Microstructure feature engineering data

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Lißner, Julian, 2023, "Microstructure feature engineering data", https://doi.org/10.18419/darus-3366, DaRUS, V1

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Description	The dataset contains image data of periodic microstructural representative volume elements (RVE), as well as the effective heat conductivity for multiple phase contrasts. Various features and feature descriptors (explained in the related publication) are provided, as well as the computation thereof. The features were used in a machine learning regression setting (see related publication). The data is split into two sets, one with 30.000 samples containing only one inclusion type per RVE and another set of 1.500 samples containing mixed inclusions. The repository comes with a data loader suited for flexible indexing. (2023-02-27)
Subject	Computer and Information Science; Engineering
Keyword	Microstructure, Homogenization, Representative Volume Element (RVE)
Related Publication	Lißner, Julian and Fritzen, Felix. (2023). Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks. arXiv: 2302.12545
License/Data Use Agreement	Custom Dataset Terms

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	data_access_example.py Python Source Code - 2.1 KB Published Mar 6, 2023 13 Downloads MD5: 59a5a37eb3aa95980483fca18fbb4604 exemplary script to access the data Code	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	feature_engineering_data.h5 Hierarchical Data Format - 199.9 MB Published Mar 6, 2023 27 Downloads MD5: 2526748b927fb0775b4cc1fa55945502 storage of the data Data	Access File File Access Public Download Options Hierarchical Data Format Download Metadata Data File Citation EndNote XML RIS BibTeX
	data_loader.py scripts/Python Source Code - 14.6 KB Published Mar 6, 2023 18 Downloads MD5: 4643b4eac73889b050fb1059b7c5c6dd loader object to use for easy data access CodeModule	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	feature_extraction.py scripts/Python Source Code - 17.7 KB Published Mar 6, 2023 15 Downloads MD5: f0adb59b30d0c7c87aa776d2716f96f4 scripts to compute all features CodeModule	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	general_functions.py scripts/Python Source Code - 4.1 KB Published Mar 6, 2023 13 Downloads MD5: ca6bfd9fae4c7d33b52bc70bc5b4a2bd auxiliary (small) functions CodeModule	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.18419/darus-3366
Publication Date	2023-03-06
Title	Microstructure feature engineering data
Author	Lißner, Julian (Universität Stuttgart) - ORCID: 0000-0002-2286-5211
Point of Contact	Use email button above to contact. Fritzen, Felix (Universität Stuttgart)
Description	The dataset contains image data of periodic microstructural representative volume elements (RVE), as well as the effective heat conductivity for multiple phase contrasts. Various features and feature descriptors (explained in the related publication) are provided, as well as the computation thereof. The features were used in a machine learning regression setting (see related publication). The data is split into two sets, one with 30.000 samples containing only one inclusion type per RVE and another set of 1.500 samples containing mixed inclusions. The repository comes with a data loader suited for flexible indexing. (2023-02-27)
Subject	Computer and Information Science; Engineering
Keyword	Microstructure https://www.wikidata.org/wiki/Q1498213 (Wikidata) Homogenization https://purl.org/tema/001509 (TEMA) Representative Volume Element (RVE)
Related Publication	Lißner, Julian and Fritzen, Felix. (2023). Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks. arXiv: 2302.12545 https://arxiv.org/abs/2302.12545
Funding Information	DFG: EXC 2075 - 390740016 DFG: FR2702/8 - 406068690 DFG: FR2702/10 - 517847245
Depositor	Lißner, Julian
Deposit Date	2023-02-27

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Python Version: 3.11 (https://www.python.org/) SD-style (Van Rossum, Guido and Drake, Fred L. (2009). Python 3 Reference Manual. CreateSpace. ISBN 1-4414-1269-7)
h5py Version: 3.8 (https://docs.h5py.org/en/stable/) (Andrew Collette, Thomas Kluyver, Thomas A Caswell, James Tocknell, Jerome Kieffer, Aleksandar Jelenak, Anthony Scopatz, Darren Dale, Chen, Thomas VINCENT, Matt Einhorn, payno, juliagarriga, Pierlauro Sciarelli, Valentin Valls, Satrajit Ghosh, Ulrik Kofoed Pedersen, jakirkham, Martin Raspaud, … Mark Kittisopikul. (2022). h5py/h5py: 3.7.0 (3.7.0). Zenodo. doi: 10.5281/zenodo.6575970)
NumPy Version: 1.24.2 (https://numpy.org/) (Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor and Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke and Travis E. Oliphant. (2020). Array programming with NumPy. Nature 585, 357-362. doi: 10.1038/s41586-020-2649-2)
scipy Version: 1.10.1 (https://scipy.org/) (Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt & SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261-272. doi: 10.1038/s41592-019-0686-2)
Matplotlib Version: 3.7.0 (https://matplotlib.org) (Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering 9, 3, 90-95. doi: 10.1109/MCSE.2007.55)

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Software Version	1.0
Development Status	Active
Programming Language	Python 3.11

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Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

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Terms of Use Any python code in this repository follows the BSD 3-Clause. The remainder of the data/datasets are licensed via CC-BY 4.0.

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