DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Fiber Pattern Generation Tools for the Design of Long-Span, Core-Less Wound, Structural Composite Building Elements
No full textThis data set contains tools to generate fiber patterns for tubular surface components as used in the BUGA Fibre Pavilion (Website). The tools allowed to model the fiber patterns as polylines and anchor point sequences which acted as a base for planning the robotic motion paths for prefabrication of the BUGA Fibre composite components.
The surface-based syntax tool generates local support surfaces to keep subsequent fibers in place. The resulting fiber pattern is fully parametric and can be tailored to the specific requirements of different fiber components.
The interlaced syntax tool generates a smooth fiber body by continuously revolving around the component's long axis and alternating between boundary frames. The output is a continuous polyline. In the second step, the resulting fiber net is relaxed to approximate the final anticlastic shape of the fiber lattice.
The reinforcement tool allows the modeling of carbon fiber reinforcement patterns. Following a similar logic as the surface-based tool, the reinforcement wraps around the component and is then relaxed to approximate the final shape of the composite
Results data for the publication: Universal and Automated Approaches for Optimising the Processing Order of Geometries in a CAM Tool for Redundant Galvanometer Scanner-Based Systems
No full textThis data repository contains the results data associated with a publication named: Universal and Automated Approaches for Optimising the Processing Order of Geometries in a CAM Tool for Redundant Galvanometer Scanner-Based Systems
The publication focuses on the design and development of a CAM tool. The tool is developed for the application of laser marking with kinematically redundant motion systems using galvanometer scanners. The core functionality of the tool is the optimisation of the processing sequence of different geometries to be marked. This serves as the basis for path separation algorithms.
The data contained in this repository is related to the results section of the publication. It contains plots visualising the process order optimisation. The plots relate to the test cases referenced in the publication.
Further information can be found in:
D. Kurth, C. Reiff, Y. Jiang and A. Verl, 'Universal and Automated Approaches for Optimising the Processing Order of Geometries in a CAM Tool for Redundant Galvanometer Scanner-Based Systems"
This project is supported by the German Federal Ministry of Economics and Technology (BMWK) on the basis of a decision of the German Bundestag. It is also supported by the Ministry of Science, Research and the Arts of the State of Baden-Württemberg within the framework of the InnovationCampus Future Mobility (ICM).</p
Multi-storey Timber Buildings and Design and Construction Stakeholder Constellations Data: 99 DACH Projects
No full textThis repository contains a collection of data on 99 contemporary multi-storey timber building projects in the DACH region (Germany, Austria, and Switzerland), designed and planned between 2004 and 2021, and the stakeholders involved in their design and construction. The dataset consists of quantitative and qualitative project data on buildings 4 stories tall and above. This includes general information on project location, construction year, building height (number of stories), links, type of structural system, materials used in construction, and classification of the design based on the form of the massing and the organization principle of the floorplan, as well as a classification into innovation trajectories in timber construction. These are defined as the following three groups; (T1) standard innovation, (T2) incremental innovation, and (T3) pioneering innovation trajectories in timber construction. A total of 306 project stakeholders, their roles on the project as architects, engineers, timber engineers, general contractors, fabricators, and timber suppliers, the client type (public or private) and, when available, the motivation for the use of timber (i.e., sustainability) are also included in the dataset. Link to their website, and location absed on the headquarters, are included in the data on the stakeholders. In addition, the buildings data includes projectID's that can be used to find the projects in some of the related datasets
IntCDC Research Integration in Building Demonstrator - Ecological Quality
No full textBuilding on the progress in IntCDC Building planning and construction, the life cycle assessment of the integrated research systems has been performed in interaction with the respective research teams. The overall environmental impact is provided focusing on Global Warming Potential (GWP) and supplemented by overall environmental impact. In the following a summary of the findings is provided, complemented by a report for each research system
Replication Data of B3 for: "Asymmetric Rh Diene Catalysis under Confinement: Isoxazole Ring-Contraction in Mesoporous Solids"
No full textIn this dataset HPLC (high performance liquid chromatography) chromatograms and NMR (nuclear magnetic resonance) spectra of all newly prepared ligands, catalysts and catalysis products are included. Data from collaborating groups can be found in a seperate data set. The NMR spectra are structured according to the chapters of the research article in which they appear. The HPLC data is structured according to the molecule abbreviations used in the Supporting Information
Bayesian Modeling of Time Series Data (BayModTS)
No full textBayModTS is a FAIR workflow for processing highly variable and sparse data. The code and results of the examples in the BayModTS paper are stored in this repository. A maintained version of BayModTS that can be applied to your personal applications can be found on Git Hub
CNVVE Dataset clean audio samples
No full textThis CNVVE Dataset contains clean audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or
“mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders.
These audio samples have undergone a thorough cleaning process. The raw samples are published in https://doi.org/10.18419/darus-3897. Initially, we applied the Google WebRTC voice activity detection (VAD) algorithm on the given audio files to remove noise or silence from the collected voice signals. The intensity was set to "2", which could be a value between "1" and "3". However, because of variations in the data, some files required additional manual cleaning. These outliers, characterized by sharp click sounds (such as those occurring at the end of recordings), were addressed.
The samples are recorded through a dedicated website for data collection that defines the purpose and type of voice data by providing example recordings to
participants as well as the expressions’ written equivalent, e.g., “Uh-huh”. Audio recordings were automatically saved in the .wav format and kept
anonymous, with a sampling rate of 48 kHz and a bit depth of 32 bits.
For more info, please check the paper or feel free to contact the authors for any inquiries.</p
COLife_01 - Gigamap and DIY Files
No full textThe data cover codesigned gigamap and parametric and analogue DIY files of BioDiveIn more-than-human intervention. The gigamap was a product of digital participation in the Miro platform, updated by analogue workshops with stakeholders in feedback loops. The data were produced in the winter semester 2022/23 as a part of the design studio 'COLife: More-than-Human Perspective to CoDesign'
Replication data of Buchmeiser group for: "Synthetic and Structural Peculiarities of Neutral and Cationic Molybdenum Imido and Tungsten Oxo Alkylidene Complexes Bearing Weakly Coordinating N-Heterocyclic Carbenes"
No full textAll primary data files related to the publication. Procedures, recation conditions and used analytical equipment is discussed in detail in the experimental section or the supporting information of the paper. Novel complexes were examined via nuclear magnetic resonance (NMR) spectroscopy and the spectra can be found in NMR folder. The NMR folder also contains NMR experiments mentioned in the paper and shown in the Supplementary Information. NMR Spectra are named according to the numbering in the publication. Crystal data (see related datasets) is deposited in the Cambridge Structural Database (CSD) of the Cambridge Crystallographic Data Centre (CCDC
Data for: Capturing local details in fluid-flow simulations: options, challenges and applications using marker-and-cell schemes
No full textThis dataset contains the data in the journal paper "Capturing local details in fluid-flow simulations: options, challenges and applications using marker-and-cell schemes" of Melanie Lipp, Martin Schneider and Rainer Helmig.
Particularly,
Fig. 5a plots the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithConserv.csv`
Fig. 5b plots the absolute values of column 6 of `datafiles/localTruncErrExplanation_MomX_CV_D_StandStenc_NoInterp.csv`
Fig. 6a plots the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_10x10Grid.csv`
Fig. 6b plots the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_13x20Grid.csv`
Fig. 6c plots the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_20x20Grid.csv`
Fig. 7 plots the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_StandStenc_NoInterp.csv` minus the absolute values of column 6 of `datafiles/localTruncErrExplanation_MomX_CV_D_StandStenc_NoInterp.csv`
Fig. 8a plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_MyStenc_NoInterp.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_StandStenc_NoInterp.csv`
Fig. 8b plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_MyStenc_NoInterp.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_StandStenc_NoInterp.csv` multiplied by minus one
Fig. 9a plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithoutConserv.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_MyStenc_NoInterp.csv`
Fig. 9b plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithoutConserv.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_CV_D_MyStenc_NoInterp.csv` multiplied by minus one
Fig. 10a plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithConserv.csv` minus the absolute values of column 5 of `datafiles/
Fig. 10b plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithConserv.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_WithoutConserv.csv` multiplied by minus one
Fig. 11a plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD.csv` multiplied by minus one
the Fig.-11a caption uses datafiles/Fig11aL1norm.tex
Fig. 11b plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD.csv`
the Fig.-11b caption uses datafiles/Fig11bL1norm.tex
Fig. 12a plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_0307.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_0307.csv` multiplied by minus one
the Fig.-12a caption uses datafiles/Fig12aL1norm.tex
Fig. 12b plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_0307.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_0307.csv`
the Fig.-12b caption uses datafiles/Fig12bL1norm.tex
Fig. 13a plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_rightFine.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_rightFine.csv` multiplied by minus one
the Fig.-13a caption uses datafiles/Fig13aL1norm.tex
Fig. 13b plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_rightFine.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_rightFine.csv`
the Fig.-13b caption uses datafiles/Fig13bL1norm.tex
Fig. 14a plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_100x100.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_100x100.csv` multiplied by minus one
the Fig.-14a caption uses datafiles/Fig14aL1norm.tex
Fig. 14b plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_100x100.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_100x100.csv`
the Fig.-14b caption uses datafiles/Fig14bL1norm.tex
Fig. 15a plots the negative results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_gauss.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_gauss.csv` multiplied by minus one
the Fig.-15a caption uses datafiles/Fig15aL1norm.tex
Fig. 15b plots the positive results of the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projCoarseOnCVOptionD_WithConserv_gauss.csv` minus the absolute values of column 5 of `datafiles/localTruncErrExplanation_MomX_projectionCoarseOnCVOptionD_gauss.csv`
the Fig.-15b caption uses datafiles/Fig15bL1norm.tex
Fig. 16 plots the absolute values of the difference of column 5 of `datafiles/solErr_u.csv` minus column 5 of `datafiles/solErr_uExact.csv`
Fig. 17a plots the negative results of the absolute values of the difference of column 5 of `datafiles/solErr_donea_U_10x10_projCVOptDOnCoarse_0406refined.csv` minus column 5 of `datafiles/solErr_donea_Uexact_10x10_projCVOptDOnCoarse_0406refined.csv` minus the absolute values of the difference of column 5 of `datafiles/solErr_donea_U_10x10_projCoarseOnCVOptD_0406unrefined.csv` minus column 5 of `datafiles/solErr_donea_Uexact_10x10_projCoarseOnCVOptD_0406unrefined.csv` multiplied by minus one
the Fig.-17a caption uses `datafiles/Fig17aL1norm.tex`
Fig. 17b plots the positive results of the absolute values of the difference of column 5 of `datafiles/solErr_donea_U_10x10_projCVOptDOnCoarse_0406refined.csv` minus column 5 of `datafiles/solErr_donea_Uexact_10x10_projCVOptDOnCoarse_0406refined.csv` minus the absolute values of the difference of column 5 of `datafiles/solErr_donea_U_10x10_projCoarseOnCVOptD_0406unrefined.csv` minus column 5 of `datafiles/solErr_donea_Uexact_10x10_projCoarseOnCVOptD_0406unrefined.csv`
the Fig.-17b caption uses `datafiles/Fig17bL1norm.tex`
the additional note at the very end of Chaption 3.2 uses `100x100_decrease_L1norm.tex` and `100x100_increase_L1norm.tex`
Fig. 18 uses `datafiles/pnm_unrefined_velocities_stokes.csv` and `datafiles/pnm_unrefined_velocities_pnm.csv` for the plotted data (square roots of the sums of column 5 squared and column 6 squared) and `datafiles/pnm_unrefined_pressures.csv` and `gridPlotfileB='datafiles/pnm_pore_grid.csv` for the plotted grids
Fig. 19 uses `datafiles/pnm_refined_velocities_stokes.csv` and `datafiles/pnm_refined_velocities_pnm.csv` for the plotted data (square roots of the sums of column 5 squared and column 6 squared) and `datafiles/pnm_refined_pressures.csv` and `gridPlotfileB='datafiles/pnm_pore_grid.csv` for the plotted grids
Fig. 20 uses `datafiles/localTruncErrExplanation_Conti_proj.csv`
Tab. 1 uses `Tab1.tex`
Tab. 2 uses `Tab2.tex`
Tab. 3 uses `Tab3.tex`</li