1 to 10 of 2,475 Results
Sep 10, 2026 - Publication: Ultra High Performance Concrete (UHPC)
Ruf, Matthias; Madadi, Hamid; Steeb, Holger, 2026, "micro-XRCT data sets of ultra high performance concrete cores during different stages of high-frequency fatigue testing with 50 Hz", https://doi.org/10.18419/DARUS-4421, DaRUS, V1
The dataset contains micro X-ray computed tomography (micro-XRCT) data from cylindrical ultra-high-performance concrete (UHPC) cores examined at different stages of high-frequency fatigue damage. Each core had a diameter of 6.35 mm and a length of 16 mm. The cores were extracted from the same UHPC block. However, different cores were imaged at the... |
Sep 10, 2026 - Publication: Ultra High Performance Concrete (UHPC)
Ruf, Matthias; Madadi, Hamid; Steeb, Holger, 2026, "micro-XRCT data set of the final failure of a Berea sandstone core after high-frequency fatigue testing with 50 Hz", https://doi.org/10.18419/DARUS-4420, DaRUS, V1
The dataset contains micro X-ray computed tomography (micro-XRCT) data from a cylindrical Berea sandstone specimen subjected to high-frequency fatigue testing. The specimen had a diameter of 12 mm and a length of 30 mm. The fatigue test associated with the micro-XRCT measurements was performed at a loading frequency of 50 Hz. The compressive streng... |
Sep 10, 2026 - Publication: OpenFOAM-based numerical simulation of flow in porous media
Wang, Mingfeng; Karadimitriou, Nikolaos; Steeb, Holger, 2026, "Data for: The Role of Pore-Scale Geometry in Immiscible Displacement: A Numerical Comparison Between Chemical and Mechanical Erosion", https://doi.org/10.18419/DARUS-6435, DaRUS, V1
1. Introduction Fluid flow through porous media can alter the void space geometry theough chemical erosion, which dissolves and then shrinks solid grains, or mechanical erosion, which remove solid grains. The impacts of these distinct erosion mechanisms on two-phase flow behavior remain poorly understood, despite their broad relevance to subsurface... |
Sep 10, 2026 - Usability and Sustainability of Simulation Software
Neubauer, Felix, 2026, "Replication data for: Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach", https://doi.org/10.18419/DARUS-6158, DaRUS, V3, UNF:6:UrN7pGlBgFwIikRGgxB/eQ== [fileUNF]
This dataset accompanies the paper “Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach” and provides source code snapshots, evaluation inputs and outputs, LLM-based conversion annotations, and derived analyses. It includes the Schema Conversion Orchestrator, a Python/Flask service that discover... |
Sep 9, 2026 - Institute for Reactive Flows (IRST)
Sessler, Christian; Gärtner, Jan Wilhelm; Kronenburg, Andreas, 2026, "Replication Data for: Numerical Investigation of Flash Evaporation of the Combined Injection of Liquid Oxygen and Gaseous Methane/Hydrogen", https://doi.org/10.18419/DARUS-5667, DaRUS, V1
General Information This dataset contains the OpenFOAM case files to generate the published data in: C. Sessler, J. W. Gärtner and A. Kronenburg, "Numerical Investigation of Flash Evaporation of the Combined Injection of Liquid Oxygen and Gaseous Methane/Hydrogen", Multiphase Science and Technology, Volume 37, Issue 4, 2025, pp. 1-19, doi: 10.1615/... |
Sep 9, 2026 - Projects without PN Affiliation
Herkert, Robin, 2026, "Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation", https://doi.org/10.18419/DARUS-6155, DaRUS, V2
This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation" (2026). The data were generated by the provided Python scripts from Hamiltonian test systems and are organized according to the corresp... |
Sep 7, 2026 - Usability and Sustainability of Simulation Software
Chinnikkaramadom Govindan, Giridhar, 2026, "Replication Data for: Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents", https://doi.org/10.18419/DARUS-5964, DaRUS, V1, UNF:6:0+Q1vgk6IYwV/GGvOjFwZg== [fileUNF]
This dataset contains files, scripts, and documentation for running benchmarks for the library shacl-bridge, created as part of my master thesis "Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents". For instructions to run the benchmark, see the README.md of the dataset... |
Sep 5, 2026 - Institute for Reactive Flows (IRST)
Cheng, Ruyue; Shamooni, Ali, 2026, "Replication Data for: Three-dimensional super-resolution reconstruction of turbulent spray flow fields", https://doi.org/10.18419/DARUS-5811, DaRUS, V1
This repository contains the data and python code to build/train/test the super-resolution model (deep neural network). Three zip files are provided: code: contains the code to build/train/test super-resolution models caseSettings: contains the configuration files for the training and testing datasets: contains all the training/validation/test data... |
Sep 4, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning
Egenlauf, Patrick; Kröninger, Hannes A.; Kung, Arnulf; Gaimann, Mario U.; Klopotek, Miriam, 2026, "Replication Data for: Entropy production of active matter systems as indicator for computing performance", https://doi.org/10.18419/DARUS-6343, DaRUS, V1
This dataset contains all relevant data to reproduce the results of the paper titled "Entropy production of active matter systems as indicator for computing performance". The dataset is organized around three main parameter scans: speed-controller force for the driven system, speed-controller force for the undriven system, and driver repulsion forc... |
Sep 4, 2026 - Holm group
Hoßbach, Julian; Tovey, Samuel; Kuppel, Sandro; Ensslen, Tobias; Behrends, Jan; Holm, Christian, 2026, "Replication Script for "Deep Learning-Driven Peptide Classification in Biological Nanopores"", https://doi.org/10.18419/DARUS-5740, DaRUS, V2
This dataset contains all scripts required to replicate the results of our paper "Deep Learning-Driven Peptide Classification in Biological Nanopores". Data can be obtained through the corresponding author. |
