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License: MIT License

1 to 10 of 33 Results

Image processing code for characterization of multiphase flow in porous media May 15, 2024 - Publication: Microfluidic experiments Vahid Dastjerdi, Samaneh; Steeb, Holger, 2024, "Image processing code for characterization of multiphase flow in porous media", https://doi.org/10.18419/darus-4153, DaRUS, V1 This work utilizes microfluidic experiments to gather data captured as snapshots during the experiments. These snapshots provide real-time information and undergo image processing to derive the required data. Image processing involves several steps tailored to the investigations:...
Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study Apr 2, 2024 - Scientific Computing Strack, Alexander; Taylor, Christopher; Diehl, Patrick; Pflüger, Dirk, 2024, "Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study", https://doi.org/10.18419/darus-4094, DaRUS, V1 The source code and benchmark scripts related to "Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study". This paper conducts a case study of the multidimensional Fast Fourier Transform to identify which applications will...
Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories Mar 27, 2024 - Publication Tools Roy, Sarbani; Wang, Fangfang; Gläser, Dennis, 2024, "Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories", https://doi.org/10.18419/darus-3785, DaRUS, V1 Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata fro...
Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference Mar 14, 2024 - PN 2-7 Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/darus-4093, DaRUS, V1 This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.
Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data Mar 14, 2024 - PN 6A-4 Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", https://doi.org/10.18419/darus-4087, DaRUS, V1 Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\ │ ├── plots\ ├...
ABxM.DistributedRobotics.RADr: Agent-based Design and Control of multiple Roaming Autonomous Distributed robots (RADr) Mar 13, 2024 - EXC IntCDC Research Project 19 'Co-Design Methods for Developing Distributed Cooperative Multi-Robot Systems for Construction' Leder, Samuel; Siriwardena, Lasath; Menges, Achim, 2024, "ABxM.DistributedRobotics.RADr: Agent-based Design and Control of multiple Roaming Autonomous Distributed robots (RADr)", https://doi.org/10.18419/darus-4058, DaRUS, V1 ABxM.DistributedRobotics.RADr is an add-on to ABxM.Core for agent-based design and control of multiple Roaming Autonomous Distributed robots (RADr) that assemble hexagonal digital materials. The add-on contains various agent system constructs and utilities for simulation of the s...
Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/darus-4031, DaRUS, V1 This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the st...
Code and benchmark for NPCS, a Native Provenance Computation for SPARQL Feb 21, 2024 - Analytic Computing Asma, Zubaria; Hernández, Daniel; Galárraga, Luis; Flouris, Giorgos; Fundulaki, Irini; Hose, Katja, 2024, "Code and benchmark for NPCS, a Native Provenance Computation for SPARQL", https://doi.org/10.18419/darus-3973, DaRUS, V1 Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL. The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenanc...
Code for From Shapes to Shapes Feb 16, 2024 - Analytic Computing Seifer, Philipp; Hernández, Daniel; Lämmel, Ralf; Staab, Steffen, 2024, "Code for From Shapes to Shapes", https://doi.org/10.18419/darus-3977, DaRUS, V1 This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm imp...
Code for Training and Testing CNVVE Feb 13, 2024 - Analytic Computing Hedeshy, Ramin; Menges, Raphael; Staab, Steffen, 2024, "Code for Training and Testing CNVVE", https://doi.org/10.18419/darus-3896, DaRUS, V1 This dataset consists of files used for training and testing the CNVVE Dataset. This dataset consists of 950 audio samples encompassing six distinct classes of voice expressions. These expressions were collected from 42 generous individuals who donated their voice recordings for...

Image processing code for characterization of multiphase flow in porous media

May 15, 2024 - Publication: Microfluidic experiments

Vahid Dastjerdi, Samaneh; Steeb, Holger, 2024, "Image processing code for characterization of multiphase flow in porous media", https://doi.org/10.18419/darus-4153, DaRUS, V1

This work utilizes microfluidic experiments to gather data captured as snapshots during the experiments. These snapshots provide real-time information and undergo image processing to derive the required data. Image processing involves several steps tailored to the investigations:...

Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study

Apr 2, 2024 - Scientific Computing

Strack, Alexander; Taylor, Christopher; Diehl, Patrick; Pflüger, Dirk, 2024, "Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study", https://doi.org/10.18419/darus-4094, DaRUS, V1

The source code and benchmark scripts related to "Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study". This paper conducts a case study of the multidimensional Fast Fourier Transform to identify which applications will...

Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories

Mar 27, 2024 - Publication Tools

Roy, Sarbani; Wang, Fangfang; Gläser, Dennis, 2024, "Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories", https://doi.org/10.18419/darus-3785, DaRUS, V1

Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata fro...

Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Mar 14, 2024 - PN 2-7

Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/darus-4093, DaRUS, V1

This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.

Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data

Mar 14, 2024 - PN 6A-4

Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", https://doi.org/10.18419/darus-4087, DaRUS, V1

Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\ │ ├── plots\ ├...

ABxM.DistributedRobotics.RADr: Agent-based Design and Control of multiple Roaming Autonomous Distributed robots (RADr)

Mar 13, 2024 - EXC IntCDC Research Project 19 'Co-Design Methods for Developing Distributed Cooperative Multi-Robot Systems for Construction'

Leder, Samuel; Siriwardena, Lasath; Menges, Achim, 2024, "ABxM.DistributedRobotics.RADr: Agent-based Design and Control of multiple Roaming Autonomous Distributed robots (RADr)", https://doi.org/10.18419/darus-4058, DaRUS, V1

ABxM.DistributedRobotics.RADr is an add-on to ABxM.Core for agent-based design and control of multiple Roaming Autonomous Distributed robots (RADr) that assemble hexagonal digital materials. The add-on contains various agent system constructs and utilities for simulation of the s...

Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models

Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries

Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/darus-4031, DaRUS, V1

This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the st...

Code and benchmark for NPCS, a Native Provenance Computation for SPARQL

Feb 21, 2024 - Analytic Computing

Asma, Zubaria; Hernández, Daniel; Galárraga, Luis; Flouris, Giorgos; Fundulaki, Irini; Hose, Katja, 2024, "Code and benchmark for NPCS, a Native Provenance Computation for SPARQL", https://doi.org/10.18419/darus-3973, DaRUS, V1

Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL. The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenanc...

Code for From Shapes to Shapes

Feb 16, 2024 - Analytic Computing

Seifer, Philipp; Hernández, Daniel; Lämmel, Ralf; Staab, Steffen, 2024, "Code for From Shapes to Shapes", https://doi.org/10.18419/darus-3977, DaRUS, V1

This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm imp...

Code for Training and Testing CNVVE

Feb 13, 2024 - Analytic Computing

Hedeshy, Ramin; Menges, Raphael; Staab, Steffen, 2024, "Code for Training and Testing CNVVE", https://doi.org/10.18419/darus-3896, DaRUS, V1

This dataset consists of files used for training and testing the CNVVE Dataset. This dataset consists of 950 audio samples encompassing six distinct classes of voice expressions. These expressions were collected from 42 generous individuals who donated their voice recordings for...

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