DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Raw data for: The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli
No full textDopamine is a promising organic compound with several key applications in emergency medicine, diagnosis and treatment of cancer, production of lithium anodes, and wastewater treatment. Since studies on in vivo dopamine production are limited, this study demonstrates the development and optimisation of a dopamine production strain by the help of the knowledge driven design-build-test-learn (DBTL) cycle for rational strain engineering. The knowledge driven DBTL cycle, involving upstream in vitro investigation, is an automated workflow that enables both mechanistic understanding and efficient DBTL cycling. Following the in vitro cell lysate studies, the results were translated to the in vivo environment through ribosome binding site (RBS) engineering. As a result, we developed a dopamine production strain capable of producing dopamine at concentrations of 69.03 ± 1.2 mg/L which equals 34.34 ± 0.59 mg/gbiomass. Compared to state-of-the-art in vivo dopamine production, our approach improved performance by 2.6 and 6.6-fold, respectively. The fine-tuning of the dopamine pathway by RBS engineering clearly demonstrated the impact of GC content in the Shine-Dalgarno sequence on the RBS strength. In essence, a highly efficient dopamine production strain was developed by implementing the knowledge driven DBTL cycle
Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix
No full textThis archive for reproducibility contains the datasets used to train the models, a small script to read the datasets, and the finest model.
More information can be found in the README.md
Replication Data for: The collision kernel of nanoparticles in homogeneous isotropic turbulence: Direct simulations and modelling
No full textThis data set contains the software and the simulation setups to reproduce the results shown in the related journal paper. The respective 'README.md' files provide further documentation on the utilised solvers and the performed simulation cases
eSPARQL Implementation
No full textThis project contains the code of an implementation of the eSPARQL
language, which extends SPARQL-star with a `FROM BELIEF` that
allows an easy formulation of epistemic queries.
Further information can be found in the README.md file
Script to calculate turbulence-enhanced Brownian coagulation rate coefficients in nanoparticle suspensions
No full textThis dataset contains a script to calculate collision rate coefficients between nanoparticles under the joint influence of Brownian diffusion and turbulent advection. Coefficients like this are required within population balance models to describe the temporal evolution of particle size distributions.
More information regarding the usage of the provided script can be found in the corresponding README.</p
Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples
No full textThis dataset contains the data and code for the publication: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples.
Code Repository
A dynamic version of the code repository can be found at https://github.com/rbnbr/VoroParaSense.
The version presented in this dataset corresponds to the version used in the corresponding paper (referenced as v1.0.0) and does not contain post-publication changes to the repository.
Figures
Some of the figures contained in the Jupyter Notebooks were used to generate the CC BY 4.0 licensed figures in https://doi.org/10.1111/cgf.70122. The figures here are the raw .png figures. The published figures are mostly vector graphics and were slightly modified compared to the figures rendered in the Jupyter notebooks.
Author: Ruben Bauer
Source: https://doi.org/10.1111/cgf.70122
License: CC BY 4.0
The figures of the main paper are:
examples\notebooks\plot_examples\two_d_clipping_and_distribution.ipynb used to generate Figure 2
examples\notebooks\plot_examples\three_dimensional_case.ipynb used to generate Figure 3
examples\notebooks\plot_examples\two_d_clipping_and_distribution.ipynb used to generate Figure 4
examples\notebooks\plot_examples\two_d_major_transition_directions.ipynb used to generate Figure 5
examples\notebooks\single_dataset_convenience_plots\iris_conv_plot.ipynb used to generate Figure 6, 7, 8, 9, 10, 12
examples\notebooks\plot_examples\space_dividing_line.ipynb used to generate Figure 11
examples\notebooks\single_dataset_convenience_plots\semiconductor_conv_plot.ipynb used to generate Figure 14
examples\notebooks\single_dataset_convenience_plots\droplet_impact_conv_plot.ipynb used to generate Figure 15, 16
For the supplemental material:
examples\notebooks\plot_examples\normal_vectors_angle_vis.ipynb used to generate Figure 1
examples\notebooks\plot_examples\plane_plane_distance.ipynb used to generate Figure 2
examples\notebooks\plot_examples\runtime_experiments_2.ipynb used to generate Figure 3, 4
examples\notebooks\plot_examples\bandwidth_experiments.ipynb used to generate Figure 5 - 15
Install
Tested with Python 3.12.3.
Setup virtual environment with Python: python -m venv .venv
Activate the environment, then install the requirements via: pip install -r requirements.txt
Run python ./main.py to run the main example.
</ol
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
No full textThe RobustSpring dataset contains the image corruption data files for scene flow, optical flow and stereo estimation with the Spring dataset. Note that this repository contains only the Spring test data files. For easier handling, we organized them into sub-directories by image corruption type:
brightness.zip : brightness image corruption
contrast.zip : contrast image corruption
defocus_blur.zip : defocus_blur image corruption
elastic_transform.zip : elastic_transform image corruption
fog.zip : fog image corruption
frost.zip : frost image corruption
gaussian_blur.zip : gaussian_blur image corruption
gaussian_noise.zip : gaussian_noise image corruption
glass_blur.zip : glass_blur image corruption
impulse_noise.zip : impulse_noise image corruption
jpeg_compression.zip : jpeg_compression image corruption
motion_blur.zip : motion_blur image corruption
pixelate.zip : pixelate image corruption
rain.zip : rain image corruption
saturate.zip : saturate image corruption
shot_noise.zip : shot_noise image corruption
snow.zip : snow image corruption
spatter.zip : spatter image corruption
speckle_noise.zip : speckle_noise image corruption
zoom_blur.zip : zoom_blur image corruption
Each image corruption folder is internally organized as follows:
test : Indicates that this is the test proportion of the Spring dataset
0003: Scene subfolder for scene 0003, 131 frames
frame_left: Left frames
frame_left_0001.png: Frame 0001
...
frame_left_0131.png: Frame 0131
frame_right: Right frames
frame_right_0001.png: Frame 0001
...
frame_right_0131.png: Frame 0131
0019: Scene 0019, same internal structure as above, 111 frames.
0028: Scene 0028, same internal structure as above, 39 frames.
0028: Scene 0029, same internal structure as above, 135 frames.
0031: Scene 0031, same internal structure as above, 73 frames.
0034: Scene 0034, same internal structure as above, 47 frames.
0035: Scene 0035, same internal structure as above, 120 frames.
0040: Scene 0040, same internal structure as above, 111 frames.
0042: Scene 0042, same internal structure as above, 116 frames.
0046: Scene 0046, same internal structure as above, 117 frames.
File Formats:
All images are given as .png files
For the project website see spring-benchmark.org
Supplementary Data to: Modelling Interfacial Dynamics Using Hydrodynamic Density Functional Theory: Dynamic Contact Angles and the Role of Local Viscosity
No full textSeveral files are included here:
Jupyter notebooks which can be used
a) to visualize density profiles of equilibrium and dynamic droplet simulations (DFT, HDFT, MD, NEMD), and to determine the respective contact angle
b) to determine and plot the center of mass position of the droplet, to calculate the average or steady-state velocity of the droplet and to visualize two dimensional velocity profiles
c)* to determine the initial information required for a hydrodynamic DFT simulation; this comprises the external potential, the weighted density of wall atoms (for entropy scaling) and the initial density profile (i.e. an equilibrated droplet) from DFT.
The enviroment.yml file, which can be used with anaconda to setup the python enviroment (conda env create -f environment.yml)
A folder containing data for density profiles, velocity profiles, center-of-mass positions and velocities over time. These files are loaded from the jupyter notebooks.
</ol
Experimental data: Flexural buckling of beech LVL columns
No full textThis repository contains the experimental results of 27 flexural buckling tests on timber columns made of beech laminated veneer lumber (LVL) GL75. The loading, the deformations at midheight, the rotations at the supports, the dimensions, and the weight were recorded. The modulus of elasticity in grain direction was determined in preliminary tests
Data repository for "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting"
No full textThis data repository contains the experiment data and code for the paper "Accelerating Spiking Neural Networks on CPUs via Cache-aware Splitting".
The code.zip file contains the git repositories of the code that was used in the experiments:
fast-arrays library: executes vectorized calculations with AVX-512F operations
mlflow-rs library: client for the MLflow experiment tracking tool to store metrics and artifacts of the experiments
snn-rs project: SNN implementation as well as experiment execution code
The experiment_tracking.zip file contains the experiment results:
hyperparameters
the exact code that was executed (git commit hash + git patch)
collected metrics
trained SNN models
class assignments
training log
</p