The Research Data Management Plan (RDMP) of the priority program SPP 2170 is the formal document that should help to mangage the handling of data. Since enormous amounts of research data (Big Data) will be generated, the exchange and access to the data should be ensured. Every experiment in the laboratory, or every simulation generates huge amounts of unstructured data. To make these findable, accessible, interoperable, and reusable (FAIR), discipline-specific criteria must be defined in addition to the hardware and software that form the general platform. Therefore the RDMP of the DFG-funded priority program SPP2170 describes how this information could be processed in the future.

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.

The dataset covers a gigamapping of game design and execution of the game GoCOLife that introduces a more-than-human perspective to the players. The data were produced as part of a studio course 'COLife: More-than-Human Perspective to CoDesign' in the summer semester 2024.

Polar measurements of the DP-FR-1924-MODLWT airfoil. Data recorded at the Laminar Wind Tunnel, University of Stuttgart. For details on the measurements, visit https://www.iag.uni-stuttgart.de/en/institute/test-facilities/laminar-wind-tunnel/

This repository contains the object model data sets of the case study specimens from the related publication: Gil Pérez, M., Zechmeister, C., Kannenberg, F., Mindermann, P., Balangé, L., Guo, Y., Hügle, S., Gienger, A., Forster, D., Bischoff, M., Tarín, C., Middendorf, P., Schwieger, V., Gresser, G. T., Menges, A., Knippers, J.: 2022, Computational co-design framework for coreless wound fibre-polymer composite structures. Journal of Computational Design and Engineering, 9(2), pp. 310-329. (doi: 10.1093/jcde/qwab081) Abstract: In coreless filament winding, resin-impregnated fibre filaments are wound around anchor points without an additional mould. The final geometry of the produced part results from the interaction of fibres in space and is initially undetermined. Therefore, the success of large-scale coreless wound fibre composite structures for architectural applications relies on the reciprocal collaboration of simulation, fabrication, quality evaluation, and data integration domains. The correlation of data from those domains enables the optimization of the design towards ideal performance and material efficiency. This paper elaborates on a computational co-design framework to enable new modes of collaboration for coreless wound fibre–polymer composite structures. It introduces the use of a shared object model acting as a central data repository that facilitates interdisciplinary data exchange and the investigation of correlations between domains. The application of the developed computational co-design framework is demonstrated in a case study in which the data are successfully mapped, linked, and analysed across the different fields of expertise. The results showcase the framework’s potential to gain a deeper understanding of large-scale coreless wound filament structures and their fabrication and geometrical implications for design optimization. The data set contains data from three sets of ten coreless filament wound fiber-polymer composite testing specimens that were digitally simulated, robotically manufactured, laser scanned and mechanically tested. The data contains contributions from different domains: simulation, fabrication, quality evaluation, and data integration. The data was stored via an open-source object model (BHoM) that was extended for its use in coreless filament winding. "The BHoM (Buildings and Habitats object Model) is a collaborative computational development project for the built environment. BHoM aims to standardise the data and functionality that AEC domain experts use to design across all disciplines." (Source: https://bhom.xyz/documentation/) The files are named by specimen type and ID (SX-Y.json) - X refers to the specimen type and Y refers to the specimen ID.

This dataset features both data and code related to the research article titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes raw data packaged in tarball format, including Python scripts used to derive the results presented in the publication. High-resolution raw data for contour plots is available upon request. 1 Download the Dataset: Download the dataset file using Access Dataset. Ensure you have sufficient disk space available for storing and processing the dataset. 2 Extract the Dataset: Once the dataset file is downloaded, extract its contents. The dataset is compressed in a tar.xz format. Use appropriate tools to extract it. For example, in Linux, you can use the following command: tar -xf Publication_CCS.tar.xz tar -xf Publication_Karst.tar.xz tar -xf Validation_Sim.tar.xz This will create a directory containing the dataset files. 3 Install Required Python Packages: Before running any code, ensure you have the necessary Python (version 3.10 tested) packages installed. The required packages and their versions are listed in the requirements.txt file. You can install the required packages using pip: pip install -r requirements.txt 4 Run the Post Processing Script: After extracting the dataset and installing the required Python packages, you can run the provided post processing script. The post processing script (post_process.py) is designed to replicate all the plots from a publication based on the dataset. Execute the script using Python: python3 post_process.py This script will generate the plots and output them to the specified directory. 5 Explore and Analyze: Once the script has completed running, you can explore the generated plots to gain insights from the dataset. Feel free to modify the script or use the dataset in your own analysis and experiments. High-resolution data, such as the vtu's for contour plots is available upon request; please feel free to reach out if needed. 6 Small Grid Study: There is a tarball for the data that was generated to study the grid used in the related publication. tar -xf Publication_CCS.tar.xz If you unpack the tarball and have the requirements from above installed, you can use the python script to generate the plots. 7 Citation: If you use this dataset in your research or publication, please cite the original source appropriately to give credit to the authors and contributors.

Solving the energy balance at the atmosphere-subsurface interface drives heat input (in the summer) into the subsurface. We use this subsequently to calculate heat transport and water flow into the subsurface and then to calculate temperature s around drinking-water supply pipes. This data is from the weather station of the University of Stuttgart. We are providing the measured Boundary Conditions, needed to compute the interface boundary conditions: long wave radiation incoming short wave radiation incoming air temperature in 2 m above ground wind velocity in 2 m above ground relative humidity in 2 m above ground precipitation intensity Data is given tabulated, a readme-file explains the column names.

Instructions for the first steps with DaRUS

The dataset offers sequences that are optimized for nonlinearity estimation. The sequences are compliant to IEEE 802.11 standards and are given as binary phase shift keying (BPSK) modulated orthogonal frequency division multiplexing (OFDM) symbol in frequency domain. The sequences are given for various numbers of total subcarriers and positions of occupied subcarriers that match to the training fields defined in the IEEE 802.11 standards. All sequences are normalized to unit power and comply to a maximum peak-to-average-power (PAPR) constraint of 13.05 dB. For each sequence format, one CSV file is given. The columns hold optimized sequences for nonlinearity estimation with orthonormal Laguerre polynomials for each estimation order from P=3 up to P=10.

This dataset contains the results of a database search to obtain research articles related to collective robotic construction (CRC). The database search criteria can be found in the related publication: Leder, S., Menges, A.: 2023, Architectural design in collective robotic construction. Automation in Construction, Vol. 156, p. 105082. (DOI: 10.1016/j.autcon.2023.105082). The found research articles are sorted by their relevance to the research topic of CRC and then if applicable, are categorized by the three dimension discussed in the paper, (1) design description, (2) goal specification, and (3) execution. The question that define the categorizaton at each dimension are as follows: Is the architectural design known before construction? How is architectural design communicated to the robots? When is the robotic planning of the architectural design executed?

This dataset consists of software code associated with the publication titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes a Dockerimage that contains the precompiled code for immediate use. For transparency, the Dockerfile is also provided. 1 Download the Dataset: Download the compressed Dockerimage wrr_image.tar directly. If you want to inspect the Dockerimage, you can have a look at the associated Dockerfile first. Inside the Dockerfile one will find an instance of git which is privately hosted and not guaranteed to be hosted forever. Source code can also be inspected inside the docker container. 2 Load Docker Image: Load the Docker image from the provided tar.xz file. docker load --input wrr_image.tar 3 Run Docker Container: Run the Docker container with appropriate volume mounts. docker run -v $(pwd)/share/:/home/wrr_user/code/simulations/run/customBoussinesq/share -it wrr_image It might be that the image is called slightly differently for instance wrr_image:latest , one needs to check the terminal output. This command mounts the share directory from your current host directory into the container's /home/wrr_user/code/simulations/run/customBoussinesq/share directory. This allows you to move simulation results outside of the container by moving the results into the share folder. 4 Running Computations: The container is precompiled with necessary resources. You can either submit bash scripts to the cluster scheduler or use the Allrun scripts inside the cases. Move to the desired case and type ./Allrun 4 to run with 4 cores. Note that the computations are resource-intensive and may not work on a local machine, even with an appropriate number of cores set.

Fatalities with semi-automated vehicles typically occur when users are engaged in non-driving related tasks (NDRTs) that compromise their situational awareness (SA). This work developed a tactile display for on-body notification to support situational awareness, thus enabling users to recognize vehicle automation failures and intervene if necessary. We investigated whether such tactile notifications support "event detection'' (SA-L1) or 2 "anticipation'" (SA-L3). Using a simulated automated driving scenario, a between-groups study contrasted SA-L1 and SA-L3 tactile notifications that respectively displayed the spatial positions of surrounding traffic or future projection of the automated vehicle's position. Our participants were engaged in an NDRT, i.e., an Operation Span Task that engaged visual working memory (WM) resources. They were instructed to intervene if the tactile display contradicted the driving scenario, thus indicating vehicle sensing failures. On a single critical trial, we introduced a failure that could have resulted in a vehicle collision. SA-L1 tactile displays of potential collision targets resulted in less subjective workload on the NDRT than SA-L3, which indicated the vehicle's future actions. These findings and qualitative questionnaire suggest that the simplicity of SA-L1 display required less mental resources, which allowed participants to better interpret sensing failures in vehicle automation. We make available data on intervention performance (distance, Maximum intensity, Time to Collision), WM performance (Attention and WM interference), qualitative questionnaire (NASA-TLX and SART), together with subjective questions from the semistructured interview and Unity VR environment.

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 video shows a synchrotron x-ray video of full-penetration laser welding of the aluminum alloy AA1050A (Al99.5). At the beginning of the video the transition from partial penetration welding with a keyhole which is closed at its bottom to full-penetration welding with a keyhole which is opened at its bottom can be observed. The images show that the fluctuations of the capillary’s geometry during the beginning of the process result in an excessive formation of pores. During the further progress of the process a reliable full-penetration process is achieved with an increased stability of the geometry of the keyhole. A detailed analysis of this transition and its implications on the absorptance are presented in the related publication.

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.

The participating universities in SFB/Transregio 161 acknowledge the general importance of re-search data management as a vital issue for all of their work and provide increasing central sup-port for long-term accessibility and reusability of data, documentation of methods and tools and privacy protection. However, technical and organisational offerings for data management can only be effective as far as researchers are aware of them and can make informed decisions on which solution is the right one for which data, which in turn requires an overall knowledge of the types and amounts of data collected or produced in the project. Furthermore, ensuring long-term availability of data for reproducibility of research results, which is one of the key ideas be-hind the research efforts of SFB/Transregio 161 and even more important in the final funding period of the project, requires planful actions as serving data beyond the lifetime of a project involves allocation of technical and organisation resources. This data management plan (DMP) identifies the research data collected or produced in SFB/Transregio 161, assesses their value for the goals of reusability and reproducibility and states the timeframes in which SFB/Transregio 161 and/or the participating universities guarantee their future availability. It furthermore describes the technical and organisational means for storing and publishing research data which the researchers of SFB/Transregio 161 can make use of. Such means might be provided by the project and funded by DFG or by the universities. The DMP states minimum requirements for metadata that must be provided for research data, briefly ad-dresses privacy and ethics issues and provides the researchers with guidelines for GPDR-compliant handling of personal data which might be collected in the projects. The DMP is a living document and therefore will be updated as necessary.

Sixteen protein sequences for enzymes with known activity against polyethylene terephthalate (PET) were clustered using CD-HIT to derive a reduced set of twelve centroid sequences. These twelve protein sequences were aligned in a structure-guided multiple sequence alignment by T-COFFEE. A profile hidden Markov model (HMM) was derived from this multiple sequence alignment by HMMER.

Models trained with Heat Plume Prediction and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438. Based on raw data from doi:darus-3651 and doi:darus-3652.

Content: This dataset includes raw as well as processed data from three experiments (Exp 1 - 3). Each dataset consists of the readouts from the pressure sensor(s), as logged with the use of QmixElements, raw images, and segmented images. Log files: Data in the log files are essentially the raw output of the software QMixElements which is used to operate the syringe pumps and log the pressure sensors. It consists of the columns time, inlet pressure, outlet pressure as well as the flow rates of the two syringes. We need to point out that each pressure sensor has an offset which needs to be accounted for in order to account for the actual pressure in the most accurate way. In order to derive the correct pressure drop (Δp) across the flow cell (outlet pressure - inlet pressure), a two minute period of no flow is applied. It is expected that in the absence of flow the pressure drop across the cell has to be zero and, therefore, the offset of the pressure drop can be determined. For each experiment there are separate log files for stage a) initial permeability measurement (Exp[Nr.]_PermInitial) stage b) continuous injection of reactive solution (Exp[Nr.]_ParallelInjection) stage c) final permeability measurement (Exp[Nr.]_PermFinal) In Experiment 2 the final permeability measurement was not conducted, since the cell was clogged, thus the corresponding log file is missing. Note: When estimating the permeability reduction over time during stage c), e.g K/K0 = Δp0/Δp(t) , the initial pressure drop (Δp0) is very crucial. Small variations and uncertainties effect the estimated permeability reduction tremendously. Due to the small flow rates in the experiments, the measured initial pressure drop is very small compared to the pressure range of the sensors and falls within the error limit of the sensor itself, casting the measurements quite uncertain. Therefore, it is recommended to back calculate the initial pressure drop based on the initial permeability measurement of stage a) and the flow rate of stage c). Raw images: Images taken by optical microscopy are given in the folder rawImages. These are synchronized with the pressure measurements found in the log files from QMixElements, which is realized by the following naming convention of each image: Exp[Nr.]_[timestamp in sec]. The timestamp in seconds corresponds to the time in the log file. While in Experiment 1 and 2 images were captured at 0.1 frames per second (fps), in Experiment 3 the frame rate was set to 1 fps. In Experiment 3 some images got dumped during the recording. The physical resolution of the images are: 3.34 μm/pixel (Experiment 1), 3.36 μm/pixel (Experiment 2) and 3.17 μm/pixel (Experiment 3). These were calculated based on the known length of the domain divided by the number of pixels. Segmented images: The segmented images are saved in the folder processedImages. Not all of the raw images were processed: The first processed image has the timestamp of 420 seconds and corresponds to the time when the continuous injection of the reactant solution started. One image every five minutes were processed. These images were also flipped vertically compared to the raw images.