DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking
No full textReplication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking. 4K Images from 3 Camera Perspectives including Annotation data for Reproduction of the Learning for the ML Model. It includes the tensorboard logfile representing the quantitative loss during training, the qualitative spline prediction during training and the final trained model weights.
Acknowledgements:
The authors would like to thank the Ministry of Science, Research and Arts
of the Federal State of Baden-Württemberg for the financial support of the
projects within the InnovationsCampus Future Mobility (ICM)
Datasets: First Stage 3D Simulation - Raw, 100 and 1000 Data Points
No full textThese data sets serve as training data for modelling the temperature field emanating from a groundwater heat pump. They are simulated with Pflotran and saved in h5 format. They contain 100 and 1000 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 320 m x 1280 m x 320 m with 64 x 256 x 64 cells. The varying parameters of the data sets are pressure and permeability. Both are constant within a data point, but vary across the data sets. Other parameters that define the data sets, such as porosity, are chosen to be as close as possible to reality.
Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.
Generated with scripts from
Dataset generation with Pflotran with arguments given in inputs/args.yaml
Replication Data for: Using Support Effects to Increase the Productivity of Immobilized Ruthenium Hydride Catalysts for the Hydrogenation of CO2
No full textReplication data for the paper: Using Support Effects to Increase the Productivity of Immobilized Ruthenium Hydride Catalysts for the Hydrogenation of CO2. Contains NMR and IR data for both the catalyst characterization (before and after catalysis) as well as the reaction NMR spectra and a table with the catalytic data summarized
ANDroMeDA_UAV01_WINSENT_Test_Site
No full textData collected with the ANDroMeDA-UAV.
The measurement system for the wind speed consists of hot wires paired with a pitot tube to obtain a 3d wind vector. The data is consists of wind speeds (total, u, v, w, angle), GPS location, Height (Ground, AMSL) and the location in the local coordinate system. The local coordinate system is referenced to the NW metmast at the test sit (48.665159,9.834769
Onco* version 0.1.0
No full textOnco* version 0.1.0
Onco* is a module based umbrella software project for numerical simulations of patient-specific cancer diseases, see following figure. From given input states of medical images the disease is modelled and its evolution is simulated giving possible predictions. In this way, a digital cancer patient is created, which could be used as a basis for further research, as a decision-making tool for doctors in diagnosis and treatment and as an additional illustrative demonstrator for enabling patients understand their individual disease. All parts resolve to an open-access framework, that is ment to be an accelerator for the digital cancer patient. Each module can be installed and run independently. The current state of development comprises the following modules:
OncoFEM
OncoGEN
OncoTUM
OncoSTR
Content
The uploaded virtual box is a virtual machine of a linux mint 21.2 cinnamon, 64 bit system and 8 GB RAM. The machine contains the pre-installed version of OncoFEM version 1.0, that corresponds to its related publication. The virtual machine need to be imported in Oracle VM VirtualBox.
Username: onco
password: 0000
The pre-installed version of onco* is implemented in a conda environment that is activated with the terminal command
python3 oncofem_tut_01_quick_start
Of course, the tutorials will only run, if the system meets the necessary requirements. The tutorials (1, 2, 3, 4, 5, 6) where performed on a local machine (intel cpu i7-9700k with 3.6 GHz, 128 GB RAM). The tumor segmentation (tutorial 7, 8) have been tested on a different machine with a gpu (Nvidia a40, 48 GB VRAM, 32 core AMD epyc type 7452). For testing, the discretisation in space is decreased, compared to the results published in the respective paper.
This is the software state at version 0.1.0 and the actual development can be found in the respective github.
OncoFEM
OncoGEN
OncoTUM
OncoSTR
</ul
Data for: "Influence of beam parameters on the capillary formation in partial and full penetration in high speed laser beam welding of metals"
No full textIn this experiment copper and aluminum were partially and fully penetrated at high speed with a laser beam. The structure as well as the depth of the capillary were filmed with an X-Ray machine. The video footage were used to reconstruct and later ray traced to analyse the thermal efficienc
Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data
No full textDescription:
Dataset published with the paper "Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison". You can use these data to generate the comparisons between the EnKF and the MCMC solution as seen in the paper.
Folder structure & Nomenclature:
Each folder with reference data starts with "ref_", followed by the scenario identifier like for S0, "ref_S0". See the scenario description below for further explanation.
In each of the reference folders, the several MCMC chains are stored. Please refer to the publication for further information about the file.
Each folder with solutions that are not references, but just replications, start with "rep_", like for the S0 steady state EnKF, "rep_EnKF_S01".
In these folders, the datafiles including the solutions are stored.
For your own references, we recommend creating your own folder called "rep_YourFolderName_ScenarioSpecifier".
Scenario description:
S0 is the base case. It features a relatively strong degree of heterogeneity with σθ = 2, relatively accurate measurement data with σe = 0.05 [L], irregularly placed observations, and steady-state groundwater flow.
S1 features the regular grid of observations instead of the random one. While irregular monitoring 375 networks are more realistic, the very close spacing of a few monitoring wells may pose a problem to some methods due to their high autocorrelation. Therefore, S1 is a fallback scenario.
S2 is again like S0, but reduces the strength of heterogeneity from σθ = 2 to σθ = 1. While σθ = 2 is a more realistic degree of heterogeneity, it may already be challenging for methods that are explicitly or implicitly linearization-based. Therefore, S2 is a fallback scenario.
S3 is again like S0, but increases the assumed level of observational errors from σe = 0.05 [L] to σe =0.1 [L]. Given the overall head difference of 20 [L] across the domain by the boundary conditions, these values can be classified as high and medium accuracy, respectively. Iterative or sampling-based methods may have problems with the accuracy requirement posed by the large accuracy in S0. Once again, S3 is a fallback solution. However, as posterior uncertainties will remain larger for smaller measurement accuracy, S3 may also trigger stronger non-linearities across the larger remaining postcalibration uncertainty ranges.
S4 changes S0 to feature transient (instead of steady-state) groundwater flow. This is relevant forEnKF-type methods that work via transient data assimilation and that do not iterate.
Download and benchmarking:
Github Package</a
Dataset for "Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory"
No full textSocial media platforms use short, highly engaging videos to catch users’ attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment ( = 60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (i.e., executing a previously planned action). The study required participants to remember intentions over interruptions. We found that the TikTok condition significantly degraded the users’ performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution. We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers not to harm the users’ memory and wellbeing.
Description of the Dataset
Data frame:
The ./data/rt.csv provides the data frame of reaction times.
The ./data/acc.csv provides the data frame of reaction accuracy scores.
The ./data/q.csv provides the data frame collected from questionnaires.
The ./data/ddm.csv is the learned DDM features using ./appendix2_ddm_fitting.ipynb, which is then used in ./3.ddm_anova.ipynb.
Figures:
All figures appeared in the paper are placed in ./figures and can be reproduced using *_vis.ipynb files.</p
Data for "Absorbing State Phase Transition with Clifford Circuits"
No full textThis dataset includes simulation data and jupyter notebooks used to reproduce the results in "Absorbing State Phase Transition with Clifford Circuits". The different folders correspond to figures of this paper. The notebooks are run with python 3 and include all functions used for the scaling analysis and plotting
SiSPAT-Isotope code for modelling stable water isotopologue transport within soils
No full textSiSPAT-Isotope source code to reproduce the results presented in J. Schneider, S. Kiemle, K. Heck, Y. Rothfuss, I. Braud, R. Helmig, J. Vanderborght (2024) Analysis of experimental and simulation data of evaporation-driven isotopic fractionation in unsaturated porous media. Vadose Zone Journal, e20363.
SiSPAT is a one-dimensional numerical model of water and energy fluxes in the soil-plant-atmosphere continuum. The extension SiSPAT-Isotope allows to sequentially solve the transport of stable water isotopologues and their fractionation behaviour in soils. The source code is stored in the files Sispat_isotope_it.f and Subrsispat_isotope_it.f.
With gfortran Sispat_isotope_it.f the program is executed. The file nomfich.dat contains all input and output files used in the simulation and is read into Sispat_isotope_it.f.</p