Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “Reduced Order Models”
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"
<p>Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023).</p> <p>The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1</p> <p> </p>
Accompanying data for the paper "Reduced order modeling of geometrically nonlinear rotating structures using the direct parametrisation of invariant manifolds"
<p>Links</p><ul><li>isSupplementTo <i>publication-article</i> <a href="https://doi.org/10.46298/jtcam.10430">https://doi.org/10.46298/jtcam.10430</a></li><li>isSupplementedBy <i>software</i> <a href="https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147">https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147</a></li></ul><p>Language</p><ul><li>English</li></ul><p>License</p><ul><li>Creative Commons Attribution 4.0</li></ul><p>Contributions</p><ul><li>Adrien MARTIN carried out the main part of study, defined the examples, performed the numerical simulations and drafted the manuscript;</li><li>Andrea OPRENI and Alessandra VIZZACCARO developed the methodology and built the main parts of the Julia code implementing the reduction method;</li><li>Andrea OPRENI developed the first version of the HBFEM code which has been updated for rotation in collaboration with Adrien MARTIN;</li><li>Marielle DEBEURRE performed all the simulations shown in Appendix C related to the Timoshenko beam model with continuation;</li><li>Loïc SALLES supervised the work, discussed applications to blades, and helped in designing and understanding the twisted plate model;</li><li>Attilio FRANGI supervised the work and help in the development of the methodology;</li><li>Olivier THOMAS helped in all discussions related to the comparisons with the thin beam example and wrote Appendix C;</li><li>Cyril TOUZE supervised the work, carried out most of the writing and developed the methodology;</li></ul><p>All authors read and approved the final manuscript.</p><p>Data collection: period and details</p><ul><li>Datasets produced between September and December 2022</li></ul><p>Funding sources</p><ul><li>Funding from AID (Agence de l'Innovation de Défense), project REMODEL, contract number 2020 65 0057 ENSTA</li></ul><p>Data structure and information</p><ul><li>README.md: Contains the general information concerning this dataset</li></ul><p>Figures</p><ul><li>fig_1: description of the rotating beam</li><li>fig_2(a,b,c,d): Linear characteristics of the rotating cantilever beam</li><li>fig_3(a,b): FRC of the rotating cantilever beam around 1F mode</li><li>fig_4: Convergence of the non-autonomous part of DPIM for the 1F mode</li><li>fig_5(a,b,c,d,e,f): Interpolation of the coefficients of the autonomous ROM</li><li>fig_6(a,c): Hardening/softening behaviour of the rotating beam; fig 6b is a zoom on fig 6a</li><li>fig_7(a,b,c): Comparisons of FRCs obtained from interpolated ROMs with FOM solution</li><li>fig_8a: FRC of the rotating cantilever beam around 2F mode; fig 8b is a zoom of fig 8a</li><li>fig_9(a,b,c,d): fig 9 a-b-c : geometry of the blade and some modes and static displacements; fig 9d : Campbell diagram of the blade</li><li>fig_10: FRC of the twisted plate</li><li>fig_11(a,b,c): Computing time and convergence analysis with respect to mesh refinement for the fan blade</li></ul><p>fig_12(a,b,c,d): FRC of interpolated ROMs with increasing degrees compared to reference solution</p><p>fig_A_1: Campbell diagram of the beam : impact of Coriolis effects</p><ul><li>fig_C_3(a,b,c,d,e,f,g,h,i): Comparison of the results on the beam studied between DPIM and article from Thomas for 1F and 2F modes</li><li>fig_C_2(a, b): Comparison of the results on the beam studied between : DPIM, article from Thomas and results from Debeurre</li></ul>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
Data for "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations"
<p>Data, figure generation scripts, and zero-dimensional version of the 17 species Biogeochemical Flux Model (BFM17) for the paper "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations" submitted to Geoscientific Model Development. </p>
Geometric and Physical Reduced Order Modeling Applied to CFD Results
<p>Short videos illustrating the results from the ROM paper</p>
Ill conditioned matrix for Reduced Order Model
<p>Matrix for Random-SVD ROM</p>
Supplemental materials for "Boosting Barlow Twins reduced order modeling for machine learning-based surrogate models in multiphase flow problems"
<p>Supplemental materials for "Boosting Barlow Twins reduced order modeling for machine learning-based surrogate models in multiphase flow problems" in Water Resources Research. Detailed information is available in readme.md.</p>
Datasets of paper "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation"
<p>These are the datasets, processing scripts, and plots that are used in the paper titled "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation" submitted to the JAMES.</p>
Data in support of Plume Dynamics Reduced-Order Models
<p>This split tarball contains the data used in experiments in the paper "Coarse Graining and Reduced Order Models for Plume Dynamics". Unzipping the tarball in the experiment repository, or symlinking from the repository to the unzipped directory "plume_videos", will allow experiments to run.</p> <p>Data is laid out as it is in our machine. This includes movie files, numpy arrays of movies, and pickled coordinates of plume origins. </p> <p>Tarball is fragmented using the unix split command. Run `cat x* | tar -xzvf -`. See https://unix.stackexchange.com/questions/61774/create-a-tar-archive-split-into-blocks-of-a-maximum-size</p>
Multi-fidelity reduced-order surrogate modeling
<p>Training and testing datasets used for the experiments in <a href="https://arxiv.org/abs/2309.00325">Multi-fidelity reduced-order surrogate modeling</a></p>
Data from the thesis: Reduced-order models to predict mesoscale mechanical behavior of polycrystalline materials
<p>This record contains the data and code from the thesis: Reduced-order models to predict mesoscale mechanical behavior of polycrystalline materials. The contents of the chapter-wise zip files are described in the respective markdown files with the suffix <strong><em>_readme.md</em></strong>.</p> <p> </p> <p>A record containing only the code from the thesis is availabe at: <a href="https://doi.org/10.5281/zenodo.10983507" target="_blank" rel="noopener">10.5281/zenodo.10983507</a>.</p>
Reduced-Order Model of Time-Projection Chamber
<p>These datasets are generated by a <strong>reduced-order model (ROM) of the sPHENIX Time-projection chamber (TPC)</strong>, inner layer group. We have two datasets:</p> <ol> <li>`dataset.zip` and</li> <li>`dataset_with_label.zip`.</li> </ol> <p>Each dataset contains 8000 training examples and 2000 test examples. Each sample in the dataset contains ~50 to 100 trajectories. Samples in both dataset contain fields: `response`, `tracks`, and `params `. Samples in the `dataset_with_label.zip` has an additional field `label`. </p> <p>Here are the description of fields:</p> <ul> <li>`response` (shared): float tensor of shape (16, 256, 1152) in radial (layer), axial (horizontal), and azimuthal directions. </li> <li>`tracks` (shared): float tensor of shape (N, 2, 16), where N is the number of trajectories. Each trajectory is recorded as a tensor of shape (2, 16). The two numbers for each layer (last axis) is the angle (in the azimuthal direction) and axial location of the trajectory. </li> <li>`params` (shared): float tensor of shape (N, 4). The four numbers for the n-th entry are initial momentum (3D) and mass-charge ratio (scalar) of the particle that generates the n-th trajectory.</li> <li>`label` (`dataset_with_label` only): integer tensor of shape (16, 256, 1152), the value for voxels <strong>with positive response</strong> is the trajectory id that contribute the most to the voxel. The value for voxels with zero response is random and should not be used.</li> </ul>
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.