1 to 10 of 36 Results
Oct 13, 2026
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... |
Oct 13, 2026
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... |
Oct 8, 2026
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Jul 29, 2026
Hernández, Daniel; Jung, Jong Hyun; Ikeda, Yuji; Ou, Yongliang; Kumar, Pranav; Schächtel, Tom; Liu, Wenchuan; Li, Xin; Zhang, Xi; Xu, Xiang; Zhu, Li-Fang; Körmann, Fritz; Staab, Steffen; Grabowski, Blazej, 2026, "MLIPs Ontology", https://doi.org/10.18419/DARUS-5948, DaRUS, V1
MLIPs Ontology: An Ontology for Machine Learning Interatomic Potentials More information can be found in the README.md. |
Jul 9, 2026
Thapa, Ratan Bahadur; Hernández, Daniel, 2026, "Artifacts for: Identifiability of Relational Queries in Multi-View Pretraining", https://doi.org/10.18419/DARUS-6292, DaRUS, V1
This dataset contains the complete replication artifacts for the paper Identifiability of Relational Queries in Multi-View Pretraining (Ratan Bahadur Thapa and Daniel Hernández). The paper studies when a relational query is identifiable from a data-integration interface—that is, when every legal completion of the data consistent with the interface... |
Jun 9, 2026
Seifer, Philipp; Hernández, Daniel; Lämmel, Ralf; Staab, Steffen, 2024, "Code for From Shapes to Shapes", https://doi.org/10.18419/DARUS-3977, DaRUS, V2
From Shapes to Shapes (s2s): a tool and library for inferring shape constraints that validate the result graphs of graph-transformation queries. Version 1.0 accompanies "From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries" (WWW 2024, doi:10.1145/3589334.3645550); it archives the v1.0.0 source tree. Version 2.0 acco... |
Jan 30, 2026 - IRIS 3D
Jalali Farahani, Farane; Hanke, Sara; Heiberger, Raphael; Staab, Steffen, 2026, "Reddit Social Group Mentions (Reddit-SGM)", https://doi.org/10.18419/DARUS-4557, DaRUS, V1, UNF:6:6H2MeoPFhr2nY8Rl0iKylA== [fileUNF]
Dataset Name: Reddit Social Group Mentions (Reddit-SGM) is a dataset based on Reddit comments and contains mentions of social groups annotated by three different human annotators. Corpus Size: 2,040 comments Source: Reddit Politosphere Dataset (DOI) Subreddits Included: r/politics r/worldpolitics r/ukpolitics r/Economics r/Libertarian Focus: Analys... |
Sep 8, 2025 - IRIS 3D
Fathallah, Nadeen; Hernández, Daniel; Staab, Steffen, 2025, "AccessGuru", https://doi.org/10.18419/DARUS-5177, DaRUS, V1, UNF:6:5MctEb5nlHfFUXpoBqSNMg== [fileUNF]
AccessGuru dataset contains over 3,500 real-world Web accessibility violations collected from 448 diverse websites across domains such as health, education, government, news, technology, and e-commerce. Each instance is annotated with one of 112 distinct violation types, spanning syntactic, semantic, and layout categories as defined by WCAG 2.1 gui... |
Apr 28, 2025 - KR-Building (EXC IntCDC)
Pan, Xinyi; Hernández, Daniel; Seifer, Philipp; Lämmel, Ralf; Staab, Steffen, 2025, "eSPARQL Implementation", https://doi.org/10.18419/DARUS-4344, DaRUS, V1, UNF:6:t0V66ThHSAjSCe2jRbbJng== [fileUNF]
This project contains the code of an implementation of the eSPARQL language, which extends SPARQL-star with a `FROM BELIEF` that allows an easy formulation of epistemic queries. Further information can be found in the README.md file. |
Feb 28, 2025
Fathallah, Nadeen, 2025, "Code for Improving Video Caption Accuracy with LLMs", https://doi.org/10.18419/DARUS-4776, DaRUS, V1
As part of the IKILeUS project at the University of Stuttgart, research was conducted to explore how Large Language Models (LLMs) can enhance the accuracy and contextual relevance of automatic speech recognition (ASR)-generated captions. While ASR tools provide a foundation for accessibility, they often produce grammatical errors, misinterpret homo... |
