1 to 10 of 76 Results
Apr 23, 2024 - Simulation input scripts to study Transport of Water In Soft confinemenT (TWIST)
Schlaich, Alexander, 2024, "Replication Data for: Bridging Microscopic Dynamics and Hydraulic Permeability in Mechanically-Deformed Nanoporous Materials", https://doi.org/10.18419/darus-3966, DaRUS, V1
Simulation input scripts used for "Bridging Microscopic Dynamics and Hydraulic Permeability in Mechanically-Deformed Nanoporous Materials" The folders the corresponding simulation and analysis files to setup the simulation systems (SETUP), perform the GCMC/MD simulations (ISOTHER... |
Apr 23, 2024 - CHOLife_TUHH_LSP_tracking
Hofmann, Sebastian; Buntkiel, Lukas; Rautenbach, Ryan; Gaugler, Lena; Ma, Yifan; Haase, Ingrid; Fitschen, Jürgen; Wucherpfennig, Thomas; Reinecke, Sebastian; Hoffmann, Marko; Takors, Ralf; Hampel, Uwe; Schlüter, Michael, 2024, "Data for: Experimental Analysis of Lifelines in a 15,000 L Bioreactor by Means of Lagrangian Sensor Particles", https://doi.org/10.18419/darus-3768, DaRUS, V1
The DaRUS repository entails the raw files, result files and the MATLAB codes for the publication "Experimental Analysis of Lifelines in a 15,000 L Bioreactor by Means of Lagrangian Sensor Particles" in the journal "Chemical Engineering Research and Design". This study employs La... |
Keim, Leon; Class, Holger, 2024, "Replication Data for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/darus-4143, DaRUS, V1
This dataset features both data and code related to the research article titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes raw data packaged in tarball format, including Python scri... |
Apr 11, 2024 - Holm group
Finkbeiner, Jan; Tovey, Samuel; Holm, Christian, 2024, "Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials", https://doi.org/10.18419/darus-4099, DaRUS, V1
Data and scripts for replicating results and the investigation presented in the paper. This includes the dft parameters for generating training data, all training and data selection scripts for the neural networks, scripts for running and analysing the production simulations with... |
Apr 8, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)", https://doi.org/10.18419/darus-3138, DaRUS, V1, UNF:6:NwphGtoYrBQqd2TyRh0OHA== [fileUNF]
This dataset contains stimuli and collected participant data of VisRecall++. The structure of the dataset is described in the README-File. Further, if you are interested in related codes of the publication, you can find a copy of the code repository (see Metadata for Research Sof... |
Mar 25, 2024 - A01: Molecular detail in fluid simulations: Density Functional Theory within component and momentum balances
Bursik, Benjamin; Stierle, Rolf; Schlaich, Alexander; Rehner, Philipp; Gross, Joachim, 2024, "Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling", https://doi.org/10.18419/darus-3769, DaRUS, V1
This data set contains data of three categories: 1) LAMMPS input files (.lammps), postprocessing python script (.py) and density and velocity profiles (.dat) from NEMD. 2) DFT three-dimensional density profiles (.npy) for all systems. 3) Jupyter notebooks (.ipynb) for the calcula... |
Wang, Yao; Bulling, Andreas, 2024, "Saliency3D: A 3D Saliency Dataset Collected on Screen (Dataset and Experiment Application)", https://doi.org/10.18419/darus-4101, DaRUS, V1
While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. O... |
Mar 19, 2024 - Institute for Stochastics and Applications
Höpfl, Sebastian, 2024, "Percentile Intervals in Bayesian Inference are Overconfident", https://doi.org/10.18419/darus-4068, DaRUS, V1
This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material).... |
Müller, Alexander; Vinod Kumar Mitruka, Tarun Kumar Mitruka; Jakob, Henrik, 2024, "Ikarus v0.4", https://doi.org/10.18419/darus-3889, DaRUS, V1
Ikarus is a C++-based library with Python bindings (link to documentation) built to solve partial differential equations with the finite element method. The dataset under this DOI contains the current release (v0.4) of the library. This release not only focuses on refactoring var... |
Mar 11, 2024 - Institute for Theoretical Physics IV
Speck, Thomas; Papanikolaou, Nikos, 2024, "Supplementary material for `Dynamic renormalization of scalar active field theories`", https://doi.org/10.18419/darus-4073, DaRUS, V1
The following folders have Jupyter notebook files that are used to obtain the results we present in Section I.1, I.2, III.B, III.C, IV.A and IV.B and Appendix C and D and Figures 9, 10, 11, 13. To calculate the graphical corrections we extensively use the Python package ``restflo... |
