1 to 8 of 8 Results
Apr 29, 2021 - Molecular Simulation
Kobayashi, Takeshi; Kraus, Hamzeh; Hansen, Niels; Fyta, Maria, 2021, "Supplementary material for 'Confined Ru‐catalysts in a Two‐phase Heptane/Ionic Liquid Solution: Modeling Aspects'", https://doi.org/10.18419/darus-1138, DaRUS, V1
This dataset contains simulation input files in GROMACS format accompanying the mentioned publication. Structure, topology, and simulation parameter-files are provided for the simulations discussed in the paper. The data of the final NVT production simulation run are stored for t... |
Kraus, Hamzeh, 2021, "Supplementary material for 'PoreMS: A software tool for generating silica pore models with user-defined surface functionalisation and pore dimensions'", https://doi.org/10.18419/darus-1170, DaRUS, V1
This dataset contains Jupiter Notebooks and the necessary structure files needed for generating the pore systems discussed in the related publication. Additionally, high-resolution figures discussed in the paper are added. |
Munz, Tanja; Schäfer, Noel; Blascheck, Tanja; Kurzhals, Kuno; Zhang, Eugene; Weiskopf, Daniel, 2020, "Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games", https://doi.org/10.18419/darus-1130, DaRUS, V1
Supplemental material for "Comparative Visual Gaze Analysis for Virtual Board Games". Contains the source code, an executable file, an example data set, a video for demonstration of the analysis system and additional information about Go. The analysis system supports eye tracking... |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1
This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods. |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1
This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne... |
Jun 1, 2020 - Glycoside Hydrolase 19 Engineering Database
Orlando, Marco, 2020, "Profile hidden Markov models of the Glycoside Hydrolase 19 Engineering Database", https://doi.org/10.18419/darus-803, DaRUS, V1
A starting alignment was built if other sequences with a known PDB structure were available, by performing a GH19 domain structure-based alignment generated through the mmaker command implemented in ChimeraX. Other seed sequences in the same superfamily were added to this fixed s... |
Jun 1, 2020 - Glycoside Hydrolase 19 Engineering Database
Orlando, Marco, 2020, "Seed sequences for the Glycoside Hydrolase 19 Engineering Database", https://doi.org/10.18419/darus-804, DaRUS, V1
Query sequences for the individual BLAST searches used to initialize the Glycoside Hydrolase 19 Engineering Database (GH19ED, https://gh19ed.biocatnet.de/). |
Jun 1, 2020 - Glycoside Hydrolase 19 Engineering Database
Orlando, Marco, 2020, "GraphML files for protein sequence networks of glycoside hydrolase 19 homologues", https://doi.org/10.18419/darus-802, DaRUS, V1
GraphML files for undirected weighted graphs with nodes that represent protein sequences of glycoside hydrolase 19 homologues. Protein sequences were clustered by a threshold of 90% sequence identity to derive representative sequences. Pairwise sequence identity between two seque... |
