171 to 180 of 201 Results
Mar 8, 2023 - Collaborative Artificial Intelligence
Bulling, Andreas, 2023, "Labeled pupils in the wild (LPW)", https://doi.org/10.18419/DARUS-3237, DaRUS, V1
We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover p... |
Mar 6, 2023 - Data Analytics in Engineering
Lißner, Julian, 2023, "Microstructure feature engineering data", https://doi.org/10.18419/DARUS-3366, DaRUS, V1
The dataset contains image data of periodic microstructural representative volume elements (RVE), as well as the effective heat conductivity for multiple phase contrasts. Various features and feature descriptors (explained in the related publication) are provided, as well as the computation thereof. The features were used in a machine learning regr... |
Mar 3, 2023 - EXC IntCDC Research Project 20 'Knowledge Representation for Multi-Disciplinary Co-Design'
Elshani, Diellza; Lombardi, Alessio; Hernández, Daniel; Staab, Steffen; Fisher, Al; Wortmann, Thomas, 2023, "BHoM to bhOWL converter", https://doi.org/10.18419/DARUS-3364, DaRUS, V1
The dataset is the release version v2.0.0 of the BHoM to bhOWL converter, which helps convert BHoM data to a knowledge graph in any software BHoM supports. BHoM (The Buildings and Habitats object Model) is collaborative framework that runs within several AEC design software, which helps to represent data in a object oriented database model. OWL (We... |
Reina, Guido, 2023, "Supplemental Material for "Can Image Data Facilitate Reproducibility of Graphics and Visualizations? Towards a Trusted Scientific Practice"", https://doi.org/10.18419/DARUS-3345, DaRUS, V1
This data set contains the visualization pipeline states and resulting images for the example scenarios as listed in the manuscript. The auxiliary python scripts are contained for reference, but can be obtained from the MegaMol source code as well (https://github.com/UniStuttgart-VISUS/megamol). One folder per dataset / visualization software combi... |
Mar 2, 2023 - PN 1-6
Kelm, Mathis; Bringedal, Carina; Flemisch, Bernd, 2023, "Replication Data for phase-field contributions in level-set comparison study", https://doi.org/10.18419/DARUS-3359, DaRUS, V1, UNF:6:MsNXqcARspF5yuVdFLKT0Q== [fileUNF]
Primary and post-processed simulation data and visualization tools to reproduce the phase-field results presented in the related publication. The folders "simulation" and "effective_quantities" contain primary simulation results obtained with the code published in the DuMux-pub module Kelm2022a and post-processed effective quantities obtained using... |
Feb 24, 2023 - Collaborative Artificial Intelligence
Bulling, Andreas, 2023, "MPIIEmo", https://doi.org/10.18419/DARUS-3287, DaRUS, V1
We present a human-validated dataset that contains 224 high-resolution, multi-view video clips and audio recordings of emotionally charged interactions between eight couples of actors. The dataset is fully annotated with categorical labels for four basic emotions (anger, happiness, sadness, and surprise) and continuous labels for valence, activatio... |
Feb 20, 2023 - PN 6
Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/DARUS-3299, DaRUS, V1
Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoints and stores the metadata for the checkpoint and parameters for the mo... |
Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/DARUS-2829, DaRUS, V1
This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 benchmark from here and referenced in Köppel et al. 2019 There are a set... |
Feb 14, 2023 - Institute for Theoretical Physics III: Büchler Group
Ilg, Tobias; Büchler, Hans Peter, 2023, "Data for "Ground-state stability and excitation spectrum of a one-dimensional dipolar supersolid"", https://doi.org/10.18419/DARUS-3295, DaRUS, V1
Mathematica file and dataset to reproduce the results in "Ground-state stability and excitation spectrum of a one-dimensional dipolar supersolid". The Mathematica file "Groundstatestabiity.nb" created with Mathematica 12.0.0.0 contains all relevant functions and descriptions to reproduce the data used for the related publication. The files datN(s/f... |
Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media
Keim, Leon; Class, Holger; Schirmer, Larissa; Strauch, Bettina; Wendel, Kai; Zimmer, Martin, 2023, "Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column", https://doi.org/10.18419/DARUS-3276, DaRUS, V1
This dataset contains the DuMux code for the simulations in https://doi.org/10.3390/geosciences13020051 For the detailed list of software used, please have a look at the file install_class2023.sh. To run the simulations at your own computer, please conduct the following steps: Install docker for example in ubuntu . Make sure you can run docker with... |
