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This is the Data Repository of the University of Stuttgart.

SFB 1313 "Interface-Driven Multi-Field Processes in Porous Media"

SFB 1333 "Molecular heterogeneous catalysis in confined geometries"

Institute of Biochemistry

Institute of Applied Mechanics (MIB)

Institute of Thermodynamics and Thermal Process Engineering

Stuttgart Center for Simulation Science (SC SimTech)

Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (EXC IntCDC)

SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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1 to 10 of 3,519 Results

Literature Dataset to derive Requirements for Digital Product Passports and AAS for Load Carriers Oct 13, 2026 - Department VIII: Information Systems II Petrik, Dimitri, 2026, "Literature Dataset to derive Requirements for Digital Product Passports and AAS for Load Carriers", https://doi.org/10.18419/DARUS-6261, DaRUS, V1, UNF:6:LJK5HgjhCuuALViKt6aydw== [fileUNF] Identified literature sample to derive functional requirements on digital product passports when they are extended beyond the product boundaries to support cleaner logistics through data models of smart loading carriers.
Scalar Lur'e system with dead-zone nonlinearity for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated Oct 13, 2026 - Analytic Computing Frank, Daniel, 2026, "Scalar Lur'e system with dead-zone nonlinearity for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated", https://doi.org/10.18419/DARUS-6521, DaRUS, V1 Overview Input-output data of a scalar discrete-time Lur'e system with a dead-zone nonlinearity. The system is regionally but not globally stable: inside the dead zone it is a stable linear system, outside it is unstable. The dataset has two parts: converging trajectories that stay bounded over the whole horizon, and diverging trajectories that sta...
Duffing oscillator for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated Oct 13, 2026 - Analytic Computing Frank, Daniel, 2026, "Duffing oscillator for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated", https://doi.org/10.18419/DARUS-6520, DaRUS, V1 Overview Input-output data of a damped, softening Duffing oscillator, it also includes the internal state. The system is regionally but not globally stable: the origin is asymptotically stable, but the saddle points (±1, 0) bound its basin of attraction, and inputs that push the state across that boundary make it diverge. The dataset therefore has...
Multi-Level Dataset of Using Wood Well Metrics on 695 Timber Buildings Oct 12, 2026 - Using Wood Well: Better Metrics for Better Use Svatoš-Ražnjević, Hana; Ogle, Alastair; Menges, Achim, 2026, "Multi-Level Dataset of Using Wood Well Metrics on 695 Timber Buildings", https://doi.org/10.18419/DARUS-6284, DaRUS, V2, UNF:6:hzHvc9/3HZ6aS7hIOn0Ztg== [fileUNF] The dataset consists of three related CSV files representing the results of metric development and application on timber buildings within the Using Wood Well project, which brings together a large international sample of building projects. The Level 1 (L1) dataset examines Wood Use Intensity (WUI-Project) across 695 timber building projects from 25...
BIOS semantic data platform 1.0.0 Oct 12, 2026 - PANTABIO Köster, Helmut, 2026, "BIOS semantic data platform 1.0.0", https://doi.org/10.18419/DARUS-6431, DaRUS, V1 Software release for the BIOS semantic data platform to share, annotate and utilise data for a distributed DBTL cycle for synthetic biology. BIOS is a modular research data platform that helps laboratory teams organize data, describe it consistently, and connect it to experimental work. It brings files, metadata, protocols, and experiment results i...
EXC IntCDC Research Project 38 'Green Infrastructures for Building Stock Transformation'(University of Stuttgart, Max Planck Institute for Intelligent Systems) Oct 8, 2026Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (EXC IntCDC) RP38-1: Regenerative Green Infrastructures for Existing Building Stock Densification, Conversion and Retrofitting
CSO events - extended micropollutants Oct 8, 2026 - KomS Dataverse Stricker, Birthe; Kuch, Bertram; Schönung, Jürgen; Stumpf, Bernhard; Kohlgrueber, Vera; Braeutigam, Patrick, 2026, "CSO events - extended micropollutants", https://doi.org/10.18419/DARUS-6467, DaRUS, V1 Combined sewage overflow events were sampled in 2024 and 2025 by the Mannheim Municipal Drainage (Eigenbetrieb Stadtentwässerung Mannheim) at their ten most relevant (based on their yearly overflow volume) CSO retention basins as well as storage sewers within the catchment area of Mannheim (Germany). Samples were taken time-proportional as 20-min q...
Replication Data for: Model Order Reduction for Parametric Dissipative Quantum Systems Oct 8, 2026 - Institute of Applied Analysis and Numerical Simulation Haasdonk, Bernard; Stamm, Benjamin; ZENG, Zhuoyao, 2026, "Replication Data for: Model Order Reduction for Parametric Dissipative Quantum Systems", https://doi.org/10.18419/DARUS-6519, DaRUS, V1, UNF:6:8h5IzpPtovnZ6Q3+22q5DA== [fileUNF] Companion code and replication data for the paper "Model Order Reduction for Parametric Dissipative Quantum Systems" (Haasdonk, Stamm, Zeng). The Python package lindbladian_rbm builds reduced-basis (RB) surrogates of the steady state of parametrised Lindbladian master equations, in two reduced-space formulations: EigGreedy (a unit-norm constrained...
Knowledge Graphs(Universität Stuttgart) Oct 8, 2026Analytic Computing
Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models Oct 7, 2026 - Stochastic Simulation and Safety Research for Hydrosystems (LS3) Scheurer, Stefania; Frenner, Riccardo; Bruennette, Tim; Oladyshkin, Sergey; Nowak, Wolfgang, 2026, "Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models", https://doi.org/10.18419/DARUS-5967, DaRUS, V1, UNF:6:+gEES8LITw2YkntYAMuceg== [fileUNF] Supplementary code to replicate key experiments from our paper. The code computes confidence intervals for the Finite Volume Neural Network (FINN), a physics-aware ML model that learns unknown parts of a PDE (here, the retardation function of a diffusion-sorption model). The workflow has four steps: 1. Train FINN on the observations. 2. Learn an em...

Literature Dataset to derive Requirements for Digital Product Passports and AAS for Load Carriers

Oct 13, 2026 - Department VIII: Information Systems II

Petrik, Dimitri, 2026, "Literature Dataset to derive Requirements for Digital Product Passports and AAS for Load Carriers", https://doi.org/10.18419/DARUS-6261, DaRUS, V1, UNF:6:LJK5HgjhCuuALViKt6aydw== [fileUNF]

Identified literature sample to derive functional requirements on digital product passports when they are extended beyond the product boundaries to support cleaner logistics through data models of smart loading carriers.

Scalar Lur'e system with dead-zone nonlinearity for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated

Oct 13, 2026 - Analytic Computing

Frank, Daniel, 2026, "Scalar Lur'e system with dead-zone nonlinearity for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated", https://doi.org/10.18419/DARUS-6521, DaRUS, V1

Overview Input-output data of a scalar discrete-time Lur'e system with a dead-zone nonlinearity. The system is regionally but not globally stable: inside the dead zone it is a stable linear system, outside it is unstable. The dataset has two parts: converging trajectories that stay bounded over the whole horizon, and diverging trajectories that sta...

Duffing oscillator for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated

Oct 13, 2026 - Analytic Computing

Frank, Daniel, 2026, "Duffing oscillator for nonlinear system identification - regionally stable, with converging and diverging trajectories - synthetically generated", https://doi.org/10.18419/DARUS-6520, DaRUS, V1

Overview Input-output data of a damped, softening Duffing oscillator, it also includes the internal state. The system is regionally but not globally stable: the origin is asymptotically stable, but the saddle points (±1, 0) bound its basin of attraction, and inputs that push the state across that boundary make it diverge. The dataset therefore has...

Multi-Level Dataset of Using Wood Well Metrics on 695 Timber Buildings

Oct 12, 2026 - Using Wood Well: Better Metrics for Better Use

Svatoš-Ražnjević, Hana; Ogle, Alastair; Menges, Achim, 2026, "Multi-Level Dataset of Using Wood Well Metrics on 695 Timber Buildings", https://doi.org/10.18419/DARUS-6284, DaRUS, V2, UNF:6:hzHvc9/3HZ6aS7hIOn0Ztg== [fileUNF]

The dataset consists of three related CSV files representing the results of metric development and application on timber buildings within the Using Wood Well project, which brings together a large international sample of building projects. The Level 1 (L1) dataset examines Wood Use Intensity (WUI-Project) across 695 timber building projects from 25...

BIOS semantic data platform 1.0.0

Oct 12, 2026 - PANTABIO

Köster, Helmut, 2026, "BIOS semantic data platform 1.0.0", https://doi.org/10.18419/DARUS-6431, DaRUS, V1

Software release for the BIOS semantic data platform to share, annotate and utilise data for a distributed DBTL cycle for synthetic biology. BIOS is a modular research data platform that helps laboratory teams organize data, describe it consistently, and connect it to experimental work. It brings files, metadata, protocols, and experiment results i...

EXC IntCDC Research Project 38 'Green Infrastructures for Building Stock Transformation'(University of Stuttgart, Max Planck Institute for Intelligent Systems)

Oct 8, 2026Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (EXC IntCDC)

RP38-1: Regenerative Green Infrastructures for Existing Building Stock Densification, Conversion and Retrofitting

CSO events - extended micropollutants

Oct 8, 2026 - KomS Dataverse

Stricker, Birthe; Kuch, Bertram; Schönung, Jürgen; Stumpf, Bernhard; Kohlgrueber, Vera; Braeutigam, Patrick, 2026, "CSO events - extended micropollutants", https://doi.org/10.18419/DARUS-6467, DaRUS, V1

Combined sewage overflow events were sampled in 2024 and 2025 by the Mannheim Municipal Drainage (Eigenbetrieb Stadtentwässerung Mannheim) at their ten most relevant (based on their yearly overflow volume) CSO retention basins as well as storage sewers within the catchment area of Mannheim (Germany). Samples were taken time-proportional as 20-min q...

Replication Data for: Model Order Reduction for Parametric Dissipative Quantum Systems

Oct 8, 2026 - Institute of Applied Analysis and Numerical Simulation

Haasdonk, Bernard; Stamm, Benjamin; ZENG, Zhuoyao, 2026, "Replication Data for: Model Order Reduction for Parametric Dissipative Quantum Systems", https://doi.org/10.18419/DARUS-6519, DaRUS, V1, UNF:6:8h5IzpPtovnZ6Q3+22q5DA== [fileUNF]

Companion code and replication data for the paper "Model Order Reduction for Parametric Dissipative Quantum Systems" (Haasdonk, Stamm, Zeng). The Python package lindbladian_rbm builds reduced-basis (RB) surrogates of the steady state of parametrised Lindbladian master equations, in two reduced-space formulations: EigGreedy (a unit-norm constrained...

Knowledge Graphs(Universität Stuttgart)

Oct 8, 2026Analytic Computing

Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models

Oct 7, 2026 - Stochastic Simulation and Safety Research for Hydrosystems (LS3)

Scheurer, Stefania; Frenner, Riccardo; Bruennette, Tim; Oladyshkin, Sergey; Nowak, Wolfgang, 2026, "Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models", https://doi.org/10.18419/DARUS-5967, DaRUS, V1, UNF:6:+gEES8LITw2YkntYAMuceg== [fileUNF]

Supplementary code to replicate key experiments from our paper. The code computes confidence intervals for the Finite Volume Neural Network (FINN), a physics-aware ML model that learns unknown parts of a PDE (here, the retardation function of a diffusion-sorption model). The workflow has four steps: 1. Train FINN on the observations. 2. Learn an em...

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