61 to 70 of 381 Results
Benz, Michael, 2024, "Data for Hydrogen Spillover through Hydride Transfer: The Reaction of ZnO and ZrO2 with Strong Hydride Donors", https://doi.org/10.18419/DARUS-4263, DaRUS, V1, UNF:6:/prwO0mYU07zXPJebhlu5A== [fileUNF]
The data set contains TEM images, XAS data, EPR data, MAS NMR data and NMR data of zinc oxide and zirconium oxide samples before and after treatment with molecular metal hydrides. |
Nov 22, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao, 2024, "SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)", https://doi.org/10.18419/DARUS-3884, DaRUS, V2
Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a q... |
Nov 22, 2024 - Estes Group
Maier, Sarah Eleonore, 2024, "Data for Comparison of the Catalytic Activity of Surface-Immobilized Copper Complexes with Phosphonate Anchoring Groups for Atom-Transfer Radical Cyclizations and Additions", https://doi.org/10.18419/DARUS-3770, DaRUS, V1
This file contains the NMR- and UV/Vis data relevant to the publication given in the title. This data was produced in the course of our work on alumina immobilized Cu catalysts for the ATRA and ATRC reactions of halogenated organic substrates and gives proof of the reaction products and the catalyst structures both on the surface and in homogeneous... |
Nov 20, 2024PN 6
This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri... |
Nov 20, 2024 - SFB 1333 A3 - Lotsch group, MPI-FKF
Endo, Kenichi; Canossa, Stefano; Heck, Fabian; Proserpio, Davide M.; Istek, Muhammed Satukbugra; Stemmler, Friedrich; van Slageren, Joris; Hartmann, Sean; Hartschuh, Achim; Lotsch, Bettina V., 2024, "Replication Data for: "Crystalline porous frameworks based on double extension of metal-organic and covalent organic linkages"", https://doi.org/10.18419/DARUS-4566, DaRUS, V1, UNF:6:3yz8QuVP+bonrBlRSFRwJw== [fileUNF]
All primary data files of the journal article mentioned can be found here. The data is structured by materials. The characterization data of each material are located in the subfolders "MOCOF-1", "MOCOF-1-R2", "MOCOF-2", "MOCOF-3", "COF-366-Co", "[Co(tapp)]nXn", "Co(tdpp)", "Co(tapp)", "Co(tdpp)(pba)2(OTf)", and "Others". The characterization data... |
Nov 19, 2024 - Institute for Reactive Flows (IRST)
Iaroslavtceva, Nadezhda, 2024, "Replication Data for: Iaroslavtceva et al. "A consistent MMC-LES approach for turbulent premixed flames" (2024) Proc. Combust. Inst.", https://doi.org/10.18419/DARUS-4570, DaRUS, V1
A Consistent MMC-LES Approach for Turbulent Premixed Flames This data set contains case setups to reproduce the simulation results presented in Iaroslavtceva et al. (2024) Proceedings of the Combustion Institute. In this publication, a set of freely propagating turbulent premixed flames with varying equivalence ratios is used to test the performanc... |
Nov 19, 2024PN 2
SimTech Project PN 2-8 Siebert "Modelling of architectural-informed and activation-driven contractions of the human M. tibialis anterior" |
Nov 19, 2024 - Publication: A novel geometry-informed drag term formulation for pseudo-3D Stokes simulations with varying apertures
Krach, David; Weinhardt, Felix; Wang, Mingfeng; Schneider, Martin; Class, Holger; Steeb, Holger, 2024, "Results for pseudo-3D Stokes simulations with a geometry-informed drag term formulation for porous media with varying apertures", https://doi.org/10.18419/DARUS-4347, DaRUS, V1
Content: This data set includes all the necessary input data to fully replicate the benchmarks and applications in Krach et al.(2024). In addition, the results for all solvers are provided as raw simulation output as well as in condense form. Complete datasets: 01_CYLINDER, 02_SINGLE_PRECIPITATE, 03_SEGMENT1, 04_SEGMENT2, 05_SEGMENT3 Each data set... |
Steinle, Lukas, 2024, "Replication Data for: Automatic Preload Adaptation for Rack-and-Pinion Drives to Maximize Performance and Energy Efficiency", https://doi.org/10.18419/DARUS-4224, DaRUS, V1
This dataset contains all experimental data that is shown and referenced within the paper "Automatic Preload Adaptation for Rack-and-Pinion Drives to Maximize Performance and Energy Efficiency". Electrically preloaded rack-and-pinion drives are typically utilized in machine tools to precisely move heavy loads over long travel distances. In the stat... |
