41 to 50 of 93 Results
Mar 9, 2023 - PN 3-11
Degen, Morris; Santos, José Carlos; Pluhackova, Kristyna; Cebrero, Gonzalo; Ramos, Saray; Jankevicius, Gytis; Hartenian, Ella; Guillerm, Undina; Mari, Stefania A.; Kohl, Bastian; Müller, Daniel J.; Schanda, Paul; Maier, Timm; Perez, Camilo; Sieben, Christian; Broz, Petr; Hiller, Sebastian, 2023, "Supplementary material for 'Structural basis for ninjurin-1 mediated plasma membrane rupture in lytic cell death'", https://doi.org/10.18419/darus-3373, DaRUS, V1
Eukaryotic cells can undergo different forms of programmed cell death, many of which culminate in plasma membrane rupture (PMR) as the defining terminal event. PMR was long thought to be driven by osmotic pressure, until it was recently shown to be in many cases an active process... |
Mar 2, 2023 - PN 1-6
Kelm, Mathis; Bringedal, Carina; Flemisch, Bernd, 2023, "Replication Data for phase-field contributions in level-set comparison study", https://doi.org/10.18419/darus-3359, DaRUS, V1, UNF:6:MsNXqcARspF5yuVdFLKT0Q== [fileUNF]
Primary and post-processed simulation data and visualization tools to reproduce the phase-field results presented in the related publication. The folders "simulation" and "effective_quantities" contain primary simulation results obtained with the code published in the DuMux-pub m... |
Feb 20, 2023 - PN 6
Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1
Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint... |
Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1
This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be... |
Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media
Keim, Leon; Class, Holger; Schirmer, Larissa; Strauch, Bettina; Wendel, Kai; Zimmer, Martin, 2023, "Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column", https://doi.org/10.18419/darus-3276, DaRUS, V1
This dataset contains the DuMux code for the simulations in https://doi.org/10.3390/geosciences13020051 For the detailed list of software used, please have a look at the file install_class2023.sh. To run the simulations at your own computer, please conduct the following steps: In... |
Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media
Keim, Leon; Class, Holger; Schirmer, Larissa; Wendel, Kai; Strauch, Bettina; Zimmer, Martin, 2023, "Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022.", https://doi.org/10.18419/darus-3271, DaRUS, V1
This dataset contains data generated during the measurement campaign inside the karst cave. The CO2 sensors in the cave air will continue to measure (as of Feb. 2023). For details on the site etc. see https://doi.org/10.3390/geosciences13020051 To create the graphs in the Class e... |
Jan 30, 2023 - PN 7-6
Kneifl, Jonas; Rosin, David; Avci, Okan; Röhrle, Oliver; Fehr, Jörg, 2023, "Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations", https://doi.org/10.18419/darus-3302, DaRUS, V1
This dataset provides simulation results from a high-fidelity human upper-arm finite element model under varying muscle activations and an example script to load the data. The upper arm model consists of the bones of the radius and ulna for the forearm and the humerus for the upp... |
Jan 25, 2023 - PN 4-4
Rosenfelder, Mario; Ebel, Henrik; Eberhard, Peter, 2023, "Experiment Videos of the Force-Based Non-Prehensile Cooperative Transportation of Objects with Mobile Robots", https://doi.org/10.18419/darus-3331, DaRUS, V1
The videos provided show two experimental results of cooperative object transportation using lightweight omnidirectional mobile robots. In particular, the mobile robots shall transport two different polygonal, but non-convex, objects along predefined paths. No central decision en... |
Jan 17, 2023 - PN 7-6
Rodegast, Philipp; Maier, Steffen; Kneifl, Jonas; Fehr, Jörg, 2023, "Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios", https://doi.org/10.18419/darus-3301, DaRUS, V1, UNF:6:rnJlYpzgwi2nYAtNG7jBtA== [fileUNF]
This dataset provides time-dependent simulation results from high-fidelity motorcycle body crash scenarios. The set contains the angular as well as linear positions, velocities, and accelerations of different parts of the motorcycle. In addition, force and contact sensor signals... |
Jan 11, 2023 - Materials Design
Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/darus-3239, DaRUS, V1
Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low mom... |
