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Experteninterviews aus der Immobilienbranche, transkribiert
No full text<p>The data set includes the transcripts of three interviews supported by lead questions and conducted with real estate industry experts. The experts provide analysis of the various actors involved in the housing market in eastern Austria. The interview partners were each asked the following lead questions:</p>
<ul>
<li>What are the usual financial mechanisms through which new residential construction projects by public clients/non-profit building associations/commercial developers are financed?</li>
<li>How long do the concept, planning and construction phases of typical residential construction projects by public clients/non-profit building associations/commercial developers take?</li>
<li>What are the main differences between the activities of public clients/non-profit building associations/commercial developers in Burgenland, Lower Austria and Vienna?</li>
</ul>
<p>Furthermore, a series of questions regarding the activities of the respective actors on the housing market were determined in advance and posed during the interview. It is noteworthy that all of the questions had been pre-defined as open-ended.</p>
Structural joint inversion of SRT and SWA data
No full text<h1><strong>Structural joint inversion code and data</strong></h1>
<h2><strong>Context and methodology</strong></h2>
<p><strong>Research domain and context</strong><br>This software and accompanying dataset were developed in the context of research on <strong>joint inversion approaches in geophysics</strong>. The work focuses on the development and evaluation of a structural joint inversion algorithm that integrates seismic refraction tomography (SRT) and surface wave analysis (SWA) data within a unified inversion framework.</p>
<p><strong>Purpose of the software and dataset</strong><br>The primary research output is a <strong>Python implementation of a joint inversion algorithm</strong>. The accompanying dataset serves a <strong>demonstration and validation purpose</strong>, enabling users to reproduce example results, explore the behavior of the algorithm, and test modifications or extensions. The dataset is not intended as a comprehensive benchmark, but rather as a reproducible and transparent illustration of the algorithm’s application.</p>
<p><strong>Creation of the software and dataset</strong><br>The joint inversion algorithm was developed and implemented by the author. The dataset consists of <strong>synthetic data</strong>, generated specifically for demonstration purposes. Scripts for generating the synthetic data are included as part of the software distribution, ensuring full transparency and reproducibility of the examples.</p>
<p> </p>
<h2>Technical details</h2>
<p><strong>Dataset and code structure</strong><br>The repository is organized as follows:</p>
<ul>
<li>
<p><code>/sji</code><br>Contains the core Python implementation of the joint inversion algorithm.</p>
</li>
<li>
<p><code>/examples</code><br>Contains two example applications demonstrating the use of the software:</p>
<ul>
<li>
<p><code>Model A/</code></p>
<ul>
<li>
<p><code>data/</code> (synthetic input data for Model A)</p>
</li>
</ul>
</li>
<li>
<p><code>Model B/</code></p>
<ul>
<li>
<p><code>data/</code> (synthetic input data for Model B)</p>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<p>Each example includes scripts that can be executed to create the synthetic data and run the joint inversion workflow.</p>
<p>The data sets includes mesh files (.bms) and the synthetic data and model vectors, as well as sensor positions (.syn, .dat, .txt) stored as plain-text, ASCII-encoded files. All data files are generated using the provided scripts. Functions to import the different data types can be found in the provided scripts as well.</p>
<p> </p>
<p><strong>Running the example scripts</strong></p>
<p>1. Plot settings and synthetic model design</p>
<ul>
<li>Plotting preferences and the synthetic model design are defined in the script: <code>settings.py</code>.</li>
</ul>
<p>2. Create synthetic model</p>
<ul>
<li>Model vectors and mesh for the synthetic model are created by executing script: <code>1_create_model.py</code>.</li>
</ul>
<p>3. Create synthetic data</p>
<ul>
<li>The synthetic data files for SRT and SWA are created by executing script: <code>2_create_syn_data.py</code>.</li>
</ul>
<p>4. Create inversion meshes</p>
<ul>
<li>To create the inversion meshes and store them as .bms files run script: <code>3_create_invmesh.py</code>.</li>
</ul>
<p>5. Run the conventional inversion</p>
<ul>
<li>A conventional deterministic inversion can be performed for both methods using script: <code>4_conventional_inversion.py</code>.</li>
<li>The script contains code chunks to save the final model estimates and create a simple plot of the models.</li>
</ul>
<p>6. Run the joint inversion</p>
<ul>
<li>To run the joint inversion approach run script: <code>5_joint_inversion.py</code>.</li>
<li>The script contains code chunks to save the final model estimates and create a simple plot of the models.</li>
</ul>
<p> </p>
<p><strong>Software requirements</strong><br>The inversion alorithm is implemented in <strong>Python</strong>. The following dependencies are required:</p>
<ul>
<li>
<p>pygimli 1.5.3</p>
</li>
<li>
<p>scipy 1.15.2</p>
</li>
<li>
<p>matplotlib 3.10.0</p>
</li>
<li>
<p>seaborn 0.13.2</p>
</li>
<li>
<p>pandas 2.2.3</p>
</li>
<li>
<p>disba 0.7.0</p>
</li>
</ul>
<p>The file requirements.txt, included in the downloadable material, lists all required dependencies as well. </p>
<p><strong>Additional resources</strong><br>No external documentation beyond the source code is provided. The repository contains the full implementation of the algorithm as well as scripts for data generation and example execution.</p>
<h2>Further details</h2>
<p><strong>Reuse expectations</strong><br>The software and dataset are intended to be <strong>reused, adapted, and extended</strong>. Users may apply the algorithm to their own datasets, modify the implementation, or build upon it for further methodological development.</p>
<p><strong>Limitations and caveats</strong><br>No specific limitations beyond those inherent to synthetic demonstration data are currently known. Users should be aware that the provided datasets are illustrative and may not reflect the complexity of real-world geophysical data.</p>
<p><strong>Licensing</strong><br>The python codes are released under the <strong>MIT license</strong>.</p>
GeoTree3D - Synthetic Trees with Aligned Orthophotos, DSMs, and 3D Point Clouds
No full text<p><strong>GeoTree3D</strong> is a synthetic dataset for learning-based reconstruction of 3D tree geometry from sparse top-down geospatial data. It consists of procedurally generated trees with aligned RGB orthophotos, Digital Surface Models (DSMs), and colored 3D point clouds. Orthophotos include realistic canopy appearance and shadows under varying illumination, while DSMs encode tree height and crown extent consistent with airborne elevation data. GeoTree3D supports supervised learning and controlled evaluation of tree reconstruction methods from minimal geospatial input.</p>
<p>The dataset is organized into three main folders:</p>
<ol>
<li>
<p><strong>DSM</strong>, containing <code>.mat</code> files with heightmaps corresponding to individual trees; </p>
</li>
<li>
<p><strong>ORTHOPHOTOS</strong>, containing one folder per tree. Each folder includes a subfolder named <code>rendering</code>, which contains images <code>view_000.png</code> to <code>view_009.png</code> rendered under different illumination conditions, along with a <code>light_directions.txt</code> file specifying the corresponding light directions.</p>
</li>
<li>
<p><strong>TREES</strong>, containing <code>.mat</code> files with colored 3D point clouds for each tree and a <code>species_log.txt</code> file mapping tree identifiers to species labels.</p>
</li>
</ol>
<p>All <code>.mat</code> files are binary MATLAB v5 files storing standard numeric arrays (e.g., heightmaps, 3D coordinates, RGB values). They do not require MATLAB and can be opened using common scientific computing tools, such as Python via <code>scipy.io.loadmat</code>.</p>
<p>To facilitate long-term reuse, we additionally provide a <code>loading_data_scripts/</code> folder containing an example Python script <code>mat_loader.py</code> for loading DSMs, and point clouds, along with a <code>requirements.txt</code> file specifying the Python library versions used. The loading scripts are released under the MIT license.</p>
Consumer heat load profiles for district heating network in Lower Austria
No full text<p>The dataset contains consumer heat load profiles of a rural district heating network (DHN) in Großschönau, Lower Austria, for the year 2024.</p>
<p>The DHN supplies 18 consumers. For 11 consumers the heat consumption is measured on a daily basis (<code>daily_heat_consumption.csv</code>). For 7 consumers 1 min values are available, which were aggregated to hourly sums due to low resolution of the monitoring value (<code>hour_heat_consumption.csv</code>).</p>
<p>The consumers were given generic names for data compliance reasons.</p>
<p>Data was normalized to a yearly sum of 1 kWh.</p>
<p>File format is CSV, one column per consumer.</p>
<p>A paper containing an analysis of the dataset is on the way and will be referenced to in the future.</p>
The data for "Single-band fluorides akin to infinite-layer cuprate superconductors"
No full text<p>This repository contains the data behind the figures of the paper "Single-band fluorides akin to infinite-layer cuprate superconductors" by Wenfeng Wu et al.</p>
<p>Research context: Single-band fluorides studied by density functional and dynamical mean field theory as well as dynamical vertex approximation.</p>
<p>Purpose of the data: to make the results and figures of the aforementioned article reproducible by other groups</p>
<p>Relation to the article: the repository contains the data used to generate the figures of the aforementioned article</p>
<p> </p>
Data for: "Solar wind erosion of lunar regolith is suppressed by surface morphology and regolith properties"
No full text<h2>Introduction and Changelog</h2>
<p>This data repository contains the data behind the figures of the <a href="https://doi.org/10.1038/s43247-025-02546-0">paper entitled "Solar wind erosion of lunar regolith is suppressed by surface morphology and regolith properties"</a>. The CC-BY licence applies to all data files. </p>
<p>In the current version of the repository, the data are presented as they appear in the <a href="https://doi.org/10.1038/s43247-025-02546-0">final version of the above peer-reviewed manuscript</a>. In the original version, the same data were presented, but differently arranged. The previous version reflected the structure/order of the figures as they appeard in the <a href="https://arxiv.org/abs/arXiv:2410.14450">preprint available on arXiv</a>.</p>
<h2>Figure 1</h2>
<p>Data files behind Figure 1 (sputter yields for flat samples) have names starting with "Fig1a" and "Fig1b", indicating data for hydrogen and helium irradiations, respectively. The rest of the filename coincides with the labelling of the Figure.<br>They are organised as comma separated value (CSV) files, where the first line gives the names and units of the columns. For this Figure, data are given in both units amu/ion and atoms/ion, corresponding to both axes of the Figure. </p>
<h2>Figure 2</h2>
<p>Data that occurs for the first time in Figure 2 is stored in files starting with "Fig2a" and "Fig2b", respectively. The rest of the naming convention follows the labelling from the Figure legend, allowing to uniquely identify the data sets. Some data occur first in Figure 1. We refrained from uploading these data sets a second time. </p>
<h2>Figure 3</h2>
<p>No new original data are presented in Figure 3. Data that was created and analysed for this study was already shown in the previous Figures. Data from other publications are clearly identified in the text. We do not upload these data from different publications in this repository. Instead, the interested reader is referred to the original publications.</p>
TU Wien "Responsible Metrics" survey
Full text link<h3>+++ Survey motivation +++</h3>
<p>In 2022 the TU Wien library (UBTUW) started the project "Responsible Metrics" with the help of researchers from the Faculty of Physics and the Faculty of Civil and Environmental Engineering (CEE). Within the project's WP2 a university-wide survey was conducted among TU Wien research staff to elicit thoughts on topics such as the criteria used for evaluating research, the effectiveness of current evaluation methods, and potential improvements that can be made to the research evaluation process.</p>
<h3>+++ Survey implementation +++</h3>
<p>Alicia Fatima Gomez Sanchez (UBTUW )and Tadej Brezina (CEE) designed the survey which was refined in several feedback loops with other project team members. The online survey was implemented using MS Forms and distributed among all TU Wien research staff using internal mailing functions. This included a general information email and a reminder.</p>
<h3>+++ Survey duration +++</h3>
<p>The survey was open from June 28th to July 17th of 2023.</p>
<h3>+++ Files +++</h3>
<p>(1) Survey questions in English: 2023-06-23_survey-print.pdf<br>(2) File of all participants' answers in English: 2024-05-28_TUW-RespMetr_final.xlsx</p>
Palladium-catalyzed ortho alkoxylation of oxazoline derivatives: An avenue to reach meta-substituted electron-rich arenes via employing a traceless directing group
No full text<p><strong>Analytical Data and Compound Numbering (in paper numbering vs. ELN entries) for the Publication entitled:<br><em>"Palladium-catalyzed ortho alkoxylation of oxazoline derivatives: An avenue to reach meta-substituted electron-rich arenes via employing a traceless directing group</em><em>"</em></strong></p>
<p>The paper was published on 2024-10-25 in ACS Omega</p>
<p>DOI: <a href="https://doi.org/10.1021/acsomega.4c04389">10.1021/acsomega.4c04389</a></p>
<p>Authors: Raheleh Pourkaveh, Dennis Svatunek, Michael Schnürch</p>
<p>Funded by the Austrian Science Fund (FWF, project number P33064-N) </p>
<p><strong>Context and methodology</strong></p>
<p>An efficient and highly regioselective palladium-catalyzed oxazoline-directed alkoxylation is reported. The reaction proceeds under air and mild temperatures (60 °C). A series of alcohols can be used as alkoxylating agents and concomitantly act as reaction solvents, whereas primary and secondary alcohols are tolerated. Furthermore, fluorinated alcohols can be applied as well, introducing pharmaceutically relevant fluorine-containing groups. 1,3-Dialkoxylated products can be further subjected to hydrolysis transforming the oxazoline-directing group to a carboxylic acid, which can be removed by decarboxylation if desired. This approach demonstrates the capability to reverse the conventional site selectivity of electrophilic aromatic substitution reactions, since it allows the synthesis of arenes with two electron-donating groups in a 1,3-relationship.</p>
<p>The publication and its Supporting Information can be found as open-access files on the publisher's website (see DOI above).</p>
<p>All detailed files containing the analytical raw data, for all compounds given in the Supporting Information of the manuscript are uploaded. An additional pdf file named File code for compounds.pdf<strong><em> </em></strong>is uploaded, that should clearly link the compound number given in the paper to the respective entry in the ELN and the respective analytical data files. </p>
<p><strong>Technical details</strong></p>
<p>The files uploaded contain the FIDs of NMR spectra recorded by an in-house Bruker Spectrometer. A software to display NMR-spectra is needed, such as <a href="https://mestrelab.com/download/mnova/">MestreNova</a> or <a href="https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html">Topspin</a>).</p>
<p>HRMS data is uploaded too and has to be processed via <a href="https://www.agilent.com/en/promotions/masshunter-mass-spec">MassHunter</a> software.</p>
Code and training dataset for the publication entitled: "A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications"
Full text link<h1>Experiment Data & Analysis</h1>
<h2><strong>Overview</strong></h2>
<p>This repository contains raw data, code and analysis scripts related to experiments performed in the ‘<a href="https://doi.org/10.1038/s41598-021-89352-8">A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications</a>’. The data, code, and documentation provided here to facilitate reproducible research and enable further exploration and analysis of the experimental results.</p>
<h2><strong>Repository Contents</strong></h2>
<p><strong>Analysis Code:</strong></p>
<p>Languages: MATLAB 2020a or later with Deep Learning Toolbox</p>
<p>Description: This repository contains MATLAB scripts for data preprocessing, deep learning-based classification, and visualization of lung cancer cell images. The scripts train convolutional neural networks (CNNs) to classify six lung cell lines, including normal and five cancer subtypes.</p>
<p><strong>Documentation: </strong></p>
<p><strong>File</strong>: <code>LungCancer_CellLine_Code.zip</code></p>
<p>Description: This file provides exemplary code and sample images used for the machine learning approach.</p>
<p><strong>File</strong>: <code>Supplementary information and instructions.pdf</code></p>
<p>Description: This file provides an instruction and a description of the individual steps from raw data to image analysis.</p>
<p><strong>File</strong>: <code>Original Image data and Metadata Example - pc9.zip</code></p>
<p>Description: This .zip container provides an example of raw data in a native .vsi file format with folders containing the .ets file, with metadata documentation of the imaging parameters for a microfluidic channel imaged with the IX83 microscope.</p>
<p><strong>File</strong>: <code>Data augmentation documentation.docx</code> (and <code>Data augmentation documentation.pdf</code>)</p>
<p>Description: This document provides descriptions of how data augmentation was performed.</p>
<p><strong>File</strong>: <code>Raw data.zip</code></p>
<p>Description: This file contains image raw data.</p>
<p><strong>File</strong>: <code>GrayCellData.rar</code></p>
<p>Description: This file contains image data converted to grayscale images.</p>
<p><strong>File</strong>: <code>CellData_Full.rar</code></p>
<p>Description: This file contains RGB image data.</p>
<h2><strong>Microfluidic cultivation protocol prior to imaging:</strong><strong> </strong></h2>
<p><strong>Cell Lines: </strong>The lung normal cell and non-small lung cancer cells (PC-9, SK-LU-1, H-1975, A-427, and A-549)</p>
<p><strong>Plate Format: </strong>Plasma-bonded and coated microfluidics chip platform fabricated with silicon sheets and sterile object glass slides.</p>
<p><strong> </strong></p>
<p><strong>Surface Coating</strong></p>
<p>Prior to cell seeding, the surface of the polydimethylsiloxane (PDMS) microfluidic chip was treated with collagen to enhance cell adhesion. A 0.1% (w/v) collagen solution was prepared using Type I collagen (derived from rat tail) dissolved in a 0.02 M acetic acid buffer. The PDMS surfaces were incubated with the collagen solution for 2 hours at room temperature to allow for proper coating. Following this, the chips were rinsed with phosphate-buffered saline (PBS) to remove any unbound collagen. Collagen, being a key extracellular matrix component, provides a conducive environment for cell attachment and proliferation. This surface modification was crucial for ensuring that the cells would adhere effectively to the microfluidic architecture, promoting optimal growth conditions. The collagen coating facilitated stronger cell-matrix interactions, thereby improving the overall experimental reliability and enabling accurate analysis of cell behavior in the microfluidic system.</p>
<p><strong>Seeding Density</strong></p>
<p>In this study, various cell types (lung normal cells and non-small cell lung cancer cells: PC-9, SK-LU-1, H-1975, A-427, and A-549) were cultured within a microfluidic chip designed with a total length of 75 mm and a width of 25 mm, featuring three separate chambers, each with a diameter of 900 μm. The seeding density was calculated to be approximately 5,000 cells/mL. Given the chamber dimensions, this density was optimized to ensure that the cells could achieve ~70% confluency within a reasonable timeframe while maintaining their viability and functionality. The initial seeding in a 25 cm² culture flask allowed for efficient expansion and preparation of the cells prior to their transfer to the microfluidic environment (the cell culture medium was DMEM or RPMI supplemented with 10% FBS and 1% PS).</p>
<p><strong>Cultivation Duration</strong></p>
<p>After trypsin treatment of cells cultured in a flask, the cells were allowed to adhere to the microfluidic chip for a duration of 48-72 hours post-injection. This incubation period was essential for the cells to establish stable adhesion to the collagen-coated surfaces, enabling them to regain their morphology and functionality. It ensured that the cellular environment within the microfluidic chambers mimicked in vivo conditions, allowing for proper cell spreading and growth.</p>
<p><strong>Medium Composition</strong></p>
<p>The medium utilized for cell cultivation consisted of DMEM (Dulbecco's Modified Eagle Medium) or RPMI-1640, supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (PS), tailored to the specific cell types used. This composition was chosen to provide the necessary nutrients, growth factors, and antibiotics to support cell proliferation and prevent contamination. DMEM and RPMI are known to support a wide range of mammalian cell types, thereby enhancing the versatility of the experimental setup. The medium was pre-warmed to 37°C before use, and the cells were maintained in a humidified incubator at 37°C with 5% CO₂ during cultivation.</p>
<p><strong>Imaging Setup</strong></p>
<p>The imaging data was acquired using an automated IX83 microscope (Olympus, Japan), featuring a Merzhäuser motorized stage, a Hamamatsu ORCA-Flash4.0 camera, and a Lumencolor Spectra X fluorescent light source. This setup ensures high-resolution fluorescence imaging with precise stage control and sensitive image capture. Data was recorded automatically after adjustment of the z-axis using a multi-region area of interest on each microfluidic channel with the focus map function (medium density setting) with cellSens Dimension software (Version 2.1-2.3, Olympus). The DAPI staining of the blue fluorescence channel was used to facilitate large-area adjustment of the focus map prior to automated imaging. The green fluorescence channel representing the phalloidin staining of f-actin was used as a single channel exported images for the deep learning procedure outlined in the paper.</p>
<h2><strong>Setup and Installation</strong></h2>
<p><strong>1. Extract the Raw Data:</strong></p>
<p>Unzip the <code>Raw data.zip</code> file into your working directory.</p>
<p><strong>2. Environment Setup:</strong></p>
<p>Read the documentation <code>Supplementary information and instructions.pdf</code> and the <code>readme.txt</code> in the code for more details on the setup.</p>
<p><strong>3. Running the Analysis:</strong></p>
<p>Open the file <code>Supplementary information and instructions.pdf</code> for a detailed description.</p>
<h2><strong>Usage Instructions</strong></h2>
<p>Data Exploration: The analysis scripts include functions for exploratory data analysis (EDA). You can modify these scripts to investigate specific experimental conditions.</p>
<p><strong>Reproducibility</strong></p>
<p>Follow the code comments and documentation to replicate the analyses. Ensure that the environment and dependencies are correctly configured as described in the setup section.</p>
<p><strong>Licensing</strong></p>
<p>This repository is licensed as follows: Code is accessible under <strong>BSD 2-Clause "Simplified" </strong>license<strong> </strong>and data under a <strong>Creative Commons Attribution 4.0 International</strong> license.</p>
<p> </p>
<p><strong>Acknowledgement:</strong></p>
<p>This work was supported by the <strong>Iran National Science Foundation (INSF) Grant No. 96006759</strong>.</p>
<h2><strong><u>Contact persons:</u></strong></h2>
<p><em>For data acquisition:</em></p>
<p>Abdullah Allahverdi, [email protected];</p>
<p>Hadi Hashemzadeh, [email protected];</p>
<p>Mario Rothbauer, [email protected]</p>
<p> </p>
<p><em>For data processing and augmentation: </em></p>
<p>Seyedehsamaneh Shojaei, [email protected], [email protected]</p>
Element concentrations in water, soil and sediment samples from the Wulka River catchment
No full text<h2>Content</h2>
<p>The dataset contains the concentration of multiple elements in water samples taken during high-flow events in the Wulka River and two of its tributaries, and sediment and soil samples from its catchment.</p>
<h3>Context and methodology</h3>
<p>This dataset accompanies a publication where details regarding methods can be found.</p>
<ul>
<li>The following elements were analysed some or all of the samples: Li, K, Rb, Ba, Sc, Y, La, Ce, Ti, Zr, V, Nb, Cr, Mn, Fe, Co, Ni, Cu, Ag, Zn, Cd, Al, Ga, Tl, Pb, As, Sb, Bi, Se, Sr, U, N, P.</li>
<li>Additionally, loss on ignition (for solid samples) and suspended solids concentrations (for water samples) are included.</li>
<li>The analytical thresholds are reported with the data: Level of Detection (LOD) and Level of Quantitation (LOQ)</li>
<li>Samples were collected in the years 2021/2022</li>
<li>The Wulka River is a medium-sized river in eastern Austria and the only significant tributary to Lake Neusiedl.</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>Data consists of 3 CSV files, one file for river water samples, soil samples and sediment samples each.</li>
<li>In the tables, columns are separated by the semicolon, the dot is used as decimal separator.</li>
</ul>
<p>For further details, check the publication:</p>
<p>"Dynamics of potentially toxic elements in small rivers during high-flow events" by Steffen Kittlaus, Radmila Milačič Ščančar, Katarina Kozlica, Nikolaus Weber, Jörg Krampe, Matthias Zessner, Ottavia Zoboli, submitted to the Journal of Contaminant Hydrology.</p>