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K21-1409
No full text<h2>Related work</h2>
<p>This dataset is part of the digital documentation of the Tomb of Meret Neith, Umm el Qaab, Abydos, Egypt about 3000BC, its artefacts, and the reconstruction of the tomb. For an overview of the related work, please visit <a href="https://researchdata.tuwien.at/communities/meretneith/">https://researchdata.tuwien.at/communities/meretneith/</a>.</p>
<h2>Archaeological information</h2>
<h3>Object name</h3>
<p>Knife fragment</p>
<h3>Object number(s)</h3>
<p>LN1491; K21-1409</p>
<h3>Description</h3>
<p>two pieces of two different bifacial flint knives, tip and middle part, possibly same object</p>
<h3>Location of object at time of photographs</h3>
<p>MoTA storage</p>
<h3>Find location</h3>
<p>Tomb Y; context L6 S2</p>
<h3>Bibliography</h3>
<p>N/A</p>
<h2>Technical Information</h2>
<p>For imaging, Canon RAW and Apple RAW images were captured.</p>
<h3>Physical properties</h3>
<table>
<tbody>
<tr>
<td>Length (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Width (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Height (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Weight (g)</td>
<td>N/A</td>
</tr>
<tr>
<td>Volume (cm3)</td>
<td>N/A</td>
</tr>
<tr>
<td>Density (g/cm3)</td>
<td>N/A</td>
</tr>
</tbody>
</table>
<h3>Comments</h3>
<p>only one part (K21-1409a) could be reconstructed, for K21-1409b only images are available</p>
<h3>Files overview</h3>
<ul>
<li><b>images_videos.zip</b> contains photographs of the real object in RAW format.</li>
<li>[optional] <b>exports_{H,M,L}Q.zip</b> archives contain the scanned object as 3D model in OBJ format including MTL and texture files. HQ, MQ, and LQ refer to high, medium and low quality versions of the model.</li>
<li>[optional] <b>{objectName}_report.pdf</b> contains further technical information of the reconstruction process.</li>
</ul>
K21-1490
No full text<h2>Related work</h2>
<p>This dataset is part of the digital documentation of the Tomb of Meret Neith, Umm el Qaab, Abydos, Egypt about 3000BC, its artefacts, and the reconstruction of the tomb. For an overview of the related work, please visit <a href="https://researchdata.tuwien.at/communities/meretneith/">https://researchdata.tuwien.at/communities/meretneith/</a>.</p>
<h2>Archaeological information</h2>
<h3>Object name</h3>
<p>Projectile point</p>
<h3>Object number(s)</h3>
<p>LN1610; K21-1490</p>
<h3>Description</h3>
<p>complete ivory arrowhead</p>
<h3>Location of object at time of photographs</h3>
<p>MoTA storage</p>
<h3>Find location</h3>
<p>Tomb Y; context L20 S1</p>
<h3>Bibliography</h3>
<p>N/A</p>
<h2>Technical Information</h2>
<p>For imaging, Canon RAW and Apple RAW images were captured.</p>
<h3>Physical properties</h3>
<table>
<tbody>
<tr>
<td>Length (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Width (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Height (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Weight (g)</td>
<td>N/A</td>
</tr>
<tr>
<td>Volume (cm3)</td>
<td>N/A</td>
</tr>
<tr>
<td>Density (g/cm3)</td>
<td>N/A</td>
</tr>
</tbody>
</table>
<h3>Comments</h3>
<p>no photogrammetry, only images</p>
<h3>Files overview</h3>
<ul>
<li><b>images_videos.zip</b> contains photographs of the real object in RAW format.</li>
<li>[optional] <b>exports_{H,M,L}Q.zip</b> archives contain the scanned object as 3D model in OBJ format including MTL and texture files. HQ, MQ, and LQ refer to high, medium and low quality versions of the model.</li>
<li>[optional] <b>{objectName}_report.pdf</b> contains further technical information of the reconstruction process.</li>
</ul>
Supplementary Dataset for Structural Optimization in Tensor LEED Using a Parameter Tree and R-Factor Gradients
No full text<p>This archive contains raw data and results supplementing the paper "<strong>Structural Optimization in Tensor LEED Using a Parameter Tree and R-Factor Gradients</strong>", which introduces the viperleed-jax package. viperleed-jax is a modern and efficient implementation of structure optimization for quantitative low-energy electron diffraction (LEED-I(V)). It builds on the viperleed.calc Python package for LEED-I(V) calculations published previously.</p>
<p>For up-to-date information on viperleed.calc and viperleed-jax, see the project homepage at <a href="https://www.viperleed.org" target="_blank" rel="noopener">https://www.viperleed.org</a>.</p>
<h2>Data Structure</h2>
<p>The archive contains the following files:</p>
<p>viperleed-jax-raw-data/<br>├── Fe2O3_012_1x1/ <-- Fe2O3(1-102)-(1x1)<br>│ ├── Fe2O3_viperleed-jax-CPU/<br>│ │ └── ...<br>│ ├── Fe2O3_viperleed-jax-GPU/<br>│ │ └── ...<br>│ ├── Fe2O3_TensErLEED/<br>│ │ └── ...<br>│ ├── Countour_plot_meshes/<br>│ │ └── ...<br>├── Ir_2x1_O/ <-- Ir(100)-(2x1)O<br>│ └── ...<br>├── Pt25Rh75/ <-- Pt25Rh75(100)-(3x1)O<br>│ └── ...<br>├── Pt_111_Te_10x10/ <-- Pt(111)-(10x10)Te<br>│ ├── ...<br>│ └── CMAES_result_Pt_10x10_from_displaced_LMAX_10.npz<br>├── timing_benchmarks <-- Timing benchmark results<br>│ ├── timing_benchmarks_Fe2O3.csv<br>│ ├── timing_benchmarks_Ir2x1O.csv<br>│ ├── timing_benchmarks_Pt25Rh75.csv<br>│ └── timing_benchmarks_Pt_10x10.csv<br>└── README.md <-- This README file<br><br>For reproducibility, the Python environment used for the calculations is provided in the included <code>requirements.txt</code> file.</p>
<h3>Optimization of the Fe2O3(1-102)-(1x1) surface structure</h3>
<p>Using viperleed-jax, a LEED-I(V) structure optimization was performed for the Fe2O3(1-102)-(1x1) surface. The optimization is performed in three segments, each consisting of one full-dynamic reference calculation and one tensor-LEED structure optimization. The optimization process is discussed in detail in the main text of the above-mentioned paper.</p>
<p>This system was also used to benchmark the performance of viperleed-jax on CPU and GPU hardware, and to compare it against the TensErLEED backend used previously in viperleed.calc. The optimization progress for these calculations can be found in the <code>Fe2O3_012_1x1/</code> directory and the appropriate subdirectories. The top level of each subdirectory contains the final result.</p>
<p>Details of the optimization progress can be found in the history directory and in history.info. The countour plots shown in the main text were generated using the data in the <code>Countour_plot_meshes/</code> directory.</p>
<h3>Optimization of other surface structures</h3>
<p>The archive further contains inputs and results for the optimization of three additional surface structures using viperleed-jax on GPU hardware, as discussed in the SI of the main paper. These structures are:</p>
<p>- Ir(100)-(2x1)O</p>
<p>- Pt25Rh75(100)-(3x1)O</p>
<p>- Pt(111)-(10x10)Te</p>
<p>For Ir(100)-(2x1)O and Pt25Rh75(100)-(3x1)O, the appropriate directories contain the input and output files, plus a step-by-step history as produced by viperleed .calc's bookkeeper utility. The top level of each directory contains the final result. Details of the optimization progress can be found in the history directory and in history.info.</p>
<p>The optimization progress for these calculations can be found in <code>.npz</code> files in the respective SUPP directories. <code>.npz</code> is a format for storing compressed array data by the Python Numpy library. See the<a href="https://numpy.org" target="_blank" rel="noopener"> Numpy documentation</a> for more details.</p>
<p>For Pt(111)-(10x10)Te, due to the large size of the system, only the inputs and the <code>.npz</code> file containing the optimization progress are provided.</p>
<h3>Timing benchmarks</h3>
<p>The performance of viperleed-jax on GPU was benchmarked in the form of time needed to evaluate a single R-factor value and R-factor gradient, vs. the angular momentum cutoff used in the LEED-I(V) calculation. These benchmarks are provided in the<code>timing_benchmarks/</code> directory as .csv files, listing the cutoff used and the time taken for R-factor and gradient evaluation, as well as the time taken to just-in-time compile the JAX functions for the given cutoff.</p>
Tracebot In-Gripper
No full text<h3>Context and methodology</h3>
<p>This dataset was created to investigate pose refinement methods of transparent objects when held by a gripper. The aim is to quantitatively evaluate the performance of various methods in that context. We collected diverse data for the investigation, including glass and plastic objects, filled with liquid and empty, properties like opacity and index of refraction are varied. The selected objects also vary significantly in shape and size. Additionally, uniform and highly-textured backgrounds are tested.</p>
<h3>Technical details</h3>
<p>The dataset is collected by moving an object held by a robot gripper in front of two static cameras. The same gripper poses are collected for every scene, and the camera poses are obtained through inverse kinematics of the robot arm, after calibration. We use 3D-DAT (<a href="https://github.com/markus-suchi/3D-DAT/releases/tag/v1.0.0">https://github.com/markus-suchi/3D-DAT/releases/tag/v1.0.0</a>) for annotation, placing object models in the virtual 3D scene, and manually correcting their poses based on their reprojection error in the different RGB views.</p>
<p>To obtain 3D object models, the physical objects are coated using a mat spray paint after collecting the different scenes. A high-quality depth sensor (Photoneo MotionCam-3D scanner, <a href="https://www.photoneo.com/">https://www.photoneo.com/</a>) is used to reconstruct them. The set of 15 objects used in our experiments is illustrated in Figure 3, and includes plastics and glass objects, filled or empty with a variety of shapes, and a variety of sizes.</p>
<p>A total of 22 scenes are collected using two cameras (leading to a total of 44 set of images). The cameras are Intel Realsense D435 (<a href="https://www.intelrealsense.com/depth-camera-d435i/">https://www.intelrealsense.com/depth-camera-d435i/</a>), saving both the RGB image and the depth image at a resolution of 1280 × 720 pixels. The robotic arm performs a sequence of pose holding the object in various grasp pose, ensuring that the camera frustum is well covered while guaranteeing that mosst of the object is in view. The sequence consists in 16 poses. The light is uniform and comes from the top of the scene. All objects are collected in front of a uniform background and in front of a textured background.</p>
<p>The "objects/" folder contains the object models as well as a configuration file relating the model, its scale and its object id for annotation purposes. For each scene in the "scenes/" folder, the structure is as follow:</p>
<ul>
<li>rgb/ contains the color images</li>
<li>depth/ contains the depth obtained with the Realsense D435 camera</li>
<li>masks/ contains the groundtruth masks of the object</li>
<li>groundtruth_handeye.txt contains the camera poses of each viewpoint (each line contains pose in TUM format: id, tx, ty, tz, rx, ry, rz, rw with id being the current view, tx,ty,tz the translation, rx, ry, rz, rw the rotation as quaternion).</li>
<li>poses.yaml contains the scene objects annotation in the same world reference frame as the camera poses</li>
</ul>
K21-1413
No full text<h2>Related work</h2>
<p>This dataset is part of the digital documentation of the Tomb of Meret Neith, Umm el Qaab, Abydos, Egypt about 3000BC, its artefacts, and the reconstruction of the tomb. For an overview of the related work, please visit <a href="https://researchdata.tuwien.at/communities/meretneith/">https://researchdata.tuwien.at/communities/meretneith/</a>.</p>
<h2>Archaeological information</h2>
<h3>Object name</h3>
<p>Mud sealing</p>
<h3>Object number(s)</h3>
<p>LN1538; K21-1413</p>
<h3>Description</h3>
<p>small mud sealing</p>
<h3>Location of object at time of photographs</h3>
<p>MoTA storage</p>
<h3>Find location</h3>
<p>Tomb Y; Y-5 (Petrie), Y-KK5 (DAI)</p>
<h3>Bibliography</h3>
<p>N/A</p>
<h2>Technical Information</h2>
<p>For imaging, Canon RAW and Apple RAW images were captured.</p>
<h3>Physical properties</h3>
<table>
<tbody>
<tr>
<td>Length (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Width (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Height (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Weight (g)</td>
<td>N/A</td>
</tr>
<tr>
<td>Volume (cm3)</td>
<td>N/A</td>
</tr>
<tr>
<td>Density (g/cm3)</td>
<td>N/A</td>
</tr>
</tbody>
</table>
<h3>Comments</h3>
<p>no photogrammetry, only images</p>
<h3>Files overview</h3>
<ul>
<li><b>images_videos.zip</b> contains photographs of the real object in RAW format.</li>
<li>[optional] <b>exports_{H,M,L}Q.zip</b> archives contain the scanned object as 3D model in OBJ format including MTL and texture files. HQ, MQ, and LQ refer to high, medium and low quality versions of the model.</li>
<li>[optional] <b>{objectName}_report.pdf</b> contains further technical information of the reconstruction process.</li>
</ul>
Dataset for Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation
No full text<h2>Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation</h2>
<p>This dataset is associated with the Master's Thesis "Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation" and includes all relevant datasets that were created to facilitate experiments conducted. The thesis included the evaluation of SR algorithms on the downstream task of building delineation on the example of Austria. To achieve this, several datasets had to be accessed and created, which are featured in this repository. Further information regarding the process involved, code repositories, and the published thesis are accessible under the GitHub repository: <a href="https://github.com/Zerhigh/Evaluating_Sentinel-2_Super-Resolution_Algorithms_for_Automated_Building_Delineation">https://github.com/Zerhigh/Evaluating_Sentinel-2_Super-Resolution_Algorithms_for_Automated_Building_Delineation</a></p>
<h2>Structure & Processing Details</h2>
<p>All image files are processed similarly: </p>
<ul>
<li>Remote sensing images are saved as geotiffs with provided spatial transformation parameters. When using these images, retain their spatial attributes. </li>
<li>Images are processed and annotated with <a href="https://stacspec.org/en">STAC</a> metadata, with each folder containing its own collection.</li>
</ul>
<p>The following datasets are available:</p>
<ul>
<li>main datasets:
<ul>
<li>hr_masks: 2.5m resolution cadastral masks with building footprints</li>
<li>hr_orthophoto: 2.5m resolution orthophotos of Austria</li>
<li>lr_s2: 10m resolution Sentinel-2 images of Austria (temporally and spatially aligned with the other data sources)</li>
</ul>
</li>
<li>image_samples: samples dataset representing the structure of this data repository</li>
<li>building_delineation_inference: building delineation masks extracted from super-resolved or interpolated Sentinel-2 and orthophoto images</li>
<li>metric_results: results from the conducted experiments on presented metrics</li>
<li>stratification_tables: train/validation/test splits for different dataset configurations</li>
<li>super_resolved: super-resolved Sentinel-2 images (from lr_s2) output from all used SR models</li>
<li>tracasa_evaluation: dataset to achieve evaluation on a small subset for proprietary SR models</li>
<li>thesis_figures: figures and plots featured in the written thesis</li>
</ul>
<p>This dataset contains only the image data and results, code repositories are available on the linked GitHub repository.</p>
Portfolio & Showroom
Get PDF<p>Folien zur Präsentation von Portfolio & Showroom bei der Cluster Forschungsdaten Expo 2025. Portfolio & Showroom sind ein von der Universität für Angewandte Kunst Wien entwickeltes Open Source CRIS, inklusive öffentlicher digitaler Ausstellungsfläche der künstlerlisch-wissenschaftlichen Arbeiten.</p>
Networks & Communities
Get PDF<p>Poster zu Networks & Communities im Zuge der Expo 2025.</p>
Data related to publication "Reproducibility crisis in isothermal amplification: Lessons from benchmarking LAMP assays"
No full text<p>General Information: This data is related to the publication "Reproducibility crisis in isothermal amplification: Lessons from benchmarking LAMP assays" by Piglmann L., Campostrini L., Sommer R., Kirschner A., Krska R., Farnleitner A.H., Kolm C. and Reischer G.H. (2025) submitted to Applied and Environmental Microbiology. The raw data of the analysed LAMP assays are split up into four files: "File 1: First Assessment", "File 2: Specificity Experiments", "File 3: Sensitivity Experiments", "File 4: LOD Experiments". </p>
<p>In File 1 the raw data of the comparative assessment of nine LAMP assays via fluorescence monitoring are summarized. To this end, each assay was tested with a 1:10 dilution series of genomic DNA from <em>P. aeruginosa</em> type strain ATCC 10145, ranging from 100.000 to 100 genomic copies per reaction (gc/rxt), analysed in triplicates.</p>
<p>In File 2 the raw data of the specificity experiments of seven LAMP assays via fluorescence monitoring are summarized. For these experiments all LAMP assays were assessed with 19 non-target strains with a genomic DNA concentration of 1.000 gc/rxt and in triplicates. </p>
<p>In File 3 the raw data of the sensivitiy assessment of three LAMP assays via fluorescence monitoring using 12 target strains (6 clinical isolates and 6 environmental isolates) are summarized. The strains were analysed with a genomic DNA concentration of 1.000 gc/rxt and in triplicates.</p>
<p>In File 4 the raw data of the Limit-of-Detection (LOD) experiments of two LAMP assays via fluorescence monitoring are summarized. For the LOD experiments the two remaining LAMP assays were tested using genomic DNA from three <em>P. aeruginosa</em> strains (type strain ATCC 10145, clinical isolate 1, environmental isolate 1). Two-fold serial dilutions were used for all three strains and each concentration was measured in 12 replicate reactions. The LOD of the first LAMP assay was determined with dilutions of the ATCC 10145 type strain ranging from 5.76x10^4 to 7.03 gc/rxt, the clinical isolate 1 from 9.60x10^2 to 1.88 gc/rxt and the environmental strain 1 from 2.80x10^3 to 2.73 gc/rxt. As for the second LAMP assay, ATCC 10145 was measured in 2-fold dilutions from 7.20x10^3 to 7.03 gc/rxt, the clinical isolate 1 from 9.60x10^2 to 0.94 gc/rxt and the environmental strain 1 from 1.40x10^3 to 0.68 gc/rxt. </p>
Data Management Plan – Division of Macromolecular Chemistry
No full text<p>This is a general data management plan of the division Macromolecular Chemistry (FBMC) of the Institute of Applied Synthetic Chemsitry. This is applicable for all projects without an existing DMP. The focus of the research within the Devision Macromolecular Chemistry (FBMC) is on practice-oriented synthetic chemistry. The cornerstones of the activities are the synthesis and characterisation of products that are industrially and technologically exploitable and marketable as well as the development of technical manufacturing processes. Our research is devoted to the synthesis and modification of synthetic polymers and renewable materials, whereas there is a strong focus on fundamentals of photopolymerization, applied photopolymers, biomaterials, polymers and materials with defined architecture and polymer characterization.</p>