31 to 40 of 2,475 Results
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... |
Aug 11, 2026 - PN 6A-3
Schäfer, Moritz; Kästner, Johannes, 2026, "Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-6365, DaRUS, V1
This repository contains the data for our paper on "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches, an... |
Aug 11, 2026 - PN 3-11
Pluhackova, Kristyna, 2026, "Supplementary Data to: "Disulfiram inhibits pyroptosis and apoptosis through multiple complementary mechanisms"", https://doi.org/10.18419/DARUS-5538, DaRUS, V1
All-atom molecular dynamics simulations of disulfiram binding to caspase-1 as well as interactions of disulfiram (DSF), diethyldithiocarbamate (DTC), necrosulfonamide (NSA), and dimethylfumarate (DMF) with the E. Coli PLE membrane. |
Aug 7, 2026 - 3rd Physics Institute
Ho, Cheng-I, 2026, "Replication Data for: Revealing and reducing growth-induced interfacial disorder in preferentially aligned nitrogen-vacancy centers in diamond", https://doi.org/10.18419/DARUS-6396, DaRUS, V1, UNF:6:c0MMoYODW2nByhD0mHqTlw== [fileUNF]
CSV data sets to reproduce all plots in the main paper. The dataset consists of spectra and summary of statistics of decay times/rates of different regions/layers of sample S1 and S2 used in the paper. Information on how the data was acquired and used can be found in the related manuscript. |
