Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

161 to 170 of 278 Results
Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
May 25, 2023 - Data and Code for: Meta-Uncertainty in Bayesian Model Comparison
Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/DARUS-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]
This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper). The experiments from the accompanying paper (see below) are implemente...
deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning
May 15, 2023 - PN 4-7
Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/DARUS-3455, DaRUS, V1
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: dee...
Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""
May 15, 2023 - PN 4-7
Baier, Alexandra; Aspandi, Decky; Staab, Steffen, 2023, "Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""", https://doi.org/10.18419/DARUS-3457, DaRUS, V1
This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is available on Github.
Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals
May 12, 2023 - Materials Design
Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/DARUS-3339, DaRUS, V1
Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VASP files), - the low moment-tensor potentials (MTPs) and high-MTPs, - t...
micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, and 30% volume rubber content: samples 2 and 3
May 4, 2023 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2023, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, and 30% volume rubber content: samples 2 and 3", https://doi.org/10.18419/DARUS-3437, DaRUS, V1, UNF:6:LyvQPm+lMHvJ9Z4neyxihA== [fileUNF]
This dataset contains 12 micro X-ray Computed Tomography (micro-XRCT) data sets from scans of cylindrical particulate mixture samples (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The samples consist of monodisperse stiff (glass) and soft (rubber) particle mixtures. Both particles have an identical diameter of...
micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, 40%, and 60% volume rubber content: sample 1
May 4, 2023 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2023, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, 40%, and 60% volume rubber content: sample 1", https://doi.org/10.18419/DARUS-3436, DaRUS, V1, UNF:6:B6SNTx6Co9kvBdKrEWyCwA== [fileUNF]
This dataset contains 8 micro X-ray Computed Tomography (micro-XRCT) data sets from scans of cylindrical particulate mixture samples (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The samples consist of monodisperse stiff (glass) and soft (rubber) particle mixtures. Both particles have an identical diameter of 4...
DuMux 3.7.0
May 2, 2023 - DuMux
Oukili, Hamza; Ackermann, Sina; Buntic, Ivan; Class, Holger; Coltman, Edward; Flemisch, Bernd; Ghosh, Tufan; Gläser, Dennis; Grüninger, Christoph; Hommel, Johannes; Jupe, Tim; Keim, Leon; Kelm, Mathis; Kiemle, Stefanie; Koch, Timo; Kostelecky, Anna Mareike; Pallam, Harsha Vardhan; Schneider, Martin; Stadler, Leopold; Utz, Martin; Wang, Yue; Wendel, Kai; Winter, Roman; Wu, Hanchuan, 2023, "DuMux 3.7.0", https://doi.org/10.18419/DARUS-3405, DaRUS, V1
Release 3.7.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE.
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Apr 5, 2023 - PN 6
Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/DARUS-3394, DaRUS, V1
This dataset contains code and data for the third arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The code is also available on GitHub. Informati...
Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar
Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar
Apr 5, 2023 - Projects without PN Affiliation
Rettberg, Johannes; Wittwar, Dominik; Buchfink, Patrick; Brauchler, Alexander; Ziegler, Pascal; Fehr, Jörg; Haasdonk, Bernard, 2023, "Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar", https://doi.org/10.18419/DARUS-3248, DaRUS, V1
This dataset includes the system matrices in velocity formulation and a parallel coordinates plot of the sensitivity analysis from the paper titled "Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar". The system matrices can be used to perform further system reformulations acc...
In situ performed fracturing experiment of a limestone sample using an X-ray transparent triaxial cell: micro-XRCT data sets and measurement data
Apr 5, 2023 - Publication: Crystalline rocks
Ruf, Matthias; Lee, Dongwon; Steeb, Holger, 2023, "In situ performed fracturing experiment of a limestone sample using an X-ray transparent triaxial cell: micro-XRCT data sets and measurement data", https://doi.org/10.18419/DARUS-3106, DaRUS, V1, UNF:6:jZuOGs6RP72PrLA8cCwFjg== [fileUNF]
This dataset contains in situ recorded micro X-ray Computed Tomography (micro-XRCT) scan data sets (reconstructed and projection images) of a marine limestone sample under different triaxial loading conditions. The loading conditions were applied to establish the initial conditions to run the so-called Natural Hydraulic Fracture (NHF) protocol prop...
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.