DaRUS

Metrics

3,796,769 Downloads

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.

Funding Information Agency: DFG

1 to 10 of 727 Results

Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production Jul 17, 2025 - Bioprocess Seeger, Jan; Müller, Susanne; Gómez-Álvarez, Helena; Rashid, Goran M.M.; Bugg, Timothy; Díaz, Eduardo; Takors, Ralf, 2025, "Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production", https://doi.org/10.18419/DARUS-4525, DaRUS, V1 Raw experimental data for the research article: "Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production" The methodology and materials used to generate this data are described in the associated research article. The here presented data follow the structure of that...
Data for: Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation Jul 16, 2025 - A01 Gas-Surface Interactions Investigated by Atomistic Simulations Kümmel, Simon; Roth, Johannes, 2025, "Data for: Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation", https://doi.org/10.18419/DARUS-5161, DaRUS, V1 This data set includes DFT and MD data that was used for the paper "Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation". In each folder, the respective data as well as analysis and visualization scripts written in Python are given. Singl...
demoa-base: a biophysics simulator for muscle-driven motion Jul 16, 2025 - demoa Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/DARUS-2550, DaRUS, V7 For more information, such as installation, requirements and user guide, please see the demoa manual. The development of this package was supported by “Deutsche Forschungsgemeinschaft” (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016.
DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study" Jul 16, 2025 - A02: Advanced modelling concepts for coupling free flow with porous-media flow Veyskarami, Maziar, 2025, "DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study"", https://doi.org/10.18419/DARUS-5071, DaRUS, V1 This dataset contains the source code used for the work "Härter, J., Veyskarami, M., Schneider, M. et al. Self-Pumping Transpiration Cooling: A Joint Experimental and Numerical Study. Transp Porous Med 152, 56 (2025). (https://doi.org/10.1007/s11242-025-02198-w)"
Replication Data for: AI-assisted JSON Schema Creation and Mapping Jul 15, 2025 - Usability and Sustainability of Simulation Software Neubauer, Felix, 2025, "Replication Data for: AI-assisted JSON Schema Creation and Mapping", https://doi.org/10.18419/DARUS-5157, DaRUS, V1, UNF:6:cu/nriFjeIsGtYAoB7K7fw== [fileUNF] The steps and sources of the application example from the paper + the JSONata instructions for the LLM. Recommended: download all files and open the README.md with a markdown editor/viewer. The README.md document shows the steps performed in the application example and also corresponding screenshots and input and output documents. The screenshots a...
Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2024, "Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points", https://doi.org/10.18419/DARUS-4156, DaRUS, V2 This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 3 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8...
Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2025, "Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points", https://doi.org/10.18419/DARUS-5065, DaRUS, V1 This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 4 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8...
Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia; Verburg, Corné, 2025, "Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points", https://doi.org/10.18419/DARUS-5081, DaRUS, V1 Models are trained with [git: DDUNet] on 101 data points (dp). Both, vanilla UNet and DDU-Net, can be applied directly end-to-end. For inference follow the guidelines of Heat Plume Prediction to prepare raw data, then apply the models as described in [git: DDUNet]. Based on raw data from https://doi.org/10.18419/darus-5064.
Trained Models on Real Permeability Fields, 4+1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1 Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based...
Trained Models on Synthetic Permeability Fields, 3+1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2025, "Trained Models on Synthetic Permeability Fields, 3+1 Data Points", https://doi.org/10.18419/DARUS-5080, DaRUS, V1 Models are trained with Heat Plume Prediction. Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. The vanilla UNet can be applied directly end-to-end, just does not give very good results. For inference follow the guidelines of Heat Plume Prediction...

Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production

Jul 17, 2025 - Bioprocess

Seeger, Jan; Müller, Susanne; Gómez-Álvarez, Helena; Rashid, Goran M.M.; Bugg, Timothy; Díaz, Eduardo; Takors, Ralf, 2025, "Data for: Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production", https://doi.org/10.18419/DARUS-4525, DaRUS, V1

Raw experimental data for the research article: "Scaling-up the Bioconversion of Lignin to 2,4-Pyridinedicarboxylic Acid With Engineered Pseudomonas putida for Bio-Based Plastics Production" The methodology and materials used to generate this data are described in the associated research article. The here presented data follow the structure of that...

Data for: Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation

Jul 16, 2025 - A01 Gas-Surface Interactions Investigated by Atomistic Simulations

Kümmel, Simon; Roth, Johannes, 2025, "Data for: Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation", https://doi.org/10.18419/DARUS-5161, DaRUS, V1

This data set includes DFT and MD data that was used for the paper "Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation". In each folder, the respective data as well as analysis and visualization scripts written in Python are given. Singl...

demoa-base: a biophysics simulator for muscle-driven motion

Jul 16, 2025 - demoa

Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/DARUS-2550, DaRUS, V7

For more information, such as installation, requirements and user guide, please see the demoa manual. The development of this package was supported by “Deutsche Forschungsgemeinschaft” (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016.

DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study"

Jul 16, 2025 - A02: Advanced modelling concepts for coupling free flow with porous-media flow

Veyskarami, Maziar, 2025, "DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study"", https://doi.org/10.18419/DARUS-5071, DaRUS, V1

This dataset contains the source code used for the work "Härter, J., Veyskarami, M., Schneider, M. et al. Self-Pumping Transpiration Cooling: A Joint Experimental and Numerical Study. Transp Porous Med 152, 56 (2025). (https://doi.org/10.1007/s11242-025-02198-w)"

Replication Data for: AI-assisted JSON Schema Creation and Mapping

Jul 15, 2025 - Usability and Sustainability of Simulation Software

Neubauer, Felix, 2025, "Replication Data for: AI-assisted JSON Schema Creation and Mapping", https://doi.org/10.18419/DARUS-5157, DaRUS, V1, UNF:6:cu/nriFjeIsGtYAoB7K7fw== [fileUNF]

The steps and sources of the application example from the paper + the JSONata instructions for the LLM. Recommended: download all files and open the README.md with a markdown editor/viewer. The README.md document shows the steps performed in the application example and also corresponding screenshots and input and output documents. The screenshots a...

Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2024, "Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points", https://doi.org/10.18419/DARUS-4156, DaRUS, V2

This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 3 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8...

Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2025, "Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points", https://doi.org/10.18419/DARUS-5065, DaRUS, V1

This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 4 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8...

Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia; Verburg, Corné, 2025, "Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points", https://doi.org/10.18419/DARUS-5081, DaRUS, V1

Models are trained with [git: DDUNet] on 101 data points (dp). Both, vanilla UNet and DDU-Net, can be applied directly end-to-end. For inference follow the guidelines of Heat Plume Prediction to prepare raw data, then apply the models as described in [git: DDUNet]. Based on raw data from https://doi.org/10.18419/darus-5064.

Trained Models on Real Permeability Fields, 4+1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1

Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based...

Trained Models on Synthetic Permeability Fields, 3+1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2025, "Trained Models on Synthetic Permeability Fields, 3+1 Data Points", https://doi.org/10.18419/DARUS-5080, DaRUS, V1

Models are trained with Heat Plume Prediction. Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. The vanilla UNet can be applied directly end-to-end, just does not give very good results. For inference follow the guidelines of Heat Plume Prediction...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications