Stuttgart Center for Simulation Science (SC SimTech)

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241 to 250 of 278 Results

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
preCICE Distribution Version v2104.0 Sep 27, 2021 - Usability and Sustainability of Simulation Software Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/DARUS-2125, DaRUS, V1 The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archive this specific version v2104.0 of the distribution. If you want to u...
Visual Analytics System for Hidden States in Recurrent Neural Networks Sep 10, 2021 - PN 6-4 Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/DARUS-2052, DaRUS, V1 Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analysis system. The sub directories contain the following: dataPreparation...
micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.96 mol/L initial concentration Sep 9, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.96 mol/L initial concentration", https://doi.org/10.18419/DARUS-2003, DaRUS, V1 This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.96 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 25% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...
micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.32 mol/L initial concentration Sep 9, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.32 mol/L initial concentration", https://doi.org/10.18419/DARUS-2002, DaRUS, V1 This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.32 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 20% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...
micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.64 mol/L initial concentration Sep 6, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.64 mol/L initial concentration", https://doi.org/10.18419/DARUS-1848, DaRUS, V1 This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.64 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 20% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...
Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces" Aug 20, 2021 - Paper Nature Materials 2021 Schlaich, Alexander, 2021, "Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"", https://doi.org/10.18419/DARUS-2115, DaRUS, V1 This dataset includes the basic simulation scripts needed in order to reproduce the data shown in "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces". The folder structure corresponds to the individual parts of the paper, more detail is given in the individual REA...
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...

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

preCICE Distribution Version v2104.0

Sep 27, 2021 - Usability and Sustainability of Simulation Software

Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/DARUS-2125, DaRUS, V1

The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archive this specific version v2104.0 of the distribution. If you want to u...

Visual Analytics System for Hidden States in Recurrent Neural Networks

Sep 10, 2021 - PN 6-4

Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/DARUS-2052, DaRUS, V1

Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analysis system. The sub directories contain the following: dataPreparation...

micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.96 mol/L initial concentration

Sep 9, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media

Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.96 mol/L initial concentration", https://doi.org/10.18419/DARUS-2003, DaRUS, V1

This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.96 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 25% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...

micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.32 mol/L initial concentration

Sep 9, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media

Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.32 mol/L initial concentration", https://doi.org/10.18419/DARUS-2002, DaRUS, V1

This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.32 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 20% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...

micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.64 mol/L initial concentration

Sep 6, 2021 - Publication: Porosity and Permeability of Subflorescent Salt Crusts Formed by Evaporation From Porous Media

Ruf, Matthias; Lee, Dongwon; Piotrowski, Joseph; Huisman, Johan Alexander; Steeb, Holger, 2021, "micro-XRCT data sets of subflorescent salt crusts from evaporation of MgSO4 solution with 0.64 mol/L initial concentration", https://doi.org/10.18419/DARUS-1848, DaRUS, V1

This data set contains data of scans of three separated dry salt crusts that formed by evaporation of magnesium sulfate solution with 0.64 mol/L initial concentration from F32 quartz sand at 40%, 30%, and 20% saturation inside the sand packing. The cylindrical sample holder from polymethyl methacrylat (PMMA) had a diameter of 31 mm and a wall thick...

Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"

Aug 20, 2021 - Paper Nature Materials 2021

Schlaich, Alexander, 2021, "Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"", https://doi.org/10.18419/DARUS-2115, DaRUS, V1

This dataset includes the basic simulation scripts needed in order to reproduce the data shown in "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces". The folder structure corresponds to the individual parts of the paper, more detail is given in the individual REA...

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...

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