All primary data files of measurements and processed data of the journal article "Comparative study of lattice parameter and pore size of ordered mesoporous silica materials using physisorption, SAXS measurements and transmission electron microscopy", are deposited. File types which are not easily readable have been converted to other formats, i.e. TIF and TXT, and have been deposited additionally. PDH files may be opened with the same applications as TXT files. The dataset contains the following data: nitrogen physisorption measurements, small-angle X-ray scattering curves, transmission electron micrographs. The data is named according to the sample name. A short description of the samples is given in the following: OMS_TLCT: Ordered mesoporous silica material synthesized via true liquid crystal templating with hexadecylethyldimethylammonium chloride as surfactant MCM-41: Ordered mesoporous silica material synthesized via a cooperative self-assembly process with hexadecyltrimethylammonium chloride as surfactant SBA-15: Ordered mesoporous silica material synthesized via a cooperative self-assembly process with the poloxamer P123 as surfactant SBA-15_sa: Ordered mesoporous silica material synthesized via a cooperative self-assembly process with the poloxamer P123 as surfactant and n-decane as swelling agent

Instructions for the first steps with DaRUS

This repository contains the stochastic finite element model developed to study the stiffness and strength variation of glulam beams. The S3GluM model was developed during the PhD thesis of Cristóbal Tapia Camú.

This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The samples were dried in a drying oven for 10 days prior to the absorption measurements. The determination of the water absorption capacity is carried out at two different temperatures (30 °C and 60 °C). The samples are immersed in a demineralized water bath, which is stored in a drying oven. The time-dependent diffusive process is carried out with weight gain measurements. At each measuring point, the sample is taken out of the water bath and the surface water is wiped off with a lint-free cloth. Attention was paid to keep the measuring time (t < 30 sec) at each measuring point as short as possible in order not to influence the diffusive process. The measurement interval was set at the beginning of the measurement at 30 °C to 60 min and at 60 °C to 10 min. It was measured at daytime and working hours. After the samples have been completely saturated, the desorption experiment starts. The saturated samples were stored in a drying oven at 30 °C and the weight loss was measured. The measurement parameters correspond to the absorption measurements at 30 °C.

The Dumux source code is provided in a docker container, which compiles and produces an executable, which models heat transport and water flow from the atmosphere to the subsurface. The needed boundary and initial conditions for four locations, modeled in our paper "Vadose Zone Journal Submission VZJ-2023-06-0046-OA" are provided and can be calculated.

Videos showing water molecules at a sodium chloride (NaCl) solid surface for different water content. The force field for the water is TIP4P/epsilon (https://doi.org/10.1021/jp410865y), and the force field for the ions is from Loche et al. (https://doi.org/10.1021/acs.jpcb.1c05303). The trajectories have been generated using the GROMACS simulation package, and the videos have been created using VMD.

For more information, such as installation, requirements and user guide, please see the demoa-manual.pdf The developement of this package was supported by “Deutsche Forschungsgemeinschaft” (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016.

In this repository the data files of OncoFEM are collected. These are in the following listed and cited where appropriate: SRI24 atlas T1 and T2 MRI modalities (T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, “The SRI24 multichannel atlas of normal adult human brain structure,” Human Brain Mapping, vol. 31, no. 5, pp. 798-819, 2010. doi: 10.1002/hbm.20906) Tutorial files: Because of long calculation times particular interim results are provided generated geometry (geometry.xdmf + geometry.h5) edema mapping (edema.xdmf + edema.h5) cerebrospinal fluid distribution file (csf.nii.gz) white matter distribution (wm.nii.gz) grey matter distribution (gm.nii.gz) Segmented tumor distribution, class 0 (tumor_class_pve_0.nii.gz) Segmented tumor distribution, class 1 (tumor_class_pve_1.nii.gz) Segmented tumor distribution, class 2 (tumor_class_pve_2.nii.gz) Folder of magnetic resonance images of first Authors head in DICOM format (T1 and Flair modality) First six datasets (T1, T1ce, T2, Flair, tumour segmentation) of BraTS 2020 collection (https://www.med.upenn.edu/cbica/brats2020/data.html) Five different weights of tumor segmentations with different input channels. Herein, either one of T1, T1ce, T2, Flair or all of them are used. Additionally, an empty image is included for an adaptive training. The data corresponds to OncoFEM version 1.0. The up-to-date Version of oncofem can be obtained from github or Version 1.0 from DaRUS, which comes in a pre-installed virtual box. For usage, download and unzip file next to the oncofem folder or adjust the paths stored in the config.ini file if the files need to be somewhere else.

This dataset contains a collection of data on almost 540 stakeholders in multi-storey timber construction. The list includes architects, engineers, and manufacturers involved in the design and construction of 300 contemporary multi-storey timber projects built between 2000 and 2021. The data consists of quantitative data including stakeholder name, type, location, website, services offered, projects the stakeholders were involved in, and reason for disqualification, in necessary. It also contains the categories the offered services belong to, and the perceived level of timber expertise of the stakeholders.

These measurements are taken in the subsurface at the pilot site next to the weather station of the University of Stuttgart and used to calibrate and validate our pde-based model. The subsurface has been instrumented with 64 temperature sensors, 8 soil moisture sensors. There are four locations, having different soil and soil cover layers. Soil moisture is measured at 60 cm and 100 cm depth, Temperature at 30, 60, 75, 100 cm. At drinking water pipe location, there are two sensors. Column description is to be found in a readme.txt file

The videos provided show experimental results of the cooperative object transportation using mobile robots with on-board force control. In particular, the mobile robots shall transport four different polygonal, but non-convex, objects along predefined paths. The distributed formation synthesis explicitly takes the robots' maximum pushing force into account such that a closed-set manipulation space in terms of a zonotope follows. This zonotopic manipulation space, following from the Minkoswki sum of the individual manipulation capabilities of the robots, is visualized in the fifth video (Visualization_Fig4a.mp4) using data from the corresponding hardware experiment (Rectangle_N4_square.mp4). Novelly, a lightweight quadratic program runs on each robot and determines in a decentralized manner the desirable individual pushing forces suitable to transport the object. These pushing forces are then governed by means of hybrid position-force controllers running at 100 Hz. As for formation finding, no central entity is used for control purposes. The tasks are accomplished in a purely distributed manner using inter-robot communication. For each robot, the pushing force is measured using a self-designed force-sensing unit mounted on board of each mobile robot. In the videos, the direction of the uniaxial and unilateral force sensor is indicated by white rectangles. Moreover, the measured force is visualized by red rectangles superimposed onto the white ones.

We report an experiment (N=18) where participants where engaged in a dual task setting in a Social VR (Virtual Reality) scenario. We present a physiologically-adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of not-playable characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal the task complexity. Specifically we make available physiological (Electrodermal Activity - EDA, Electroencephalography - EEG; Electrocardiography - ECG) , behavioral and questionnaires data and lastly, the analysis code.

The commercial videos are presenting visions of future Augmented reality applications. We analyzed 30 YouTube videos featuring AR devices in industrial manufacturing and construction. We offer the excel sheet including the list of the 30 commercial YouTube videos with information on year/title/duration/initiator/type of initiator/link/keywords in search/field of AR application/short description/notes on each of the respective videos.

This dataset contains raw interview data (transcripts) from a seminar on study success requirements ("Lernerfolgsbedingungen im Hochschulstudium", Universität Stuttgart) in winter 2022/23. Students are narrating their experiences with a wide range of topics regarding their learning and study conditions, and their impact on study success.The interviews are best characterized as problem-centered interviews with strong narrative elements.

This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This dataset is intended to progress the scientific ML research area. In general, the data are stored in HDF5 format, with the array dimensions packed according to the convention [b,t,x1,...,xd,v], where b is the batch size (i.e. number of samples), t is the time dimension, x1,...,xd are the spatial dimensions, and v is the number of channels (i.e. number of variables of interest). More detailed information are also provided in our Github repository (https://github.com/pdebench/PDEBench) and our submitting paper to NeurIPS 2022 Benchmark track.

Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning algorithm accordingly. Industrial transfer learning offers to lower the effort of such adaptation by allowing the utilization of previously acquired knowledge in solving new (variants of) tasks. Being data-driven methods, the development of industrial transfer learning algorithms naturally requires appropriate datasets for training. However, open-source datasets suitable for transfer learning training, i.e. spanning different assets, processes and data (variants), are rare. With the Stuttgart Open Relay Degradation Dataset (SOReDD) we want to offer such a dataset. It provides data on the degradation of different electromechanical relays under different operating conditions, allowing for a large number of different transfer scenarios. Although such relays themselves are usually inexpensive standard components, their failure often leads to the failure of the machine as a whole due to their role as the central power switching element of a machine. The main cost factor in the event of a relay defect is therefore not the relay itself, but the reduced machine availability. It is therefore desirable to predict relay degradation as accurately as possible for specific applications in order to be able to replace relays in good time and avoid unplanned machine downtimes. Nevertheless, data-driven failure prediction for electromechanical relays faces the challenge that relay degradation behavior is highly dependent on the operating conditions, the high-resolution measurement data on relay degradation behavior is only collected in rare cases, and these can then only cover a fraction of the possible operating environments. Relays are thus representative of many other central standard components in automation technology. For more information (especially on the specific structure and contents of this dataset), please see "2020 Stuttgart Open Relay Degradation Dataset (SOReDD).pdf".

Research Data Management (RDM) describes the collection, preservation, and sharing of data created or used in a research project. SimTech’s Data and Software Management team offers expertise and resources to develop and implement sustainable RDM in your SimTech project (for free). The following form serves to assess the needed support. If you have any questions about your project idea or about this form, contact us at rdm@simtech.uni-stuttgart.de.

The results of EICP (Enzyme Induced Calcite Precipitation) experiments. This dataset includes pressure measurements and 3D reconstructed X-ray images during the conducted experiments. The experiment was performed on two Borosillicates Glass beads Columns (BGC). The images were taken with so called "Low-dose" strategy by minimizing the exposure time and projection acquisition in order to boost data acquisition time (6 min / dataset). Later, the low quality of acquired images affected by adopted strategy was enhanced by ML algorithm. The codes which were used for the post-processing in order to enhance the image quality can be found at https://doi.org/10.18419/darus-2991. The image data was recorded at the time steps 1, 2, 4, 6, 8, 10 and 12 hours of EICP experiment.

This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. In addition the dataset contains the pre-trained model for the 1D Inverse problem for FNO and U-Net. These models are stored using the same structure as the dataset they trained on. All the files are saved in .pt files, the default file type for the PyTorch library. More detailed information are also provided in our Github repository (https://github.com/pdebench/PDEBench) and our submitting paper to NeurIPS 2022 Benchmark track.