31 to 40 of 3,466 Results
Pelzer, Julia; Böttcher, Fabian, 2026, "Step 3: Scaled-up Domain and Interactions of Heat Plumes", https://doi.org/10.18419/DARUS-5808, DaRUS, V3
This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt... |
Aug 17, 2026 - PN 3-11
Pluhackova, Kristyna; Pfaendner, Christian; Unger, Benjamin; Paul, Thilo, 2026, "Supplementary Material for "Which Metrics Best Capture Protein Structural Change in Molecular Dynamics Simulations? Evaluating Score Combinations and Force Field Effects"", https://doi.org/10.18419/DARUS-5853, DaRUS, V1
Simulation parameter files, protein pdb files for crystal structures and final simulation snapshots, Python scripts for FlexE and SphereGrinder, and Jupyter Notebooks to recreate our analyses and publication figures of the paper "Which Metrics Best Capture Protein Structural Change in Molecular Dynamics Simulations? Evaluating Score Combinations an... |
Aug 17, 2026 - Institute of Biochemistry
Koeppl, Lars-Hendrik, 2026, "Raw Data related to Koeppl et al.: S-Adenosyl-L-homocysteine Hydrolase Side Reactivity Enables Biocatalytic Access to 4',5'-Dehydro Nucleosides as Nucleoside Drug Precursors", https://doi.org/10.18419/DARUS-6397, DaRUS, V1, UNF:6:XTp+6wfxm5cV3Bj2WDXNVw== [fileUNF]
Raw HPLC, LC-MS/MS and NMR data underlying the figures of the associated publication on the side reactivity of S-adenosyl-L-homocysteine hydrolases (SAHHs) and S-inosyl-L-homocysteine hydrolases (SIHHs). File names indicate the figure the data belong to. Raw_Data_Koeppl.xlsx HPLC chromatograms and mass spectra
csv_files/ the individua... |
Pelzer, Julia; Böttcher, Fabian, 2026, "Step 1: Single Heat Plume", https://doi.org/10.18419/DARUS-5806, DaRUS, V4
This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt... |
Aug 14, 2026 - Visualisierungsinstitut der Universität Stuttgart
Tarner, Hagen; Gralka, Patrick; Reina, Guido; Beck, Fabian; Frey, Steffen, 2026, "Supplemental Material for "Visually Enriching and Comparing Runtime Performance of Visualization Pipelines"", https://doi.org/10.18419/DARUS-4115, DaRUS, V1
Supplemental Material for "Visually enriching and comparing runtime performance of visualization pipelines". The dataset contains MegaMol projects (i.e. specifications for visualization pipelines), timestamps for their respective execution at the pipeline level (as described in the paper), and screenshots for any change to the pipeline parameters.... |
Aug 14, 2026 - Institute of Flight Mechanics and Controls
Cunis, Torbjørn; Olucak, Jan, 2024, "Implementation Details and Source Code for CaΣoS: A Nonlinear Sum-of-Squares Optimization Suite", https://doi.org/10.18419/DARUS-4499, DaRUS, V2
This dataset contains detailed information, source code, and benchmark tests for the paper "CaΣoS: A nonlinear sum-of-squares optimization suite," published in the proceedings of the 2025 American Control Conference. Please refer to the "supplementary.pdf" for more information. |
Aug 13, 2026 - Stochastic Simulation and Safety Research for Hydrosystems (LS3)
Wildt, Nils, 2026, "Replication Data for: CODE: A global approach to ODE dynamics learning", https://doi.org/10.18419/DARUS-5826, DaRUS, V1
Pre-computed experimental results for the paper "CODE: A global approach to ODE dynamics learning" (Wildt, Tartakovsky, Oladyshkin, Nowak, JMLMC 2025). The dataset contains trained model results (JLD2 files) from three data-driven ODE learning approaches — arbitrary polynomial chaos expansion (aPCE/chaos), neural networks (Lux), and Gaussian proces... |
Aug 13, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling
Pelzer, Julia; Böttcher, Fabian, 2026, "All Steps: Raw Simulation Data", https://doi.org/10.18419/DARUS-5920, DaRUS, V3
This dataset contains unprocessed simulation data of modeling heat flow from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapted to fit... |
Aug 12, 2026 - PN 6A-3
Schäfer, Moritz; Kellner, Matthias; Kästner, Johannes; Ceriotti, Michele, 2026, "Replication Data for: How to Train a Shallow Ensemble", https://doi.org/10.18419/DARUS-6366, DaRUS, V1
This repository accompanies our paper on "How to Train a Shallow Ensemble". It includes workflows and input files needed to reproduce the results. Most of the experiments are contained in the directory "0_convergence_nll_training". There is a separate directory for the experiments performed for the first revision of the publication and for the repe... |
Aug 11, 2026 - EXC IntCDC Associated Project 58 'NeuralWood'
Akbar, Zuardin; Gambarelli, Serena; Wortmann, Thomas, 2026, "NeuralWood Spruce Boards: Raw Dataset", https://doi.org/10.18419/DARUS-5683, DaRUS, V1
Overview This dataset is part of the project Neuralwood: Neural Networks for the Prediction and Utilization of Natural Material Variations in Timber Construction by the Department for Computing in Architecture, Institute for Computational Design and Construction (ICD/CA) and Materials Testing Institute (MPA), University of Stuttgart. The dataset co... |
