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

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.

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.

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.

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.

Instructions for the first steps with DaRUS

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.

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.

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.

This dataset contains the proposed sphingolipid rheostat model with mass-action kinetics and a proposed negative feedback from ceramide to its synthesis from Sphinganine. The model is written in the Systems Biology Markup Language (SBML) and with the experimental data, experimental conditions, parameters, and observables part of the PEtab paramter estimation problem. The PEtab problem was used to infer the parameters from the experimental data in a reproducible manner. It is uploaded with the scripts to execute the estimation and process the simulation results. Further, the estimated model parameters and model simulation results are uploaded in machine- and human-readable formats.

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.

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.

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

This repository contains historical data collected in the digital humanities project Dhimmis & Muslims – Analysing Multireligious Spaces in the Medieval Muslim World. The project was funded by the VolkswagenFoundation within the scope of the Mixed Methods initiative. The project was a collaboration between the Institute for Medieval History II of the Goethe University in Frankfurt/Main, Germany, and the Institute for Visualization and Interactive Systems at the University of Stuttgart, and took place there from 2018 to 2021. The objective of this joint project was to develop a novel visualization approach in order to gain new insights on the multi-religious landscapes of the Middle East under Muslim rule during the Middle Ages (7th to 14th century). In particular, information on multi-religious communities were researched and made available in a database accessible through interactive visualization as well as through a pilot web-based geo-temporal multi-view system to analyze and compare information from multiple sources. The code for this visualization system is publicly available on GitHub under the MIT license. The data in this repository is a curated database dump containing data collected from a predetermined set of primary historical sources and literature. The core objective of the data entry was to record historical evidence for religious groups in cities of the Medieval Middle East. In the project, data was collected in a relational PostgreSQL database, the structure of which can be reconstructed from the file schema.sql. An entire database dump including both the database schema and the table contents is located in database.sql. The PDF file database-structure.pdf describes the relationship between tables in a graphical schematic. In the database.json file, the contents of the individual tables are stored in JSON format. At the top level, the JSON file is an object. Each table is stored as a key-value pair, where the key is the database name, and the value is an array of table records. Each table record is itself an object of key-value pairs, where the keys are the table columns, and the values are the corresponding values in the record. The dataset is centered around the evidence, which represents one piece of historical evidence as extracted from one or more sources. An evidence must contain a reference to a place and a religion, and may reference a person and one or more time spans. Instances are used to connect evidences to places, persons, and religions; and additional metadata are stored individually in the instances. Time instances are connected to the evidence via a time group to allow for more than one time span per evidence. An evidence is connected via one or more source instances to one or more sources. Evidences can also be tagged with one or more tags via the tag_evidence table. Places and persons have a type, which are defined in the place type and person type tables. Alternative names for places are stored in the name_var table with a reference to the respective language. For places and persons, references to URIs in other data collections (such as Syriaca.org or the Digital Atlas of the Roman Empire) are also stored, in the external_place_uri and external_person_uri tables. Rules for how to construct the URIs from the fragments stored in the last-mentioned tables are controlled via the uri_namespace and external_database tables. Part of the project was to extract historical evidence from digitized texts, via annotations. Annotations are placed in a document, which is a digital version of a source. An annotation can be one of the four instance types, thereby referencing a place, person, religion, or time group. A reference to the annotation is stored in the instance, and evidences are constructed from annotations by connecting the respective instances in an evidence tuple.

Telemetry Data from Flying Laptop. For further information of the project: https://www.kleinsatelliten.de/

We compare how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimuthal equidistant projection, or two-point equidistant projection. The distances of the start and end point are varied. Map transitions zoom out and pan towards the middle point, then zoom in and continue panning, following the recommendations by Van Wijk and Nuij (IEEE InfoVis, 2003). We measure response time and accuracy in the task. We evaluate the results by the sample means per participant, using interval estimation with 95% confidence intervals. We construct the confidence intervals by using BCa bootstrapping. The study is pre-registered on OSF.io, but due to file size limitations, we were not able to submit the video stimuli there. Instead, we provide them here. This repository contains the MPEG-4 video files that were shown to the participants in the videos/ folder. These are numbered from 0 to 1199 for each of the three map projections, which are also stated in the file name, for a total of 3,600 video stimuli. An additional 3×6 example stimuli are also included. For each video stimulus, a JSON file with the same prefix file name (projection + number) is located in the metadata/ folder. These files contain the ground truth metadata for the respective stimulus. The stimuli shown for teaching the participants the task are located with the same structure under the examples/ folder. The entire source code for the study is also available in the related publication. The related repository includes: The code for generating the individual PNG frames, and JSON metadata, for each stimulus. The server and front-end code for the online study itself. The Python and R code for evaluating the study results.

Description of the first steps and the most important tasks for a new DaRUS-Admin

Data sets of the dissertation 'A Molecular-Continuum Multiscale Solver for Liquid-Vapor Flow: Modeling and Numerical Simulation', comprised of microscale particle simulation results for non-equilibrium liquid-vapor phase boundaries.

Pdf list containing all information collected about this object. For more information see entry in Goobi.This dataset is part of the digital gyroscope collection. For more information about the collection and its creation, please see the documentation dataset: Niklaus, Maria, 2021, "Information about the Gyrolog data repository", doi: 10.18419/darus-821, DaRUS.To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", doi: 10.18419/darus-gyrolog, DaRUS.