krehbiel, and C. L. da Silva.</p> <p>The model simulates the collective dynamics of a system of positive streamers using the framework first proposed by Griffiths and Phelps [1976]. The README.txt file contains details about the data structure.</p> <p>C. L. da Silva, Jan/29/2019</p>
Data used in "Seasonality of Intraseasonal Variability in CMIP5 and Nonhydrostatic Atmospheric Global Models" by Nakano and Kikuchi (2019) submitted to GRL
<p>PCs time series and NICAM-AMIP 2.5 degree gridded data used in Nakano and Kikuchi (2019) submitted to GRL.</p>
Data and analysis for "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models"
<p>This is an archive of the raw data and analysis source code for the paper "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models". The archive contains:</p> <ul> <li><strong>devel.Rmd : </strong>Source code for the worksheet that contains the early development and figures for the paper.</li> <li><strong>devel.html</strong> : HTML rendering of devel.Rmd</li> <li><strong>lg-ensemble-stats.Rmd </strong>: Source code for the worksheet that contains the statistical analysis described in the paper.</li> <li><strong>lg-ensemble-stats.html</strong> : HTML rendering of lg-ensemble-stats.Rmd</li> <li><strong>cc-analysis.Rmd </strong>: Analysis of the compromise conjecture raised by some readers of the paper</li> <li><strong>cc-analysis.nb.html</strong> : HTML rendering of cc-analysis.Rmd</li> <li><strong>data.tar.bz2 </strong>: Input data for the analyses above.</li> </ul> <p>The source code in this archive is written in R and requires the R runtime environment. It also uses the fldgen package, version 1.0.0, which is available at <a href="https://github.com/JGCRI/fldgen">https://github.com/JGCRI/fldgen</a></p> <p> </p>
Supporting data for: A 250-year European drought inventory derived from ensemble hydrologic modelling
<p>Supporting data for visualization of 250-year (1766-2015) inventory of European meteorological, hydrological and agricultural droughts derived from ensemble simulations of the mesoscale Hydrological Model (mHM)</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square lattice on a (t,U) grid.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>greens function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>inverse temperature 10.0</li> <li>U 0.0 to 2.7 in steps of 0.1</li> <li>t from 1.0 to 1.48 in steps of 0.02</li> </ul> <p>All simulations are done for half filling.</p> <p>The data are used in the publication "Thermodynamics of the metal-insulator transition in the extended Hubbard model" available on the arXiv (arXiv:1903.09947). There it is used to do an extrapolation of finite size and finite trotter errors and calculate the free energy by integrating the double occupation.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Supporting data for "Quantifying process connectivity with transfer entropy in hydrologic models" [Paper #2018WR024555]
<p>This tarball contains the processed datasets used for the analysis of the process networks. It also contains the shapefiles used to define the domains of each sub-region.</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.