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SilviLaser 2021 Benchmark Dataset - Terrestrial Challenge
No full text<p>This benchmark dataset was acquired during the SilviLaser conference 2021 in Vienna. The benchmark aims to demonstrate the different terrestrial system's capabilities for capturing 3D scenes in various forest conditions. A number of universities, institutes, and companies participated and contributed their outputs to this dataset, compiled by terrestrial laser scanning (TLS), mobile laser scanning (MLS), as well as terrestrial photogrammetric systems (TPS). Along with the terrestrial data, one airborne laser scanning (ALS) data was provided as a reference.</p><p>Eight forest plots were installed in the terrestrial challenge. Each plot was formed with a 25-meter radius circular area and different tree species (i.e. spruce, pine, beech, white fir), forest structures (i.e. one layer, multi-layer, natural regeneration, deadwood), and age classes (~50 – 120 years). The 3D point clouds acquired by each participant cover the eight plots. In addition to point clouds, traditional in-situ data (tree position, tree species, DBH) were recorded by the organization team.</p><p>All point clouds provided by participants were processed in the following steps: co-registration with geo-referenced data, setting a uniform coordinate reference system (CRS), and removing data located out of the plot. This work was performed by OPALS, a laser scanning data processing software developed by the Photogrammetry Group of the TU Wien Department of Geodesy and Geoinformation. Please note that some point clouds are not archived due to problems encountered during pre-processing. The final products consist of one metadata, 3D point clouds, ALS data for reference, and corresponding digital terrain models (DTM) derived from the ALS data using OPALS software. Point clouds are in laz 1.4 format, and DTMs are raster models in GeoTIFF format. Furthermore, all geo-data use CRS of WGS84 / UTM zone 33N (EPSG:32633). More information (e.g. instrument, point density, and extra attributes) can be found in the file "SL21BM_TER_metadata.csv".</p><p>This dataset is available to the community for a wide variety of scientific studies. These unique data sets will also form the basis for an international benchmark for parameter retrieval from different 3D recording methods.</p><h2>Acknowledgements</h2><p>This dataset was contributed by the universities/institutes/companies (alphabetical order):</p><ul><li>Czech University of Life Sciences Prague</li><li>Forest Design</li><li>Green Valley International</li><li>RIEGL</li><li>Silva Tarouca Research Institute</li><li>Swiss Federal Institute for Forest, Snow and Landscape Research</li><li>Umweltdata GmbH</li><li>University of Natural Resources and Life Sciences</li><li>Wageningen University & Research</li></ul><h2>Notes</h2><ol><li>In terms of in-situ data, please contact <a href="mailto:[email protected]">Markus Hollaus</a> for details.</li><li>To perform a bulk download, please use <a href="https://owncloud.tuwien.ac.at/index.php/s/AvzGHO8Nfe1kWGZ/download">this file</a> to get the URL list.</li></ol><h2>Changelog</h2><ul><li>v1.0 First release</li><li>v1.1 Fix the misalignment issue for plot D</li></ul>
gigapixel Panoramas from Karlskirche, Vienna Austria
No full text<p>tiled gigapixel Panorama from the top of the cupola of Karlskirche, Vienna Austria; <a href="http://photoartkalmar.com/Photoart%20Kalmar%20high%20res/Gigapixel/Stadtpanorama%20Karlskirche/tour.html">online viewer available here</a><br>tiled gigapixel Panorama of the choir space of Karlskirche, Vienna Austria; <a href="http://photoartkalmar.com/Photoart%20Kalmar%20high%20res/Gigapixel/Karlskirche_Hochaltar/index.html">online viewer available here</a></p>
<p>please contact <strong>[email protected]</strong> for access</p><p>photos taken with panorama head adjusted to nodal point of camera; processed to gigapixel panorama using PTGui</p>
Connecting Data Repositories and DMP Tools using maDMPs
No full text<h2>Overview</h2><p>Data management plans (DMPs) are a relevant part of modern research. They contain information about the datasets produced and used in research projects and are often a requirement by funders for research projects.<br>As such, systems for managing these DMPs<strong> </strong>are becoming increasingly common at research organizations.<br>On the other hand, we have data repositories where datasets are often deposited and made available for sharing with other researchers.<br>At TU Wien and TU Graz, we are using the open-source tools Damap and InvenioRDM for these purposes, respectively.</p><p>Naturally, the DMP tool would often reference datasets stored in the data repository, and the data repository would have additional information about the datasets that could be used to enrich the DMPs (such as the licenses assigned to the datasets, etc.).<br>As of right now however, these systems don't communicate with each other and information is usually required to be manually entered separately in both systems.<br>This is not only tedious and inefficient, but also offers unnecessary potential for inconsistency.</p><p>In 2020, Haplo (now known as Cayuse) integrated DMPs as first-class citizens into their research data repository, to minimize this sort of friction for the users of their repository.</p><p>In contrast, we aim to create an integration between our two separate standalone systems which enables propagation of information from one system to the other in order to achieve a similar goal.<br>For this integration, we extend the APIs of both systems with endpoints that utilize the RDA DMP Common Standard for machine-actionable DMPs (maDMPs) for their communication.<br>Using this service-agnostic community standard allows us to extract a general communication protocol that can be used to integrate any data repository with any DMP tool in the future.</p><p>Since previous work has shown that full continuous synchronization of information between the two separate systems is difficult to achieve, we limit the scope of the integration to one-way updates for now.</p><p>With our poster, we intend to show the current state of the integration and the details about the communication, as well as a glimpse into our vision of its future.</p><h2>Further details</h2><p>The poster was presented during the International Data Week 2023 in Salzburg, Austria.</p>
arXiv-1902.04436: Antiferromagnetism in RuO2 as d-wave Pomeranchuk instability
No full text<p>Data + codes for Phys. Rev. B 99, 184432 (2019), arXiv-1902.04436</p><p>The [dft] directory contains the results of the DFT runs, including all the Wien2k and Wannier90 input/output files.</p><ul><li>[RuO2_1_GGA_afterSCF.tar.gz]: The result of the NM GGA.</li><li>[RuO2_2_GGA_U_afterSCF.tar.gz]: The result of the AFM GGA+U, with U = 2.8 eV and J = 0.2 eV.</li></ul><p>The [hf] directory contains the codes for the Hartree-Fock (HF, static mean-field) calculations.</p><ul><li>[codes]: The codes for the HF approach.</li><li>[example]: The result of the AFM HF, with U = 1.7 eV and J = 0.2 eV.</li></ul><p>The [arXiv-1902.04436] directory contains the manuscript and the figures for the arXiv.</p><p>SM.zip (data used in Supplement):</p><p>Fig_KH.zip : Data for figs. (agr, dat, gnu, ...) for Fig 2, 4, 5, S1, S2, and S5.<br><br>ruo2_hf.zip : Converged Hartree-Fock results for every U & J_H values.</p><p>DMFT_AH.zip: converged DMFT results</p><p> </p><p> </p>
3D Reconstruction Model of the Former Synagogue in the Hubergasse 8, Vienna (1885/86 | Architect: Ludwig Tischler)
No full text<p>This record contains model data originally created in the framework of the masters' thesis of Gerlinde Grötzmeier at TU Wien (2008).</p><p>Facts: Synagogue of the Israelitische Kultusgemeinde Wien (Jewish Community Vienna, IKG) | Erected 1885/86; completed on September 23, 1886; 1928 extended by the addition of a winter prayer room | Architects: Ludwig Tischler, Ignaz Reiser (extension) | Capacity: seating for 266 women and 406 men as well as winter prayer hall with 124 seats | Appearance: west front designed in a neo-Renaissance style, the central and side elements indicate the triple-aisled floor plan.</p><p>See also:</p><p>* City Guide --> https://www.lit-verlag.de/isbn/978-3-643-90170-5</p><p> </p><p>The data set includes</p><p>- Modelling software: ArchiCAD *.PLA - Archive</p><p>- Rendering Software: ArtLantis *.ATLA - Archive</p><p>- Panoramic representation: *.HTML & *.PNO</p><p> </p>
developed view of triumphal pillars Karlskirche, Vienna Austria
No full text<p>developed view of the two triumphal pillars of Karlskirche, Vienna Austria;<br>first version with cylindrical unrolling of the whole pillar with one vertical cut;<br>second version with unrolling the iconography in one continous ribbon;</p>
<p>please contact <strong>[email protected]</strong> for access</p><p>base dataset mesh from Meixner ZT GmbH; <br>unrolled using point sampling and unroll command within CloudCompare;<br>continous ribbon aligned using adobe photoshop; </p><p>numerical value in file name represents the horizontal image width in meters to scale;due to unrolling the horizontal direction is distorted, only the vertical direction is in scale</p>
A Roadmap towards an Ontology Commons Ecosystem for Ontology-based Data Documentation
No full text<p>This is a poster presented at the 21st RDA plenary in Salzburg, Austria, 2023.</p><p>The poster gives the main ideas for the development, implementation and widespread uptake of an Ontology Commons EcoSystem (OCES), which is an important pillar of the Industry Commons ecosystem.</p>
Data Analysis and Results of Threat Groups in OT Environment
No full text<p>We gathered data from these notable sources such as <em>Threat Group Cards by ThaiCERT</em>, <em>Malpedia by Fraunhofer FKIE</em>, <em>MITRE ATTCK</em>, and the <em>Industrial Control Systems Cyber Emergency Response Team (ICS-CERT)</em> and compiled publicly available information, including:</p>
<ul>
<li>News articles and threat reports, based on categories such as victim sector (targeted companies)</li>
<li>Threat actor group (responsible for the attack)</li>
<li>Number of publicly reported attacks until June 2023</li>
<li>Year of threat group discovery</li>
<li>Infrastructure target component (ISA- 95 model level targeted by attackers)</li>
<li>Source country of the threat</li>
<li>Victim countries of the threat</li>
<li>Motivation behind the attack</li>
<li>Tools used for the attack</li>
<li>Tool type employed in the attack</li>
</ul>
<p>From these sources, we compiled information on 120 threat groups targeting OT/ICS environments in industrial sectors such as manufacturing, energy, oil gas, industrial, petrochemical, and critical infrastructure.</p>
Key Characteristics of Algorithms' Dynamics Beyond Accuracy - Evaluation Tests
No full text<h2>Key Characteristics of Algorithms' Dynamics Beyond Accuracy - Evaluation Tests</h2><p>conducted for the paper: <strong>What do anomaly scores actually mean? Key characteristics of algorithms' dynamics beyond accuracy</strong> by F. Iglesias, H. O. Marques, A. Zimek, T. Zseby</p><h3>Context and methodology</h3><p>Anomaly detection is intrinsic to a large number of data analysis applications today. Most of the algorithms used assign an outlierness score to each instance prior to establishing anomalies in a binary form. The experiments in this repository study how different algorithms generate different dynamics in the outlierness scores and react in very different ways to possible model perturbations that affect data.</p><p>The study elaborated in the referred paper presents new indices and coefficients to assess the dynamics and explores the responses of the algorithms as a function of variations in these indices, revealing key aspects of the interdependence between algorithms, data geometries and the ability to discriminate anomalies. Therefeore, this repository reproduces the conducted experiments, which study eight algorithms (ABOD, HBOS, iForest, K-NN, LOF, OCSVM, SDO and GLOSH), submitted to seven perturbations related to: cardinality, dimensionality, outlier proportion, inlier-outlier density ratio, density layers, clusters and local outliers, and collects behavioural profiles with eleven measurements (Adjusted Average Precission, ROC-AUC, Perini's Confidence [1], Perini's Stability [2], S-curves, Discriminant Power, Robust Coefficients of Variations for Inliers and Outliers, Coherence, Bias and Robustness) under two types of normalization: linear and Gaussian, the latter aiming to standardize the outlierness scores issued by different algorithms [3].</p><p>This repository is framed within the research on the following domains: <i>algorithm evaluation, outlier detection, anomaly detection, unsupervised learning, machine learning, data mining, data analysis. </i>Datasets and algorithms can be used for experiment replication and for further evaluation and comparison.</p><h3>References</h3><p>[1] Perini, L., Vercruyssen, V., Davis, J.: <i>Quantifying the confidence of anomaly detectors in their example-wise predictions</i>. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer Verlag (2020).</p><p>[2] Perini, L., Galvin, C., Vercruyssen, V.: <i>A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods</i>. In: 2nd Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning @ ECML/PKDD (2020).</p><p>[3] Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Interpreting and unifying outlier scores. In: Proceedings of the 2011 SIAM International Conference on Data Mining (SDM), pp. 13–24 (2011)</p><h3>Technical details</h3><p>Experiments are in Python 3. Provided scripts generate all data and results. We keep them in the repo for the sake of comparability and replicability. The file and folder structure is as follows:</p><ul><li>[dataS] contains 70 synthetic datasets for the evaluation tests.</li><li>[plots_corr] contains plots with correlation matrices between indices through all experiments.</li><li>[plots_minmax] contains plots with performances for all experiments when linear normalization is used.</li><li>[plots_proba] contains plots with performances for all experiments when Gaussian normalization is used.</li><li>[scores_minmax] contains CSV files (one per dataset) with the outlier scores estimated by all algorithms under test (linear normalization)</li><li>[scores_proba] contains CSV files (one per dataset) with the outlier scores estimated by all algorithms under test (Gaussian normalization)</li><li>"all_minmax.csv" contains dynamic and accuracy indices calculated for all datasets and algorithms (linear normalization).</li><li>"all_proba.csv" contains dynamic and accuracy indices calculated for all datasets and algorithms (Gaussian normalization).</li><li>"compare_scores_group.py" is a Python script to extract new dynamic indices proposed in the paper.</li><li>"dyn_minmax.csv" contains new dynamic indices calculated for all datasets and algorithms (linear normalization).</li><li>"dyn_proba.csv" contains new dynamic indices calculated for all datasets and algorithms (Gaussian normalization).</li><li>"ExCeeD.py" is a Python library to extract Perini's Confidence index (taken from: https://github.com/Lorenzo-Perini/Confidence_AD).</li><li>"stability.py" is a Python library to extract Perini's Stability index (taken from: https://github.com/Lorenzo-Perini/StabilityRankings_AD).</li><li>"generate_data.py" is a Python script to generate datasets used for evaluation.</li><li>"indices.py" is a Python library to calculate accuracy indices.</li><li>"latex_table.py" is a Python script to show results in a latex-table format. </li><li>"merge_indices.py" is a Python script to merge accuracy and dynamic indices in the same table-structured summary.</li><li>"metric_corr.py" is a Python script to calculate correlation estimations between indices.</li><li>"outdet.py" is a Python script that runs outlier detection with different algorithms on diverse datasets.</li><li>"perf_minmax.csv" contains accuracy indices calculated for all datasets and algorithms (linear normalization).</li><li>"perf_proba.csv" contains accuracy indices calculated for all datasets and algorithms (Gaussian normalization).</li><li>"peri_conf_minmax.csv" contains Perini's confidence indices calculated for all datasets and algorithms (linear normalization).</li><li>"peri_conf_proba.csv" contains Perini's confidence indices calculated for all datasets and algorithms (Gaussian normalization).</li><li>"perini_tests.py" is a Python script to run Perini's confidence and stability on all datasets and algorithms' performances.</li><li>"peri_stab_minmax.csv" contains Perini's stability indices calculated for all datasets and algorithms (linear normalization).</li><li>"peri_stab_proba.csv" contains Perini's stability indices calculated for all datasets and algorithms (Gaussian normalization).</li><li>"README.md" provides explanations and step by step instructions for replication.</li><li>"scatterplots.py" is a Python script that generates scatter plots for comparing accuracy and dynamic performances.</li></ul><p> </p><h3>License</h3><p>The CC-BY license applies to all data generated with the "generated_data.py" script. All distributed code is under the GNU GPL license. For the "ExCeeD.py" and "stability.py" scripts, please consult and refer to the original sources provided above. </p>
Supplementary information for Design, Realization, and Validation of an Extrusion-Based Bioprinter for Hydrogel Applications on Microchips
No full text<h2>Supplementary information for Master Thesis: </h2><h2>"Design, Realisation, and Validation of an Extrusion-Based Bioprinter for Hydrogel Applications on Microchips"</h2><p>Authored by Andrius Petronaitis</p><h2>Context</h2><p>This repository contains relevant information to reproduce extrusion based bioprinter designed in aforementioned thesis. It consists of two parts: all relevant hardware information to reproduce the device 3D printed parts and all relevant software/firmware packages to control the device. </p><ul><li>Hardware files.rar</li><li>Software and firmware files.rar</li></ul><h3>Technical details</h3><ul><li>In order to modify and upload the firmware a capable design architecture software is needed, in our case we utilized Arduino IDE, but Visual Studio or something similar will be able to do the job.</li><li>To generate a 3D object and perform printing procedure a slicing software Prusa Slic3r was utilized, but other 3D slicing software, such as Cura from Ultimaker, should be fine.</li><li>Hardware files are meant to be 3D printed with a thermo filament printer.</li></ul><h3>Further details</h3><ul><li>For more information about this repository please check the thesis "Design, Realisation, and Validation of an Extrusion-Based Bioprinter for Hydrogel Applications on Microchips".</li></ul>