DaRUS (University of Stuttgart)
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Source code for: UMAT subroutine for material modelling of timber with Abaqus
No full textThis repository contains a UMAT subroutine for modelling orthotropic behaviour with brittle failure for tension in the x-direction, ellipsoid elastoplastic behaviour for compression in grain direction (x-direction), and bilinear elastoplastic behaviour for shear in the xy-, xz-, and yz-planes. A stress interaction is only considered for the three shear components. The UMAT subroutine was developed in the dissertation of Janusch Töpler. For more information see Annex D of the dissertation
Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production
No full textRaw experimental data for the research article:
"Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production"
The methodology and materials used to generate this data are described in the associated research article. The here presented data follow the structure of that article and correspond to the figures and tables of the research article.
In brief, the data were obtained by measuring concentrations of 2,4-pyridincedicarboxylic acid (PDCA) and protocatechuic acid (PCA) by high performance liquid chromatography (HPLC) in samples of cultivation supernatant. The cultivations were performed with cultures of Pseudomonas putida ligAB or R. jostii dpcaHG to convert monoaromatics or lignin containing substrates into PDCA
Sensitivity analyses and parameter fitting of biphasic articular cartilage model
No full textThis dataset contains a zip of the used program FEBio as well as all files and produced results for the investigations presented in Egli et al.. Therein, we combine experiments with sensitivity analyses (SAs) and simulations of a biphasic model of three boundary value problems (BVPs): uniaxial tension (UT), confined compression (CC) and biaxial tension (BT).
The folders UT, CC and BT contain everything concerning the respective BVPs. I.e. all used python scripts, generated results and a readme.txt on where to find what and how to run it.
The zip files SA_UT and SA_CC contain everything concerning the SAs on UT and CC. The corresponding instructions on how to run the SAs can be found in readme_SA_CC.txt and readme_SA_UT.txt.
readme_general.txt gives a short overview of what in this dataset contains what
Replication Data for: "Plausibility assessment of passive and active human body models in numerical pedestrian to vehicle collision simulations based on real-life accident data and development of an injury assessment metric"
No full textThis dataset contains all necessary files and descriptions to extend the finite element passive THUMSv4 pedestrian human body model into a muscle-driven active human body model. It also contains all the necessary tools to reposition the standard THUMSv4 pedestrian model into four different pedestrian positions as described in the publication "Plausibility assessment of passive and active human body models in numerical pedestrian to vehicle collision simulations based on real-life accident data and development of an injury assessment metric" by Trube et al. (2025
Supplemental data for "Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees"
No full textThis repository contains supplemental data for the article
"Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees",
accepted for publication in GAMM-Mitteilungen by Sanath Keshav, Julius Herb, and Felix Fritzen [1]. The data contained in this DaRUS repository acts as an extension to the GitHub repository for the so-called Voigt-Reuss net. The data in this dataset is generated by solving thermal homogenization problems for an abundance of different microstructures. The microstructures are defined by periodic representative volume elements (RVE) and periodic boundary conditions are applied to the temperature fluctuations.
We consider bi-phasic two-dimensional microstructures with a resolution of 400 × 400 pixels, as published in [2], and three-dimensional microstructures with a resolution of 192 × 192 × 192 voxels, as published in [3]. For both microstructure datasets, we provide the effective thermal conductivity tensor that is obtained by solving homogenization problems on the full microstructure for different material parameters in the two phases. For the simulation, we used our implementation of Fourier-Accelerated Nodal Solvers (FANS, [4]) that is based on a Finite Element Method (FEM) discretization. Further details are provided in the README.md file of this dataset, in our manuscript [1], and in the GitHub repository.
[1] Keshav, S., Herb, J., and Fritzen, F. (2025). Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees, GAMM‐Mitteilungen. (2025), e70005.
https://doi.org/10.1002/gamm.70005
[2] Lißner, J. (2020). 2d microstructure data (Version V2) [dataset]. DaRUS.
https://doi.org/doi:10.18419/DARUS-1151
[3] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774
[4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392.
https://doi.org/10.1007/s00466-017-1501-5 <br
Data for "Topological order in symmetric blockade structures"
No full textNumerical data to reproduce Figure 4 and Figure 5 from the paper
Replication Data for: Ultraviolet Photodetectors and their Readout Realization for Future Active-Matrix Sensing
No full textThis dataset holds all measurement data, as well as the evaluation and plotting scripts to replicate all figures of the paper.
The data is structured in folders of the figures. In the most cases a folder includes the figure (.pdf), a subfolder named Plot and a subfolder named Raw data and evaluation script. The Plot folder contains the plotting script (.py) and the plotted data (.txt). The Raw data and evaluation script folder contains the recorded (unprocessed) data (.csv, .xlxs) and a python script (.py), which generates the plotted data from the raw data
Replication Data for: Towards a Better Understanding of Graph Perception in Immersive Environments
No full textAs Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments.
However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance.
To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment.
Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation.
The files of this dataset are documented in README.md
Supplemental data for "A collapsed interface approach to resolve grain boundaries in finite element simulations of polycrystalline diffusion"
No full textThis repository contains supplemental data for the article "A collapsed interface approach to resolve grain boundaries in finite element simulations of polycrystalline diffusion" (linked to this dataset; published in Computational Materials Science 260 (2025), article 114172).
Further details are provided in the README.md file of this dataset, in our manuscript, and in the GitHub repository containing an interactive data viewer for extraction and visualization of the data contained in this dataset
Electronic Supplementary Material for "Parametrized, Transferable Classical Density Functional Theory for Alkane/Alkene Separation in Cationic Zeolites"
No full textElectronic supplementary materials for the publication stated below. Adsorption isotherms are calculated by GCMC simulations (via RASPA software) and by classical density functional theory, cDFT (python code). For both methods, adsorption isotherms were calculated for FAU and LTA zeolites with either sodium or calcium cations. Vapor-Liquid Phase Equilibria from GEMC simulations were performed for force field validation (via RASPA software). The classical DFT code can be used for adsorption isotherm calculation and also for further ksi parametrization. The input files for RASPA simulations are also included for reproduction.
Contents:
- Python codes for classical density functional theory calculations (ZIP)
- RASPA 2.0.47 input files, zeolite crystallographic files (CIF)
- GCMC adsorption data (CSV)
- classical DFT adsorption data (CSV)
- GEMC VLE data (CSV