Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

Projects without PN Affiliation

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.

241 to 250 of 293 Results

EnzymeML Metadata Block Configuration for Dataverse Aug 5, 2021 - RDM Tools Range, Jan Peter; Iglezakis, Dorothea, 2021, "EnzymeML Metadata Block Configuration for Dataverse", https://doi.org/10.18419/DARUS-2105, DaRUS, V1 This dataset contains a TSV-File for the configuration of a metadata block for data repositories basing on Dataverse. The block helps to describe datasets with the concepts of the metadata scheme EnzymeML, which is a data model provided to facilitate the documentation and exchange of enzymological and biocatalytic research data. To add a metadata b...
Sampling Strategies of the Regime-and-memory model (RMM) Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty Gonzalez-Nicolas Alvarez, Ana, 2021, "Sampling Strategies of the Regime-and-memory model (RMM)", https://doi.org/10.18419/DARUS-2035, DaRUS, V1, UNF:6:JeAvfovoq369qtbASSmQjg== [fileUNF] This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling strategies. River discharge frequency sampling strategies. Low Q sampling strategies. High Q sampling strategies. Low and High Q sampling strategies.
Regime-and-memory model (RMM) Code Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty Gonzalez-Nicolas Alvarez, Ana, 2021, "Regime-and-memory model (RMM) Code", https://doi.org/10.18419/DARUS-2034, DaRUS, V1 We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with memory, using an autocorrelation over time.One explicit parameter of our model represents the export regime. This parameter can morph the model among chemostatic-type and chemodyn...
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle", https://doi.org/10.18419/DARUS-2023, DaRUS, V1, UNF:6:kJCrJ2myXSEklpfIfr7AfQ== [fileUNF] This data contains a Thermal Humid Mechanical Cycle (THMC) of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The THMC is divided into a preheating step, a programming step and a s...
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis", https://doi.org/10.18419/DARUS-2021, DaRUS, V1, UNF:6:YC87IXxORCLw3+woYmYT3A== [fileUNF] This data contains iso-thermal and iso-humid shear frequency-sweep measurements of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). Frequency-sweeps were performed in a temperature...
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Kuhn, Moritz; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements", https://doi.org/10.18419/DARUS-2024, DaRUS, V1, UNF:6:ig+5CuLNRHaoCQtncwbvWA== [fileUNF] This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The samples were dried in a drying oven for 10 days pri...
CAMPOS Project P8: Conceptual Model Uncertainty(Universität Stuttgart) Jun 21, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)
Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/DARUS-1776, DaRUS, V1 This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data and post-processing codes for the manuscript can be found here.
Computational Cognitive Science(Universität Stuttgart) Jun 15, 2021PN 7 Data collections by the Stuttgart CCS group, mostly Encephalography, Eye-Tracking and human behavior data.
micro-XRCT data sets of a stone mastic asphalt drill core before and after a uniaxial compression test (sample 2): sample 2-3 Jun 7, 2021 - Publication: Asphalt Ruf, Matthias; Teutsch, Tim; Alber, Stefan; Steeb, Holger; Ressel, Wolfram, 2021, "micro-XRCT data sets of a stone mastic asphalt drill core before and after a uniaxial compression test (sample 2): sample 2-3", https://doi.org/10.18419/DARUS-1834, DaRUS, V1, UNF:6:QoLna7YlKfmQGxjSgS9yUA== [fileUNF] This data set contains two micro X-ray Computed Tomography (micro-XRCT) data sets resulting from region of interest scans (diameter 58.32 mm, height 46.06 mm) of the center region of a stone mastic asphalt (SMA) drill core. The drill core was scanned with identical scanner settings before ("reconstructed_20200505_01.tar.gz") and after ("reconstruct...

EnzymeML Metadata Block Configuration for Dataverse

Aug 5, 2021 - RDM Tools

Range, Jan Peter; Iglezakis, Dorothea, 2021, "EnzymeML Metadata Block Configuration for Dataverse", https://doi.org/10.18419/DARUS-2105, DaRUS, V1

This dataset contains a TSV-File for the configuration of a metadata block for data repositories basing on Dataverse. The block helps to describe datasets with the concepts of the metadata scheme EnzymeML, which is a data model provided to facilitate the documentation and exchange of enzymological and biocatalytic research data. To add a metadata b...

Sampling Strategies of the Regime-and-memory model (RMM)

Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty

Gonzalez-Nicolas Alvarez, Ana, 2021, "Sampling Strategies of the Regime-and-memory model (RMM)", https://doi.org/10.18419/DARUS-2035, DaRUS, V1, UNF:6:JeAvfovoq369qtbASSmQjg== [fileUNF]

This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling strategies. River discharge frequency sampling strategies. Low Q sampling strategies. High Q sampling strategies. Low and High Q sampling strategies.

Regime-and-memory model (RMM) Code

Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty

Gonzalez-Nicolas Alvarez, Ana, 2021, "Regime-and-memory model (RMM) Code", https://doi.org/10.18419/DARUS-2034, DaRUS, V1

We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with memory, using an autocorrelation over time.One explicit parameter of our model represents the export regime. This parameter can morph the model among chemostatic-type and chemodyn...

Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle

Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling

Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle", https://doi.org/10.18419/DARUS-2023, DaRUS, V1, UNF:6:kJCrJ2myXSEklpfIfr7AfQ== [fileUNF]

This data contains a Thermal Humid Mechanical Cycle (THMC) of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The THMC is divided into a preheating step, a programming step and a s...

Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis

Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling

Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis", https://doi.org/10.18419/DARUS-2021, DaRUS, V1, UNF:6:YC87IXxORCLw3+woYmYT3A== [fileUNF]

This data contains iso-thermal and iso-humid shear frequency-sweep measurements of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). Frequency-sweeps were performed in a temperature...

Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements

Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling

Fauser, Dominik; Kuhn, Moritz; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements", https://doi.org/10.18419/DARUS-2024, DaRUS, V1, UNF:6:ig+5CuLNRHaoCQtncwbvWA== [fileUNF]

This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The samples were dried in a drying oven for 10 days pri...

CAMPOS Project P8: Conceptual Model Uncertainty(Universität Stuttgart)

Jun 21, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data

Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/DARUS-1776, DaRUS, V1

This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data and post-processing codes for the manuscript can be found here.

Computational Cognitive Science(Universität Stuttgart)

Jun 15, 2021PN 7

Data collections by the Stuttgart CCS group, mostly Encephalography, Eye-Tracking and human behavior data.

micro-XRCT data sets of a stone mastic asphalt drill core before and after a uniaxial compression test (sample 2): sample 2-3

Jun 7, 2021 - Publication: Asphalt

Ruf, Matthias; Teutsch, Tim; Alber, Stefan; Steeb, Holger; Ressel, Wolfram, 2021, "micro-XRCT data sets of a stone mastic asphalt drill core before and after a uniaxial compression test (sample 2): sample 2-3", https://doi.org/10.18419/DARUS-1834, DaRUS, V1, UNF:6:QoLna7YlKfmQGxjSgS9yUA== [fileUNF]

This data set contains two micro X-ray Computed Tomography (micro-XRCT) data sets resulting from region of interest scans (diameter 58.32 mm, height 46.06 mm) of the center region of a stone mastic asphalt (SMA) drill core. The drill core was scanned with identical scanner settings before ("reconstructed_20200505_01.tar.gz") and after ("reconstruct...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications