DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data
No full textThis dataset contains the codes for the invasion percolation models and comparison method used in the manuscript: A framework for objectively comparing competing invasion percolation models based on highly-resolved image dat
Replication Data for: Rethinking Asset Administration Shell Communication Types
No full textThis dataset belongs to the publication "Rethinking Asset Administration Shell Communication Types: A Systematic Mapping Study and Portfolio-Based Classification." (DOI 10.1007/s11740-025-01378-3). A detailed description of the setup can be found in the publication.
This study aims to determine if definitions exist for the different types of AAS and if they are used consistently. Additionally, it should be clarified whether the definitions are based on the same source.
Due to our goal of identifying the definitions of AAS types used in the literature, as well as their usage and sources, we did not further restrict our search terms. Consequently, we searched the selected databases using the keywords "(Asset Administration Shell OR Asset Administration Shells)" AND "(Type OR Types)". The databases chosen for this study are IEEE Xplore, SpringerLink, and ScienceDirect. All publications were accessed on August 12th, 2024.
To reduce the corpus and ensure the study's reproducibility, we applied specific exclusion criteria.
Inclusion criteria:
- Electronically accessible studies
- Studies available in English
- Peer-reviewed studies
- Full papers with 4 or more pages
Exclusion criteria:
- Electronically inaccessible studies
- Studies unavailable in English
- Studies from sources without systematic peer review processes, such as books, magazines, and websites
- Short papers of less than 4 pages
- Papers which do not define or describe at least two different AAStypes
Through our search, we obtained 538 publications. After excluding non-peer reviewed publications, 347 documents were left in the corpus, which comprise the replication package. In the next step, we applied the remaining exclusion criteria, leaving 340 potentially relevant papers to read. Before the literature was distributed, the first 3 papers were evaluated together. For this purpose, these 3 papers were divided among the first five authors, who read and evaluated them individually. The results were discussed jointly to verify that we have the same understanding of the AAS types. Then, we evenly distributed the remaining 337 publications between the first five authors and synchronized about questions and conflicts in weekly sprint meetings. The publications were read and analyzed regarding definitions of AAS types. As a result, 32 of the studies were classified as relevant as they contained type definitions of at least two distinct types of AAS. Publications that distinguish between AAS, but are clearly grouped in a binary system that cannot be translated into the ternary system of AAS types, were not included in the analysis and therefore this dataset, e.g. sources that distinguish between an active and a passive part of a single AAS
Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB, a master virulence regulator in pathogenic Leptospira"
No full textInitial coordinate and simulation input files and a coordinate files of the final outputs as well as of the simulation trajectories (protein only) for all-atom MD simulations of LvrB performed using CHARMM36m/TIP4p in GROMACS2023. (pdb, xtc)
Simulation data on phosphorylated aspartic acid (residue APP) with parameters generated by CHARMM-GUI are included
MultiCamAssembly: Distributed Robotic Vision System Software Framework
No full textMultiCamAssembly
MultiCamAssembly is a distributed software framework for multi-camera perception and coordination in automated crane-based timber construction.
It orchestrates multiple, distributed assembly robots (cambots) with embedded vision that detect geometric features in prefabricated timber elements and transmit observations to a central coordinator for multi-view pose estimation.
The fused pose estimate enables vision-guided crane motion during on-site assembly.
Structure
base/ — UDP server, device management, plotting tools, CLI helpers
cambots/ — camera utilities, YOLO integration, motor control, and programs
overlay_tracker/ — geometry, detections, tracking, rendering, and CLI
ssh/ — SSH config (e.g., lights-off profile)
Project Structure
For a full folder tree with short descriptions for every file, see STRUCTURE.md
Key Workflows
Main App Workflow
Power on remote robots and connect to the local network
Verify SSH config (e.g. ssh/config_lightsoff) matches the network
Run base/programs/App.py
Guided steps:
Device discovery and selection
Remote code updates
Component selection
Model Training Workflow
Create labeled data using Label Studio
Update dataset paths in yolo/yolo_train.yaml
Set hyperparameters and epochs in yolo/yolo_train.py
Train model with yolo/yolo_train.py
Copy trained weights to cambots/yolo/models/
Generate TensorRT engines on remote devices
Validate inference on remote devices
Pushing and Pulling Workflow
Push code via deploy.bat (Windows) or deploy.sh (Linux)
Command: ./deploy.sh lightsoff-j01 [config_file]
Pushes cambots/ without deleting remote-only files
Pull data via fetch.bat (Windows) or fetch.sh (Linux)
Command: ./fetch.sh lightsoff-j01 [-d]
Retrieves data from remote cambots/save/ to local save/
Camera Calibration Workflow
Required only if cameras change
Measure checkerboard cell size and update square_size
Run calibrate_camera.py on remote devices
Capture 20 checkerboard images
Copy camera_calibration_data.npz to each remote device
Logging Workflow
Enable logging in base/programs/App.py
UDP messages logged as JSONL files in logs/
Used for post-processing and algorithm prototyping
</ol
Publication data for: "Class-II-aldolase-mimicking polyfunctional Lewis acid/azolium-aryloxide catalysts in direct enantioselective nitro-aldol additions"
No full textThis dataset provides the underlying experimental and computational research data for the publication "Class-II-aldolase-mimicking polyfunctional Lewis acid/azolium-aryloxide catalysts in direct enantioselective nitro-aldol additions".
Data type and creation: The dataset comprises analytical and spectroscopic measurements (NMR, HRMS, IR, UV-Vis, CD, SQUID, EPR) as well as chromatographic data (HPLC) generated during the synthesis and evaluation of novel Cobalt(II)-based catalysts. Additionally, it contains theoretical data derived from density functional theory (DFT) calculations (using Turbomole) and microkinetic modeling (using Julia).
Data structure: The data is systematically organized into directories corresponding to the applied analytical and computational methods.
Data interpretation: NMR, IR, and mass spectrometry data confirm the structural identity and purity of the synthesized compounds. HPLC chromatograms allow for the quantification of the enantiomeric excesses of the catalytic nitro-aldol products. The spectroscopic, magnetic, and computational data collectively elucidate the electronic properties of the Co(II) complexes and support the proposed class-II-aldolase-mimicking reaction mechanism.
Reuse potential: This dataset can be reused by other researchers to reproduce the experimental and theoretical results, or to use the spectra and chromatograms as reference material for related compounds. Furthermore, the provided computational coordinates, energies, and kinetic parameters offer a valuable basis for future theoretical benchmarking, machine learning applications in chemistry, or the design of new polyfunctional catalytic systems.
Abstract:
The catalytic asymmetric nitroaldol reaction is a powerful tool for accessing chiral 1,2-difunctionalized motifs, and numerous catalytic systems have been reported. Despite considerable progress in achieving high levels of stereocontrol, key challenges persist. In particular, optimal selectivity often requires cryogenic conditions, resulting in prolonged reaction times and limited practicality. In this article, a novel concept for asymmetric nitroaldol additions is introduced through a polyfunctional catalyst. This system integrates a Lewis acid, Co(II), with an azolium–aryloxide betaine, and exhibits mechanistic features reminiscent of class-II aldolases. The proposed mode of action is supported by comprehensive DFT calculations, microkinetic simulations, and detailed spectroscopic analyses. By the unique synergistic interplay of the Lewis acidic metal center, the aryloxide as Brønsted base and the corresponding aromatic alcohol serving as both hydrogen bond donor and Brønsted acid, high stereoselectivity was accomplished even at slightly elevated temperature for MeNO2. Like in class-II-aldolases, the aldehyde is activated by H-bonding to the aromatic alcohol and not by the Lewis acid. The latter serves to stabilize and direct the nitronate. The computational studies further demonstrate that the catalyst‘s key functional groups precisely orchestrate all accompanying transformations. As a result of the mild reaction conditions, not necessitating the use of an external base, the method also proved to be applicable to readily enolizable aliphatic aldehydes, such as phenylacetaldehyde
Data for: Energy conservation and pressure relaxation in an extended two-temperature model for copper with an electron temperature-dependent interaction potential
No full textThis data set includes MD data that was used for the paper "Energy conservation and pressure relaxation in an extended two-temperature model for copper with an electron temperature-dependent interaction potential".
In each folder, the respective data as well as analysis and visualization scripts written in Python are given.
ANATE_200ps.zip:
MD data of a 200 ps simulation using an electron temperature-dependent potential and energy conservation
ANATE_5ps.zip:
MD data of a 5 ps simulation using an electron temperature-dependent potential and energy conservation
ANAT_200ps.zip:
MD data of a 200 ps simulation using an electron temperature-dependent potential
ANAT_5ps.zip:
MD data of a 5 ps simulation using an electron temperature-dependent potential
ANA_200ps.zip:
MD data of a 200 ps simulation using an electron temperature-independent potential
ANA_5ps.zip:
MD data of a 5 ps simulation using an electron temperature-independent potential
ANA_ELECPRESS_200ps.zip:
MD data of a 200 ps simulation using an electron temperature-independent potential with additional blast force
ANA_ELECPRESS_5ps.zip:
MD data of a 5 ps simulation using an electron temperature-independent potential with additional blast force
Blast_force.zip:
MD data for the demonstration of FD cell boundary treatment
EOS: Equation of state tables required for the simulations
FD_5: Source code (src) and three sets of simulations
ANART_5: Electron temperature-independent potential
ANART_5_elecpress: Electron temperature-independent potential with blast force
ANAT_5: Electron temperature-dependent potential
Energy_conservation.zip:
MD data for the energy conservation algorithm
EOS: Equation of state tables required for the simulations
Energy_liquid: Total energy of a completely disordered structure at a large range of densities and electron temperatures
dens_[density_counter], [density_counter] from 0 to 99
TC_[TC], electron temperature [TC] in eV
Potentials: Everything needed to create a tabulated potential at a specified electron temperature
TC_[TC], electron temperature [TC] in eV
config_[config_counter], [config_counter] from 0 to 10
potfit: Temporary folder for the potential creation using potfit
LOP.zip:
MD data and analysis scripts for the local order parameter.
plot_order_parameter.py: Post-processing script for the calculation of the local order parameter based on the 12 closest atoms
plot_order_parameter_IMD.py: Plot script of the local order parameter as calculated and written by IMD
plot_order_parameter_IMD_0.py: Plot script of the initial local order parameter
Parameters.zip:
Scripts and data for the equation of state tables used in all simulations
*.out: Collection of data of ideal Cu structures at various electron temperatures and densities
*.dat: Equation of state data by Lin et al. (2008)
*.py scripts that create the equation of states tables
plot_temperature_within_skin_depth_ANA_vs_ANATE.py:
Script for the electron/lattice temperature comparison of 5 ps simulations with an electron temperature-independent potentials and an electron temperature-dependent potential with energy conservation
Sample_preparation.zip:
MD data for the creation of a well-equlibrated sample
EOS: Equation of state tables required for the simulations
NVE_surface, NpT_bulk, NpT_surface: Various stages during the sample preparation
create_structures: Script used to create ideal structures
param_files: Parameter files for the various states of the sample preparation
Te.pdf:
Electron/lattice temperature comparison of 5 ps simulations with an electron temperature-independent potentials and an electron temperature-dependent potential with energy conservation
Te.png:
Electron/lattice temperature comparison of 5 ps simulations with an electron temperature-independent potentials and an electron temperature-dependent potential with energy conservation
</ul
MultiCamAssembly DataPackage: Experimental Crane Assembly Runs and YOLO Vision Training Data
No full textThis DataPackage contains experimental field data produced with the MultiCamAssembly: Multi-Scalar Robotic Vision System Software Framework. It includes JSON log files and multi-view video recordings from five full-scale crane-based timber assembly runs using the distributed assembly robots (“cambots”).
The package also provides the YOLO training data and trained models used on the assembly robots, including labeled images of prefabricated timber elements, training configurations, trained model weights, exported inference models, and Python scripts for evaluation
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
No full textGROMACS simulation files, input and final structures for CHARMM36m MD simulations including our refined parameters for phosphorylated serine in 3 different protonation states.
The directory charmm36-jul22mod.ff contains our refined parameters
Data for: Two-photon 3D-printed BSA hydrogel fibers resemble native muscle contraction dynamics
No full text(1) Bovine serum albumin (BSA) hydrogel spheres (diameters 10-50µm) were imaged at pH 7 and pH 4 to identify evidence of anisotropic shrinkage behavior. 2D image stacks were acquired using multiphoton fluorescence microscopy.
The corresponding image files were named:
BSA_sphere_[10-50]um_pH7.tif
BSA_sphere_[10-50]um_pH4.tif
Spheres were printed from respective STL files:
sphere_[10-50]um.STL
(2) Force-length-relationship data from isometric activations of BSA fibers, stimulated by immersion in a pH 4 buffer solution, are provided for all 12 samples in 'force-length-relationship_data.xlsx'.
(3) Work loop data from stretch-shortening cycles of BSA fibers, immersed in either pH 7 (non-activated) or pH 4 (activated) buffer solution, are provided for all 7 samples in 'work-loop_data.xlsx'
Reddit Social Group Mentions (Reddit-SGM)
No full textDataset 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: Analysis of social group mentions within political discussions on Reddit
Key Fields:
body_cleaned_id: A unique identifier for each comment, originally sourced from the Reddit Politosphere Dataset
segment_id: A unique identifier for annotated mentions within a comment
subreddit: Name of the subreddit where the comment was posted
year: Year of the comment's publication
body_cleaned: Cleaned text content of the comment
Annotation Fields: Information related to the annotations provided by annotators
annotations1, 2, 3: Annotations provided by annotators 1, 2, and 3 for the body_cleaned field
vote_segments: Segments (Mentions) annotated by all annotators, segments are separated by commas, and the number after --- represents the index of the segment's starting position in the comment
vote_counts: Number of votes received for each segment (mention), votes are listed in the same order as the corresponding segments (mentions) in vote_segments, separated by commas
segment: Mention of a social group within the comment
count: Total vote count for each segment, the number after --- indicates the starting index of the segment in the comment
Additional Fields for describing label variation: Information capturing label variation in the annotations provided by annotators
disagreements: Indicator of whether annotators disagreed on the annotation
reason_disagreement: Category of disagreement, from the following categories:
Referential ambiguity (RA)
Metonymy (M)
Adjective & Description (A) Note: this category is denoted as A&D in the paper, it is represented without an ampersand in the dataset to avoid issues.
Determiner (D)
Plural Noun (P)
Individual (I)
Annotation Error (AE)
type_socialgroup: Social group category, which can be:
Intimate Group (IG)
Organized Group (OG)
Aggregate Group (AG)
segment_belongs_to: Indicates if the segment is part of a broader segment
</ul