TU Wien Research Data
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3D Reconstruction Model of the Former Synagogue in the Eitelbergergasse 22, Vienna (1928/29 | architect: Arthur Grünberger)
No full text<p>This record contains model data originally created in the framework of the masters' thesis of Roland Müller at TU Wien (2008).</p><p>Facts: Association synagogue of the Tempelverein Hietzing (Hietzing Temple Association) |</p><p>Erected 1928/29 | Architectural competition 1924; architect: Arthur Grünberger (with Adolf Jelletz) | Capacity: seating for 136 women and 244 men; small prayer room: 34 seats | Appearance: free-standing, block-shaped building with a ring of stylized merlons; no towers or other kinds of decoration</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><p> </p>
3D mesh model front facade Karlskirche, Vienna Austria
No full text<p>mesh model [obj] plus raw data of front facade of Karlskirche, Vienna Austria</p>
<p>please contact <strong>[email protected]</strong> for access</p><p>captured with laserscanner Riegl VZ-400 and Surphaser 25 HSX plus photogrammetry; terrestrial scanning and application of lifting platform (see project report)<br>data matched with Riegl (Riegl LMS & TU Wien) dataset using point sampling and ICP matching; </p><p>coordinate system WGS84_EPSG 4978; transformation matrix to local coordinate system of capturing campaign included in dataset</p>
Temporal Silhouette for Stream Clustering Validation - Evaluation Tests
No full text<h2>Temporal Silhouette for Stream Clustering Validation - Evaluation Tests</h2><p>conducted for the paper: <strong>Temporal Silhouette: Validation of Stream Clustering Robust to Concept Drift</strong></p><h3>Context and methodology</h3><p>The Temporal Silhouette (TS) is an index for the internal validation of stream clustering that is robust and consistent in the event of concept drift and different types of outliers. TS is based on the well-known Silhouette index (Rousseeuw, 1987).</p><p>In this repository, TS is compared with 3 popular CVIs (Silhouette, Davies-Bouldin, Calinski-Harabasz) and 3 iCVIs (incremental Xie-Beni index, incremental Partition Separation index, incremental representative Cross Information Potential) when evaluating performances of 4 stream clustering algorithms (CluStream, DenStream, BIRCH and StreamKMeans++). Different data scenarios are used: 2 real-life cases, 4 stationary popular datasets for clustering evaluation submitted to 32 different forms-levels of degradation, and 200 synthetic scenarios that implement different types of concept drift identified in the literature, as well as spatial and temporal outliers.</p><p>This repository is framed within the research on the following domains: <i>algorithm evaluation, streaming data analysis, stream clustering, unsupervised learning, machine learning, data mining, data analysis. </i>Datasets and algorithms can be used for experiment replication and for further clustering evaluation and comparison.</p><h3>References</h3><p>Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Comput and Applied Mathematics 20:53–65</p><h3>Technical details</h3><p>Experiments are conducted in Python 3. The file and folder structure is as follows:</p><ul><li>[dataR] contains 4 datasets obtained from real data.</li><li>[dataS] contains 80 synthetic datasets for concept drift tests.</li><li>[dataT] contains 4 datasets for stationary tests</li><li>[plots] contains plots results generated by test scripts.</li><li>[results] contains tables with results generated by test scripts.</li><li>[utils] contains utilities for transforming data and plotting results.</li><li>"dependencies.py" installs required python packages.</li><li>"LICENSE" file.</li><li>"README.md" for further details, link to sources and instructions for reproducibility.</li><li>"run_analysis_real.py" runs experiments with stream clustering and real data.</li><li>"run_analysis_synthetic.py" runs experiments with stream clustering and synthetic data submitted to concept drift.</li><li>"run_stationary.py" runs experiments with stationary data submitted to different perturbations.</li><li>"run_TS_stability.py" runs sensitivity analysis on TS w and k parameters.</li><li>"toy_tests.py" shows some simple examples of TS main cases of concept drift.</li><li>"TSindex.py" implements and provides TS functions.</li></ul><h3>License</h3><p>The CC-BY license applies to all data generated with MDCgen. All distributed code is under the GNU GPL license.</p><h3>Note</h3><p>This version replaces and makes obsolete:</p><p>Iglesias Vázquez, Felix (2023). py-temporal-silhouette-main.zip. figshare. Conference contribution. https://doi.org/10.6084/m9.figshare.22149854.v1</p><h3> </h3>
3D Reconstruction Model of the Former Synagogue in the Kaschlgasse 4, Vienna (1931/32 | Architect: Franz Katlein)
No full text<p>This record contains model data originally created in the framework of ongoing research work.</p><p>Facts: Association synagogue of the Bethaus und Unterstützungsverein) Bnei Brith (Prayer House and Assistance Association Sons of Israel) listed in the annual reports of the IKG Vienna from 1910 onwards | Erected 1931/32 | Architect: Franz Katlein (with Carl Fleischer) | Capacity: seating for 256 women and 344 men | Appearance: »functionalist« religious building on ground and first floor with unremarkable apartments above</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>
Materials of Erasmus+ TrainRDM Open Science Training Week in Bucharest
No full text<p>Presentations, and exercises of the <a href="https://rdmtraininghub.eu/">TrainRDM</a> Open Science train-the-trainer week. The event was held at Universitatea Politehnica din București, Bucharest, from 27 March to 30 March 2023. </p><h2><strong>Acknowledgement</strong></h2><p>TrainRDM – Open Science and Research Data Management Innovative and Distributed Training Programme has been funded with support from the European Commission. </p><p>Project website: <a href="https://rdmtraininghub.eu/">https://rdmtraininghub.eu/</a>; Project ID: 2020-1-RO01-KA203-080170</p>
Global Scale Maps of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals
No full text<p>This dataset was generated by the <a href="https://www.tuwien.at/mg/geo/rs">Remote Sensing Group</a> of the <a href="https://www.geo.tuwien.ac.at/">TU Wien Department of Geodesy and Geoinformation</a>, within projects funded by the European Structural and Investments Funds, the Austrian Space Applications Programme, and the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). Open use is granted under the <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>.</p>
<p>The provided dataset publication aims to support users of the ASCAT soil moisture data as provided by the <a href="https://hsaf.meteoam.it/">EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)</a> to mask invalid retrievals due to subsurface scattering. This phenomenon has been discerned as the principal source of error in the current version of ASCAT soil moisture retrievals, as it contradicts the assumption that soil backscatter increases monotonically with soil moisture content. This happens because, in dry soil conditions, the presence of stones, rocks, or distinct soil layers can disrupt this expected relationship. At TU Wien, we have developed one statistical (P_ano) and two physically based indicators (P_sub, S_sub) that show the widespread occurrence of subsurface scattering not only in desert regions but also in more humid climates with distinct dry seasons. These indicators offer a means to identify subsurface scattering effects, enabling users of H SAF ASCAT soil moisture data to mask such effects. By selecting one of the provided indicators and setting a suitable threshold, users can tailor the masking process to their specific needs. As a baseline, we recommend using the monthly subsurface scattering mask included here in its dedicated file.</p>
<p>We encourage the user community to leverage this data record to enhance the design of ASCAT soil moisture validation and application experiments. Moreover, this resource invites a reevaluation of conclusions drawn from earlier ASCAT studies and presents an opportunity to extend insights to other active microwave sensors operating at lower microwave frequencies (S-, L-, and P-band).</p>
<h2>Dataset Record</h2>
<h3>ASCAT Mask Results</h3>
<p>This file contains the a mask recommended for ASCAT soil moisture retrievals, designed to address various conditions that can render ASCAT soil moisture data unreliable. These conditions encompass frozen soils, snow cover, wetlands, and areas with dense vegetation. Regarding the mitigation of subsurface scattering effects, the applied mask is derived from the monthly statistical indicator P_ano, with a threshold set at 0.1. This mask is applied to pixels that exceed this threshold for more than nine months. The results are gridded to the 12.5 km fixed Earth grid used for ASCAT (WARP5 grid).</p>
<p>By implementing this mask, ASCAT pixels with potentially unreliable soil moisture measurements are excluded. Note that the subsurface scattering mask represents mean monthly conditions over the years 2007 to 2021, i.e. the behavior in single years may deviate from these conditions.</p>
<p>The interpretation of values is as follows:</p>
<ul>
<li>0: indicates no mask is applied, signifying that the soil moisture retrieval is considered reliable</li>
<li>1: indicates the application of the mask, implying that the soil moisture retrieval may not be reliable</li>
</ul>
<h3>ERA5-Land Data Analysis</h3>
<p>For those seeking a more comprehensive exploration of subsurface scattering or desiring to generate customized masks utilizing the provided indicators, this file offers insights into ASCAT backscatter and surface soil moisture in combination with ERA5-Land soil moisture as well as ancillary information. The underlying data spans from 2007 to 2021 and is again gridded to the 12.5 km WARP5 grid. Within this file, users can access the following information:</p>
<p><strong>Subsurface scattering indicators:</strong></p>
<ul>
<li><strong>P_ano</strong> - Probability of the occurrence of backscatter anomalies, depicts how frequently the ASCAT backscatter data exhibit anomalies</li>
<li><strong>P_ano_MM</strong> - P_ano calculated on a monthly basis</li>
<li><strong>P_sub</strong> - Probability of detecting subsurface scattering, derived from a physically based method that compares the goodness of fit of two backscatter models</li>
<li><strong>S_sub </strong>- Subsurface scattering signal strength, displays the signal range of the subsurface scattering<br>term from completely dry to wet conditions</li>
</ul>
<p><strong>Correlations before and after masking:</strong></p>
<ul>
<li><strong>R_unmasked</strong> - Pearson correlation coefficient between ASCAT surface soil moisture and ERA5-Land soil moisture with no mask applied</li>
<li><strong>R_masked</strong> - Pearson correlation after applying the mask provided in the file above</li>
</ul>
<p><strong>Selected specific masks:</strong></p>
<ul>
<li><strong>cold_mask </strong>- Frozen soil and snow cover mask using ASCAT confidence flag (bit 1), ERA5-Land soil temperature (≤ 2 °C) and snow depth data (> 0 mm after averaging with a sliding window of 31 days)</li>
<li><strong>veg_mask </strong>- Dense vegetation mask based on ASCAT confidence flags (bits 4 and 5) and Copernicus Global Land Monitoring service (CGLS) leaf area index (LAI) data (LAI > 3)</li>
<li><strong>wet_mask </strong>- Wetland mask using Global Lakes and Wetlands Database (GLWD), land cover information, and ASCAT confidence flag</li>
<li><strong>subsurface_mask </strong>- Subsurface scattering mask based on monthly P_ano data (P_ano > 0.1) if threshold is exceeded for more than nine months</li>
</ul>
<p>Again, the values can be interpreted in the following way:</p>
<ul>
<li>0: indicates no mask is applied</li>
<li>1: indicates the application of the corresponding mask</li>
</ul>
<p>Some pixels exhibit no values, even with none of the provided masks applied. This can be attributed to supplementary masking via ASCAT confidence flags as well as the exclusion of problematic CCI land cover classes not explicitly supplied.</p>
<p><strong>Ancillary dataset information:</strong></p>
<ul>
<li><strong>cci_lc </strong>- ESA CCI land cover classification</li>
<li><strong>cfvo </strong>- 5 - 15 cm coarse fragment layer - International Soil Reference and Information Centre (ISRIC) SoilGrids™ 250m 2.0</li>
<li><strong>dem </strong>- Terrain height - ETOPO 2022 global relief model</li>
<li><strong>glwd </strong>- Global Lakes and Wetlands Database (GLWD) classification</li>
<li><strong>isric </strong>- SoilGrids™ 250m 2.0 soil types by the ISRIC classification</li>
<li><strong>karst </strong>- World Karst Aquifer Map (WOKAM) classification</li>
<li><strong>kg </strong>- Köppen-Geiger climate classification</li>
<li><strong>lai </strong>- CGLS LAI</li>
<li><strong>sand </strong>- Sand content in the surface layer (0 - 5 cm) - ISRIC SoilGrids™ 250m 2.0</li>
</ul>
<p>For more extensive details regarding ancillary data, please consult the associated references to the original datasets provided in the accompanying publication.</p>
<h3>ISMN Data Analysis</h3>
<p>For users interested in the comparison of ASCAT data with in-situ soil moisture stations, this file comprises the results for all ISMN stations with sufficient data from 2007 to 2021. All ancillary data are retrieved from the nearest ASCAT grid point index for better comparability to ERA5-Land data. The content of the file is exclusively comprised of the data points corresponding to ISMN stations, and does not encompass gridded data.</p>
<p>The only additional parameter not used in the ERA5-Land analysis file is:</p>
<ul>
<li><strong>sensor </strong>- Identifier string of the ISMN station sensor the ISMN soil moisture is retrieved from</li>
</ul>
<p>For additional information on ISMN measurements, please refer to the <a href="https://ismn.earth/">corresponding documentation</a>.</p>
<h2>Related Software</h2>
<ul>
<li>Software packages <a href="https://github.com/TUW-GEO/ascat/tree/v2.0.5">ascat</a>, <a href="https://github.com/TUW-GEO/ecmwf_models/tree/v0.9.1">ecmwf_models </a>and <a href="https://github.com/TUW-GEO/ismn/tree/v1.2.0">ismn </a>facilitated the reading and conversion process of data from <a href="https://hsaf.meteoam.it/Products/ProductsList?type=soil_moisture">ASCAT on-board the series of Metop satellites</a>, from the <a href="https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets">ECMWF reanalysis models</a>, and from the <a href="https://ismn.earth/">International Soil Moisture Database (ISMN)</a>.</li>
<li><a href="https://github.com/raphaelquast/EOmaps/tree/v7.0">EOmaps</a>, an open-source Python package, has been used to visualise and analyse the provided datasets in the corresponding publication and is a suitable means for users seeking to create geographical maps.</li>
<li>We also encourage users to take advantage of the grid point locator to quickly find locations through grid point indices (gpis) provided in the WARP5 grid: <a href="https://dgg.geo.tuwien.ac.at/">https://dgg.geo.tuwien.ac.at/</a></li>
</ul>
<h2>Acknowledgements</h2>
<p>This study was funded by the SustES project supported by the European Structural and Investments Funds (Adaptive Strategies for Sustainability of Ecosystem Services and Food Security in Harsh Natural Conditions, Reg. No. CZ.02.1.01/0.0/0.0/16 019/0000797), the ROSSIHNI project supported by the Austrian Space Applications Programme (FFG Pr.No. FO999892643), and the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF).</p>
<h2>Reference</h2>
<p>Wagner, W., Lindorfer, R., Hahn, S., Kim, H., Vreugdenhil, M., Gruber, A., Fischer, M. & Trnka, M. (2024). Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 62, pp. 1-20, Art no. 4509520. <a href="https://doi.org/10.1109/TGRS.2024.3429550" target="_blank" rel="noopener">https://doi.org/10.1109/TGRS.2024.3429550</a></p>
Vienna Celestial Reference Frame S/X 2022
No full text<p>Global Celestial Reference Frame estimated from Very Long Baseline Interferometry (VLBI) observations at the Vienna International VLBI Service for Geodesy and Astrometry (IVS) Analysis Center (VIE) at the standard geodetic and astrometric radio frequencies 2.3 and 8.4 GHz (S/X band).</p><ul><li>Data span: 1979.5 - 2023.0</li><li>VIE2022-sx global solution computed with VieVS (Vienna VLBI and Satellite Software)</li></ul>
arXiv-2105.12468:Antiferromagnetic magnons and local anisotropy: dynamical mean-field study
No full text<p>Data and script for Phys. Rev. B 104, 075152 (2021)</p><p>We studied antiferromagnetic magnons in one-, two- and three-orbital Hubbard model of square and bcc cubic lattice at intermediate coupling using dynamical mean-field theory (DMFT). We investigate the effect of anisotropy introduced by an external magnetic field or single-ion anisotropy. For the latter we tune continuously between the easy-axis and easy-plane models. We also analyze a model with spin-orbit coupling in cubic site-symmetry setting. The ordered states as well as the magnetic excitations are sensitive to even a small breaking of SU(2) symmetry of the model and follow the expectations of spin-wave theory as well as general symmetry considerations.</p><p>magnon.zip: Manuscript files for submission text +figs.</p><p>magnon_data.tgz: Final and intermediate data. Detailed description of scripts and codes including versions (available on github) in README.md</p><p>collective...ipynb: python notebook to generate figures in the text, includes selection of k-paths, definition of colorscales, etc.</p><p> </p><p> </p>
Danube Hazardous Substances Model (DHSM)
No full text<p>This output constitutes the upgraded version of the Solutions model adapted to territorial needs for transnational modelling of Hazardous Substances emissions in the Danube River Basin. The model was implemented within the Danube Hazard m3c project and is denoted as Danube Hazardous Substances Model (DHSM).</p><p>The DHSM has been set up on the basis of the generic Delft3D open-source modelling framework, the central version of which is maintained by Deltares. The use of this framework is supported by a dedicated <a href="https://oss.deltares.nl/web/delft3d">portal</a> to download source code and manuals, to exchange experiences and to ask questions.</p><p>This output consists of a file package and an annex.</p><p>The file package contains four folders:</p><ul><li>DHSM: the generic software plus all input files that together form the implementation of DHSM to the DRB.</li><li>Scenarios: alternative sets of input data for the P25, best estimate (P50) and P75 emission estimates, and for the scenarios S01-S07.</li><li>Supportive: some supportive files for presentation of the results.</li><li>Documentation:<ul><li>The general principles and operation of the DELWAQ framework used for the DHSM are documented in a <a href="https://content.oss.deltares.nl/delft3d4/D-Water_Quality_User_Manual.pdf">User Manual </a></li><li>The input file format is documented in a <a href="https://content.oss.deltares.nl/delft3d4/D-Water_Quality_Input_File_Description.pdf">separate manual</a></li><li>The mass balances output is documented in a separate manual, included in the package.</li></ul></li></ul><p>The accompanying annex "Danube River Basin Scale Assessment Report" provides a full account of the model approach and implementation.</p>