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<div class="header">
<h1>Open Access Mechanics Datasets</h1>
</div>
<div>
<h3> Goals: </h3>
<ol>
<li> Provide benchmark datasets for machine learning methods applied to mechanics problems. </li>
<li> Provide "mechanics relevant" examples for students getting started with machine learning. </li>
<li> Make data-driven problems in mechanics more accessible to the broad research community. </li>
</ol>
<h3> Datasets: </h3>
<ol>
<li> <b>Mechanical MNIST</b> <br>
<b>Data:</b> <a href="https://open.bu.edu/handle/2144/39371" target="_blank" rel="noopener noreferrer" > https://open.bu.edu/handle/2144/39371 </a> <br>
<b>Code:</b> <a href="https://github.com/elejeune11/Mechanical-MNIST" target="_blank" rel="noopener noreferrer" > https://github.com/elejeune11/Mechanical-MNIST </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.eml.2020.100659" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.eml.2020.100659 </a> <br>
<b>Recommended Challenge:</b> predict scalar and full field simulation outputs from input bitmaps describing heterogeneous material properties. </li>
<li> <b>Buckling Instability Classification (BIC)</b> <br>
<b>Data:</b> <a href="https://open.bu.edu/handle/2144/40085" target="_blank" rel="noopener noreferrer" > https://open.bu.edu/handle/2144/40085 </a> <br>
<b>Code:</b> <a href="https://github.com/elejeune11/BIC" target="_blank" rel="noopener noreferrer" > https://github.com/elejeune11/BIC </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.cad.2020.102948" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.cad.2020.102948 </a> <br>
<b>Recommended Challenge:</b> predict if a column is "stable" or "unstable" from input vectors describing heterogeneous material properties. </li>
<li> <b>Right Ventricular Myocardial Mechanics (RV Mechanics)</b> <br>
<b>Data:</b> <a href="https://dataverse.tdl.org/dataverse/RVMechanics" target="_blank" rel="noopener noreferrer" > https://dataverse.tdl.org/dataverse/RVMechanics </a> <br>
<b>Code:</b> n/a (all experimental data) <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.actbio.2020.12.006" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.actbio.2020.12.006 </a> <br>
<b>Recommended Challenge:</b> define a predictive data-driven <a href="https://en.wikipedia.org/wiki/Constitutive_equation" target="_blank" rel="noopener noreferrer" >constitutive equation</a> for fibrous soft tissue. </li>
<li> <b>Blood Clot Simple Shear Testing Data</b> <br>
<b>Data:</b> <a href="https://dataverse.tdl.org/dataverse/BloodClotSimpleShear" target="_blank" rel="noopener noreferrer" > https://dataverse.tdl.org/dataverse/BloodClotSimpleShear </a> <br>
<b>Code:</b> n/a (all experimental data) <br>
<b>Manuscript:</b> <a href="https://www.sciencedirect.com/science/article/pii/S1751616120307566" target="_blank" rel="noopener noreferrer" > https://www.sciencedirect.com/science/article/pii/S1751616120307566 </a> <br>
<b>Recommended Challenge:</b> define a predictive data-driven <a href="https://en.wikipedia.org/wiki/Constitutive_equation" target="_blank" rel="noopener noreferrer" >constitutive equation</a> for isotropic soft tissue. </li>
<li> <b>3D Printed Crossed Barrel Dataset</b> <br>
<b>Data:</b> <a href="https://www.kablab.org/data" target="_blank" rel="noopener noreferrer" > https://www.kablab.org/data </a> <br>
<b>Code:</b> n/a (experimental data and ABAQUS simulations) <br>
<b>Manuscript:</b> <a href="https://advances.sciencemag.org/content/6/15/eaaz1708" target="_blank" rel="noopener noreferrer" > https://advances.sciencemag.org/content/6/15/eaaz1708 </a> <br>
<b>Recommended Challenge:</b> Find the optimal crossed barrel design with the lowest number of experiments. </li>
<li> <b>Young and Aged Mouse Skin Biaxial Mechanical Tests</b> <br>
<b>Data:</b> <a href="https://dataverse.tdl.org/dataverse/mouseskinmechanics1" target="_blank" rel="noopener noreferrer" > https://dataverse.tdl.org/dataverse/mouseskinmechanics1 </a> <br>
<b>Code:</b> n/a (all experimental data) <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.actbio.2019.10.020" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.actbio.2019.10.020 </a> <br>
<b>Recommended Challenge:</b> define a predictive data-driven <a href="https://en.wikipedia.org/wiki/Constitutive_equation" target="_blank" rel="noopener noreferrer" >constitutive equation</a> for planar soft tissue.
<li> <b>Asymmetric Buckling Columns (ABC) dataset</b> <br>
<b>Data:</b> <a href="https://open.bu.edu/handle/2144/43730" target="_blank" rel="noopener noreferrer" > https://open.bu.edu/handle/2144/43730 </a> <br>
<b>Code:</b> <a href="https://github.com/pprachas/ABC_dataset" target="_blank" rel="noopener noreferrer" > https://github.com/pprachas/ABC_dataset </a> <br>
<b>Manuscript:</b> <a href="https://arxiv.org/abs/2202.01380" target="_blank" rel="noopener noreferrer" > https://arxiv.org/abs/2202.01380 </a> <br>
<b>Recommended Challenge:</b> predict the buckling direction (left vs. right) from a spatially heterogenous column geometry.
<li> <b>Crack Propagation in Steel Structures Under Ultra-Low Cycle Fatigue</b> <br>
<b>Data:</b> <a href="https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394" target="_blank" rel="noopener noreferrer" > https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394 </a> <br>
<b>Code:</b> <a href="https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394" target="_blank" rel="noopener noreferrer" > https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394 </a> <br>
<b>Manuscript:</b> <a href="https://stacks.stanford.edu/file/druid:ft563mt0968/Dissertation-augmented.pdf" target="_blank" rel="noopener noreferrer" > https://stacks.stanford.edu/file/druid:ft563mt0968/Dissertation-augmented.pdf </a> <br>
<b>Recommended Challenge:</b> predict output QoIs for the "blind prediction" experimental dataset.
<li> <b>Ductile Fracture in Structural Steel Inclined Notch Specimens</b> <br>
<b>Data:</b> <a href="https://purl.stanford.edu/qy227tf3022" target="_blank" rel="noopener noreferrer" > https://purl.stanford.edu/qy227tf3022 </a> <br>
<b>Code:</b> <a href="https://purl.stanford.edu/qy227tf3022" target="_blank" rel="noopener noreferrer" > https://purl.stanford.edu/qy227tf3022 </a> <br>
<b>Manuscript:</b> <a href="https://stacks.stanford.edu/file/druid:qy227tf3022/TR187_Smith.pdf" target="_blank" rel="noopener noreferrer" > https://stacks.stanford.edu/file/druid:qy227tf3022/TR187_Smith.pdf </a> <br>
<b>Recommended Challenge:</b> predict force vs. displacement curves and fracture probability for held out experimental samples.
<li> <b>Cardiac Motion Analysis Challenge</b> <br>
<b>Data:</b> <a href="http://www.cardiacatlas.org/challenges/motion-tracking-challenge/" target="_blank" rel="noopener noreferrer" > http://www.cardiacatlas.org/challenges/motion-tracking-challenge/ </a> <br>
<b>Code:</b> n/a <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.media.2013.03.008" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.media.2013.03.008 </a> <br>
<b>Recommended Challenge:</b> predict cardiac motion and strain from magnetic resonance (MR) and 3D ultrasound (3DUS) imaging datasets from a dynamic phantom and 15 healthy volunteers.
</li>
<li> <b>Unsupervised Discovery of Interpretable Hyperelastic Constitutive Laws</b> <br>
<b>Data:</b> <a href="https://doi.org/10.3929/ethz-b-000505693" target="_blank" rel="noopener noreferrer" > https://doi.org/10.3929/ethz-b-000505693 </a> <br>
<b>Code:</b> <a href="https://euclid-code.github.io/" target="_blank" rel="noopener noreferrer" > https://euclid-code.github.io/ </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.cma.2021.113852" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.cma.2021.113852 </a> <br>
<b>Recommended Challenge:</b> discover strain energy density functions from emulated DIC data created via the finite element method.
</li>
<li> <b>Discovering Plasticity Models without Stress Data</b> <br>
<b>Data:</b> <a href="https://doi.org/10.3929/ethz-b-000534002" target="_blank" rel="noopener noreferrer" > https://doi.org/10.3929/ethz-b-000534002 </a> <br>
<b>Code:</b> <a href="https://euclid-code.github.io/" target="_blank" rel="noopener noreferrer" > https://euclid-code.github.io/ </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1038/s41524-022-00752-4" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1038/s41524-022-00752-4 </a> <br>
<b>Recommended Challenge:</b> discover plastic yield surfaces and hardening laws from emulated DIC data created via the finite element method.
</li>
<li> <b>Discrete Element Traction-Separation Data</b> <br>
<b>Data:</b> <a href="https://data.mendeley.com/datasets/n5v7hyny8n/1" target="_blank" rel="noopener noreferrer" > https://data.mendeley.com/datasets/n5v7hyny8n/1 </a> <br>
<b>Code:</b> <a href="https://www.poromechanics.org/software--data.html" target="_blank" rel="noopener noreferrer" > https://www.poromechanics.org/software--data.html </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.cma.2018.11.026" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.cma.2018.11.026 </a> <br>
<b>Recommended Challenge:</b> derive theory-consistent and microstructure-based traction–separation laws.
</li>
<li> <b>Experimental Quasi-Static and Impact Testing of Lattices</b> <br>
<b>Data:</b> <a href="https://www.kablab.org/lattice-quasi-static" target="_blank" rel="noopener noreferrer" > https://www.kablab.org/lattice-quasi-static </a> and <a href="https://www.kablab.org/lattice-impact" target="_blank" rel="noopener noreferrer" > https://www.kablab.org/lattice-impact </a> <br>
<b>Code:</b> n/a <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.matt.2022.06.051" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.matt.2022.06.051 </a> <br>
<b>Recommended Challenge:</b> leverage quasi-static tests to make predictions relevant to impact test outcomes.
</li>
<li> <b>Zero Modes and Classification of Combinatorial Metamaterials</b> <br>
<b>Data:</b> <a href="https://doi.org/10.5281/zenodo.7070963" target="_blank" rel="noopener noreferrer" > https://doi.org/10.5281/zenodo.7070963 </a> and <a href="https://doi.org/10.5281/zenodo.7071282" target="_blank" rel="noopener noreferrer" > https://doi.org/10.5281/zenodo.7071282 </a> <br>
<b>Code:</b> <a href="https://uva-hva.gitlab.host/published-projects/CNN_MetaCombi" target="_blank" rel="noopener noreferrer" > https://uva-hva.gitlab.host/published-projects/CNN_MetaCombi </a> and <a href="https://uva-hva.gitlab.host/published-projects/CombiMetaMaterial" target="_blank" rel="noopener noreferrer" > https://uva-hva.gitlab.host/published-projects/CombiMetaMaterial </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1103/PhysRevLett.129.198003" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1103/PhysRevLett.129.198003 </a> <br>
<b>Recommended Challenge:</b> classify each kxk unit cell design into one of two classes (frequent incompatible class I) or (rare compatible class C).
</li>
<li> <b>Comparisons for Neural Operators</b> <br>
<b>Data:</b> <a href="https://github.com/lu-group/deeponet-fno" target="_blank" rel="noopener noreferrer" > https://github.com/lu-group/deeponet-fno </a> <br>
<b>Code:</b> <a href="https://github.com/lu-group/deeponet-fno" target="_blank" rel="noopener noreferrer" > https://github.com/lu-group/deeponet-fno </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1016/j.cma.2022.114778" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1016/j.cma.2022.114778 </a> <br>
<b>Recommended Challenge:</b> benchmark neural operators across 16 examples.
</li>
<li> <b>NeuroImaging Tools & Resources Collaboratory: Brain Biomechanics Imaging Resources </b> <br>
<b>Data:</b> <a href="https://www.nitrc.org/projects/bbir/" target="_blank" rel="noopener noreferrer" > https://www.nitrc.org/projects/bbir/ </a> <br>
<b>Code:</b> <a href="https://www.nitrc.org/projects/bbir/" target="_blank" rel="noopener noreferrer" > https://www.nitrc.org/projects/bbir/ </a> <br>
<b>Manuscript:</b> <a href="https://doi.org/10.1007/s10439-021-02820-0" target="_blank" rel="noopener noreferrer" > https://doi.org/10.1007/s10439-021-02820-0 </a> <br>
<b>Recommended Challenge:</b> predict subject specific mechanical response of the human brain to in vivo loading.
</li>
</ol>
<h3> Information for contributors: </h3>
<ol>
<li>Consulting <a href="https://www.go-fair.org/fair-principles/" target="_blank" rel="noopener noreferrer" >FAIR Principles</a> (guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets) is often helpful for effective data curation.</li>
<li>Please make sure that the provided links will be long lasting. DOIs or handles can often be obtained through an institutional repository. </li>
<li>Please be thoughtful about selecting licenses for <a href="https://en.wikipedia.org/wiki/Creative_Commons_license" target="_blank" rel="noopener noreferrer" >data</a> and <a href="https://en.wikipedia.org/wiki/Comparison_of_free_and_open-source_software_licences" target="_blank" rel="noopener noreferrer" >code</a>. For the <a href="https://open.bu.edu/handle/2144/39371" target="_blank" rel="noopener noreferrer" >Mechanical MNIST</a> dataset, we chose a <a href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank" rel="noopener noreferrer" >Creative Commons Attribution-ShareAlike 4.0 License</a> for the data, and a <a href="https://en.wikipedia.org/wiki/MIT_License" target="_blank" rel="noopener noreferrer" >MIT License</a> for the code (we do not endorse any particular license choice).</li>
<li>Keep file sizes small whenever possible. For example, it may make sense to create a <a href="https://docs.h5py.org/en/stable/quick.html" target="_blank" rel="noopener noreferrer" >HDF5 file</a>. </li>
<li>For the “Recommended Challenge,” data should be curated so that a user with limited familiarity can download all or part of the dataset and start working on the problem in just a few minutes. Think of the challenge as a starting point for what could be done with the data and code provided. Multiple datasets can have the same recommended challenge.</li>
<li>If you have an interesting problem where regenerating the dataset from the provided code is quick and easy (i.e., can be done on a laptop in a few minutes by a user with no special expertise and with no need to install software other than Python and standard Python packages), we can link to just the code.</li>
<li>It is not necessary to provide a link to a published manuscript. However, citing relevant manuscripts is one way to acknowledge the use of open datasets and code.</li>
</ol>
<h3> Links to other relevant pages: </h3>
<ol>
<li> <b>DesignSafe Data Depot:</b> <a href="https://www.designsafe-ci.org/data/browser/public/" target="_blank" rel="noopener noreferrer" > https://www.designsafe-ci.org/data/browser/public/ </a> </li>
<li> <b>NanoMine:</b> <a href="https://materialsmine.org/wi/home" target="_blank" rel="noopener noreferrer" > https://materialsmine.org/wi/home </a> </li>
<li> <b>NIST Materials Data Facility:</b> <a href="https://www.materialsdatafacility.org/" target="_blank" rel="noopener noreferrer" > https://www.materialsdatafacility.org/ </a> </li>
<li> <b>Awesome Materials Informatics:</b> <a href="https://github.com/tilde-lab/awesome-materials-informatics" target="_blank" rel="noopener noreferrer" > https://github.com/tilde-lab/awesome-materials-informatics </a> </li>
<li> <b>PDEBench:</b> <a href="https://github.com/pdebench/PDEBench" target="_blank" rel="noopener noreferrer" > https://github.com/pdebench/PDEBench </a> and <a href="https://arxiv.org/abs/2210.07182" target="_blank" rel="noopener noreferrer" > https://arxiv.org/abs/2210.07182 </a> (datasets: advection, Burgers', diffusion-reaction, diffusion-sorption, compressible Navier-Stokes, incompressible Navier-Stokes, Darcy flow, shallow-water) </li>
<li> <b>PDEArena:</b> <a href="https://github.com/microsoft/pdearena" target="_blank" rel="noopener noreferrer" > https://github.com/microsoft/pdearena </a> and <a href="https://arxiv.org/abs/2209.15616" target="_blank" rel="noopener noreferrer" > https://arxiv.org/abs/2209.15616 </a> (datasets: shallow water equations, velocity function formulation of Navier-Stokes equation) </li>
<li> <b>NVIDIA Modulus:</b> <a href="https://developer.nvidia.com/modulus" target="_blank" rel="noopener noreferrer" > https://developer.nvidia.com/modulus </a> </li>
</ol>
<p> <b> Contact: </b> <a href="https://www.bu.edu/eng/profile/emma-lejeune/" target="_blank" rel="noopener noreferrer" >E. Lejeune</a> <p>
<p> If you are interested in listing your dataset, please get in touch! Last Updated: April 9, 2023 </p>
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