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VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
No full text<h2>VODCA v2</h2>
<p>Vegetation optical depth (VOD) is a model-based indicator of the water content stored in the vegetation canopy and is derived from microwave Earth observations. Moesinger et al. (2020) introduced the Global Microwave Vegetation Optical Depth Climate Archive (VODCA v1; <a href="https://zenodo.org/doi/10.5281/zenodo.2575598">10.5281/zenodo.2575598</a>), a dataset that harmonizes Vegetation Optical Depth (VOD) retrievals from multiple sensors across the C-, X-, and Ku frequency bands. This archive comprises three long-term, multi-sensor, single-frequency VOD products, covering over 30 years of observations. </p>
<p>VODCA v2 incorporates several methodological improvements compared to the first version and adds two new VOD datasets to the VODCA product suite. The VOD observations used in VODCA v2 are derived through the Land Parameter Retrieval Model (LPRM; Owe et al. (2008); Van der Schalie et al. (2017))</p>
<p><strong>VODCA CXKu</strong> is a multi-sensor, multi-frequency product of unprecedented coverage (34 years; 1987 - 2021), computed using observations in the C-, X- and Ku-band frequencies from the following sensors: the Special Sensor Microwave Imager (SSM/I) F08, F11, F13, F17, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), The Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), Windsat, the Advanced Microwave Radiometer 2 (AMSR2) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). We harmonized observations from the different sensors and frequencies by scaling them to AMSR-E X-band and then using a weighted averaging technique to fuse overlapping observations. VODCA CXKu is available at a daily temporal resolution and a spatial resolution of 0.25°. The data is masked for spurious observations (e.g., frozen ground, snow, radio-frequency interference). </p>
<p><strong>VODCA L</strong> is a single-frequency, L-band product created by using VOD observations from the Soil Moisture and Ocean Salinity (SMOS) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) and Soil Moisture Active Passive (SMAP) radiometer. We harmonized observations from the two sensors by scaling SMOS to SMAP and using a weighted averaging technique for the overlapping observations. VODCA L (2010 - 2021) has 10 daily observations and a spatial resolution of 0.25°. The data is masked for spurious observations (e.g., frozen ground, snow, radio-frequency interference). </p>
<h3>Technical details</h3>
<p>Files: </p>
<ul>
<li>"VODCA_CXKu.zip" (unzipped size: ~95 GB)
<ul>
<li>VODCA_CXKu daily images stored in netCDF (.nc) format, sorted into yearly folders</li>
</ul>
</li>
<li>"VODCA_L.zip" (unzipped size: ~3.5 GB)
<ul>
<li>VODCA_L 10 daily images stored in netCDF (.nc) format, sorted into yearly folders</li>
</ul>
</li>
</ul>
<p>Variables: </p>
<ul>
<li>for VODCA CXKU:
<ul>
<li>VODCA_CXKu - Unitless, Vegetation Optical Depth (multi-sensor, multi-frequency VOD from the C-, X- and Ku-band frequencies)</li>
<li>"time"/"lon"/"lat": Dimensions of the data</li>
</ul>
</li>
</ul>
<p> </p>
<ul>
<li>for VODCA L:
<ul>
<li>VODCA_L: Unitless, Vegetation Optical Depth (multi-sensor VOD in the L-band frequency)</li>
<li>"time"/"lon"/"lat": Dimensions of the data</li>
</ul>
</li>
</ul>
<p><strong>How to cite:</strong></p>
<p><strong>1. Zotta, R.-M., Moesinger, L., van der Schalie, R., Vreugdenhil, M., Preimesberger, W., Frederikse, T., De Jeu, R., & Dorigo, W. (2024). VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring (1.0.0) [Data set]. TU Wien. <a href="https://doi.org/10.48436/t74ty-tcx62" target="_blank" rel="noopener">https://doi.org/10.48436/t74ty-tcx62</a></strong></p>
<p><strong>2. Zotta, R.-M., Moesinger, L., van der Schalie, R., Vreugdenhil, M., Preimesberger, W., Frederikse, T., de Jeu, R., and Dorigo, W.: VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring, Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, 2024. </strong></p>
<p> </p>
<p>References:</p>
<p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. </p>
<p>Van der Schalie, Robin, et al. "The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E." <em>Remote Sensing of Environment</em> 189 (2017): 180-193.</p>
<p>Owe, Manfred, Richard de Jeu, and Thomas Holmes. "Multisensor historical climatology of satellite‐derived global land surface moisture." <em>Journal of Geophysical Research: Earth Surface</em> 113.F1 (2008).</p>
<p> </p>
Engineering of HO-Zn-N2 Active Sites in Zeolitic Imidazolate Frameworks for Enhanced (Photo)Electrocatalytic Hydrogen Evolution
No full text<h3>Context and Methodology</h3>
<p>This dataset contains the primary experimental and theoretical data supporting the research article published in <em>Angewandte Chemie</em> (DOI: 10.1002/anie.202419913; 10.1002/ange.202419913).</p>
<p>The files provide the raw and processed data used to characterize the local coordination environment, surface electronic states, and electrochemical performance of the synthesized materials, specifically focusing on the Hydrogen Evolution Reaction (HER). Supporting Density Functional Theory (DFT) calculations are also included. For detailed synthesis protocols, experimental setups, and comprehensive discussion of these results, please refer to the original publication and its Supplementary Information.</p>
<h3>Technical Details</h3>
<p><strong>1. Dataset Structure:</strong></p>
<p>The dataset is organized by characterization technique and measurement type.</p>
<ul>
<li>
<p><strong>File Format:</strong> All data is provided in <code>.xlsx</code> (Microsoft Excel) format for broad accessibility.</p>
</li>
<li>
<p><strong>Sample Identification:</strong> Inside each file, data columns are clearly labeled with the <strong>Sample Names/Numbers</strong> corresponding to those used in the manuscript.</p>
</li>
</ul>
<p><strong>2. File Descriptions:</strong></p>
<ul>
<li>
<p><strong><code>XAFS.xlsx</code>:</strong> X-ray Absorption Fine Structure data (including XANES and/or EXAFS) used to analyze the local coordination environment and oxidation states of the metal centers.</p>
</li>
<li>
<p><strong><code>XPS.xlsx</code>:</strong> X-ray Photoelectron Spectroscopy data, detailing surface elemental composition and valence states.</p>
</li>
<li>
<p><strong><code>IR.xlsx</code>:</strong> Infrared Spectroscopy (FTIR) data for functional group analysis.</p>
</li>
<li>
<p><strong><code>Raman.xlsx</code>:</strong> Raman spectroscopy data for analyzing vibrational modes and structural defects.</p>
</li>
<li>
<p><strong><code>HER.xlsx</code>:</strong> Electrochemical data for the Hydrogen Evolution Reaction (HER), including LSV polarization curves and stability test results.</p>
</li>
<li>
<p><strong><code>DFT.xlsx</code>:</strong> Data output from Density Functional Theory calculations, including adsorption energies, electronic density of states (DOS), or reaction pathway diagrams.</p>
</li>
</ul>
<p><strong>3. Software Requirements:</strong></p>
<p>No proprietary instrument software is required to view this data. All files are standard spreadsheets and can be opened with Microsoft Excel, LibreOffice, or similar software.</p>
<h3>Further Details</h3>
<p>Users are kindly requested to cite the original article when reusing any part of this dataset.</p>
Numerical results for "Compressing the two-particle Green's function using wavelets: Theory and application to the Hubbard atom"
No full text<div>
<div>This data repository contains the original figures, numerical (raw) data, and plot scripts to reproduce the figures from the publication "Compressing the two-particle Green's function using wavelets: Theory and application to the Hubbard atom" in the European Physical Journal Plus. LaTeX source files of the preprint can be found on the corresponding <a href="https://arxiv.org/abs/2402.13030" target="_blank" rel="noopener">arXiv</a> page.</div>
<h2>Licenses</h2>
<p>The CC-BY license applies to all the data. All distributed code is under the MIT license.</p>
</div>
Waste containers in public and semi-public spaces
No full text<h2>A photographic collection and categorization of waste containers in public and semi-public spaces</h2>
<p>This is <strong>Version 2</strong> of a photo collection created as part of the Urban Waste research project, funded by the Vienna Science and Technology Fund (WWTF) and the State of Lower Austria [10.47379/ESR20019]. The collection features photos of <strong>270 types of waste container systems</strong> (564 individual containers) installed in public and semi-public spaces, such as parks, shopping streets, train stations, museums, shopping centres, universities, hotels, and cinemas, from <strong>over 100 cities in 26 countries</strong>.</p>
<p>The collection is accompanied by a comprehensive categorization table, available in the Excel file. This categorization provides basic information (location, date etc.) and classifies each container system based on location categories and various technical and functional characteristics such as shape, collection fraction, container volume, opening mechanism, signage design, colour, construction material, and additional functions such as ashtrays, compression and dog bag dispensers.</p>
<p>The collected data can be a valuable source of information for research in waste management, studies on environmental behaviour, as well as in urban landscape planning, design and architecture.</p>
MYFix: Automated Fixation Annotation of Eye-Tracking Videos (Python Code and Sample Data)
No full text<h2>How to cite?</h2>
<p><a href="https://www.mdpi.com/1424-8220/24/9/2666">Alinaghi, N., Hollendonner, S., & Giannopoulos, I. (2024). MYFix: Automated Fixation Annotation of Eye-Tracking Videos. <em>Sensors</em>, <em>24</em>(9), 2666.</a></p>
<h2>Abstract</h2>
<div>In mobile eye-tracking research, the automatic annotation of fixation points is an important yet difficult task, especially in varied and dynamic environments such as outdoor urban landscapes. This complexity is increased by the constant movement and dynamic nature of both the observer and their environment in urban spaces. This paper presents a novel approach that integrates the capabilities of two foundation models, YOLOv8 and Mask2Former, as a pipeline to automatically annotate fixation points without requiring additional training or fine-tuning. Our pipeline leverages YOLO’s extensive training on the MS COCO dataset for object detection and Mask2Former’s training on the Cityscapes dataset for semantic segmentation. This integration not only streamlines the annotation process but also improves accuracy and consistency, ensuring reliable annotations, even in complex scenes with multiple objects side by side or at different depths. Validation through two experiments showcases its efficiency, achieving 89.05% accuracy in a controlled data collection and 81.50% accuracy in a real-world outdoor wayfinding scenario. With an average runtime per frame of 1.61 ± 0.35 s, our approach stands as a robust solution for automatic fixation annotation.</div>
<h2>How to use the code?</h2>
<p>To create the environment execute the following command: </p>
<p>`conda env create -f environment.yml`</p>
<p>Dependencies (which migth require extra attention):</p>
<p>- torch: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</p>
<p>- YOLO: conda install -c conda-forge ultralytics</p>
<p>- transformers: conda install -c conda-forge transformers</p>
<p> </p>
<p>To avoid problems with torch and numpy, install torch (which includes numpy anyway) first.</p>
<p>To annotate your eye-tracking data automatically, execute the `annotate.ipynb` file, and reference the data folder in the variable `base_path`, as well as the name of the gaze-data-file in `gaze_file` and the video-file in `video_file`. The following folder structure is mandatory:</p>
<p>```bash</p>
<p>.base_path</p>
<p>├── gaze_file </p>
<p>└── video_file</p>
<p>````</p>
<p>The execution of the script will create files in the following folder structure:</p>
<p>```bash</p>
<p>.base_path</p>
<p>├── extracted_frames</p>
<p>│ ├── frame_0.jpg</p>
<p>│ ├── frame_3.jpg</p>
<p>│ └── ...</p>
<p>│</p>
<p>├── outputs</p>
<p>│ ├── saved_frames_semSeg_yolo</p>
<p>│ │ ├── frame_0.jpg</p>
<p>│ │ ├── frame_3.jpg</p>
<p>│ │ └── ...</p>
<p>│ │</p>
<p>│ ├── confusion_matrix.png </p>
<p>│ ├── labeled_data_semSeg_yolo.csv</p>
<p>│ └── stitched_video.mp4</p>
<p>│</p>
<p>├── video_file</p>
<p>├── fixation_gaze_positions.csv</p>
<p>├── saccades_gaze_positions.csv</p>
<p>└── gaze_file</p>
<p>```</p>
<p>To receive evealuation results, a manual labeling for each frame is necessary. If no manual labeling is provided the script will return an error. Enter this information in the file `labeled_data_semSeg_yolo.csv` in the column `manual`.</p>
Data and scripts for "General Shiba mapping for on-site four-point correlation functions"
No full text<p>This data repository contains the original figures, numerical (raw) data, scripts that where used to calculate this data, and plot scripts to reproduce the figures from the publication "General Shiba mapping for on-site four-point correlation functions" at Physical Review Research. The preprint is available on <a href="https://arxiv.org/abs/2402.16115">arXiv</a> where also the LaTeX source files can be found. Additional information can be found in the README.</p>
<p> </p>
<h3><strong>License</strong></h3>
<p>The CC-BY license applies to all the data and pdf files. All distributed code is under the MIT license.</p>
Global Reference Frame VLBI solution VIE2023 from S/X-band
No full text<h2>Context and methodology</h2><ul><li>Global reference frame solution from Very Long Baseline Interferometry based on IVS data at S/X-band only</li><li>Celestial Reference Frame: Source positions are in the J2000 frame, but at the 2015.0 galactic aberration epoch. Reported source position errors are inflated with scaling factors: 1.5 RA, 1.5 De, and a RA/Dec noise floor 30/ 30 micro-arc-sec is applied.</li><li>Terrestrial Reference Frame: reference epoch 2015.0 and linear velocity</li><li>Earth Orientation Parameters: reported is one offset per session</li></ul><h3>Technical details</h3><ul><li>GENERATION_TIME: 2024-03-02T00:34:37</li><li>DATA_START: 1979-08-03T00:00:00</li><li>DATA_END: 2023-12-29T00:00:00</li><li>ANALYSIS_CENTER: VIE (TU Wien, Austria)</li><li>CONTACT: VIE Analysis Center ([email protected])</li><li>SOFTWARE: VieVS v3.2</li><li>TECHNIQUE: VLBI </li><li>FREQUENCY BANDS: S/X</li></ul>
GeoAR A calibration method for Geographic-aware augmented reality: Getting started
No full text<h2>How to cite</h2>
<p>Please, don't forget to cite the original research article that result in this application:</p>
<p>Galvão, M. L., Fogliaroni, P., Giannopoulos, I., Navratil, G., Kattenbeck, M., & Alinaghi, N. (2024). <a href="https://www.tandfonline.com/doi/full/10.1080/13658816.2024.2355326"><strong>GeoAR: a calibration method for Geographic-Aware Augmented Reality</strong></a>. <em>International Journal of Geographical Information Science</em>, 1–27. <a href="https://doi.org/10.1080/13658816.2024.2355326">https://doi.org/10.1080/13658816.2024.2355326</a></p>
<h2>GeoAR getting started application</h2>
<p>This getting started tutorial provides the basic information so you can implement your own geographic-aware AR application.</p>
<p>The project we provide here is described in the IJGIS article GeoAR: A calibration method for Geographic-aware augmented reality, and it provides the means for all four calibration approaches described in the article.</p>
<p>The set-up we provide here is for the device Microsoft Hololens 2, but feel free to adpat the code to use in different devices.</p>
<h2><strong>Basic requirements</strong></h2>
<p>In order to run and develop your GeoAR application using this project it is required the following:</p>
<ul>
<li>AR device (Microsoft Hololens 2)</li>
<li>Unity Hub with Unity 2021.3.2f1 installed (adaptations for a later version of Unity might be necessary)</li>
<li>Microsoft Visual Studio (Version 16.11.15 or later)</li>
<li>Mixed Reality Toolkit (MRTK) foundation package for Unity (2.8.0.0)</li>
</ul>
<p>If you do not have experience in developing with Unity or MRTK, we highly recommend you go through the following Microsoft training modules:</p>
<p><a href="https://learn.microsoft.com/en-us/training/modules/learn-mrtk-tutorials/1-1-introduction">Introduction to the Mixed Reality Toolkit – Set Up Your Project and Use Hand Interaction</a></p>
<p><a href="https://learn.microsoft.com/en-us/training/modules/intro-to-mixed-reality/">Introduction to mixed reality</a></p>
<h2><strong>1. Download and </strong>open<strong> </strong>the <strong>project in Unity</strong></h2>
<ol>
<li>Download the project folder and unpack it in your local machine</li>
<li>Use Unity Hub to open the project folder GeoARUnityProject (make sure you have the right version installed)</li>
<li>If everything is correct, you will be able to play the application in the game mode.</li>
</ol>
<p>Further instructions with video tutorials can be found here :</p>
<p><a href="https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/">https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/</a></p>
<h3><strong>License</strong></h3>
<p>All data is published under the CC-BY 4.0 license. The code is under the GNU General public license</p>
Wayfinding Stages: The Role of Familiarity, Gaze Events, and Visual Attention
No full text<h2>How to Cite?</h2>
<p>The paper is accepted for presenetation at <a href="https://cosit.ca/#accepted">COSIT2024</a>, but it is not yet published.</p>
<h2>Abstract</h2>
<p>Understanding the cognitive processes involved in wayfinding is crucial for both theoretical advances and practical applications in navigation systems development. This study explores how gaze behavior and visual attention contribute to our understanding of cognitive states during wayfinding. Based on the model proposed by Downs and Stea, which segments wayfinding into four distinct stages: self-localization, route planning, monitoring, and goal recognition, we conducted an outdoor wayfinding experiment with 56 participants. Given the significant role of spatial familiarity in wayfinding behavior, each participant navigated six different routes in both familiar and unfamiliar environments, with their eye movements being recorded. We provide a detailed examination of participants’ gaze behavior and the actual objects of focus. Our findings reveal distinct gaze behavior patterns and visual attention, differentiating wayfinding stages while emphasizing the impact of spatial familiarity. This examination of visual engagement during wayfinding explains adaptive cognitive processes, demonstrating how familiarity influences navigation strategies. The results enhance our theoretical understanding of wayfinding and offer practical insights for developing navigation aids capable of predicting different wayfinding stages. </p>
<h2>Data</h2>
<p>The data used in this paper is a CSV file (licensed under CC BY 4.0) that contains the following information:</p>
<ul>
<li>world_index: the scene video frame index</li>
<li>fixation_duration: duration of the fixation</li>
<li>x_fixation: corrected x coordinate of the fixation from the head movement impact</li>
<li>y_fixation: corrected y coordinate of the fixation from the head movement impact</li>
<li>synced_timestamp: timestamp of the event</li>
<li>stage: the wayfinding stage according to Downs and Stea theory </li>
<li>yolo_label: predicted label by Yolo</li>
<li>segmentation_label: predicted label by Mask2Former</li>
<li>semantic_label: the selected semantic label detected by MYFix</li>
<li>duration: duration of the stage</li>
<li>saccade_duration: duration of the corrected saccade</li>
<li>saccade_length: length of the corrected saccade</li>
<li>participant_id: anonymized id for the participants</li>
<li>familiarity: spatial familiarity of the participants (binary)</li>
<li>routeID: id of the routes</li>
</ul>
Adaptierter Siedlungsraum für Einwohner:innen (2020) und Beschäftigte (2018) in Österreich
No full text<h2>+++ Background +++</h2><p>Ein Konsortium bestehend aus (1) Elke Szalai, (2) Institut für Transportwirtschaft und Logistik der Wirtschaftsuniversität Wien, (3) Österreichisches Institut für Wirtschaftsforschung und (4) Forschungsbereich für Verkehrsplanung und Verkehrstechnik der TU Wien wurde vom Österreichischen Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation & Technologie (BMK) im Wege der Österreichischen Forschungsförderungsgesellschaft (FFG) mit dem Projekt "FLADEMO - Flächendeckende Mobilitäts-Servicegarantie" beauftragt.</p><p>Ein Teil dieses Projekts war die Analyse der flächendeckenden Versorgung mit Dienstleistungen des Öffentlichen Verkehrs (ÖV) in Österreich. Zum Zweck der räumlichen Verteilung von Einwohner:innen und Beschäftigten wurde ein neuer Flächendatensatz definiert und generierert, der Adaptierte Siedlungsraum (ASR).</p><h2>+++ Datengenerierung +++</h2><p>Ausführliche, illustrierte Beschreibung siehe Datei 2021-08-18_FLADEMO-task2-2_GIS_v1.pdf.</p><h2>+++ Datenbeschreibung +++</h2><p>Der Adaptierte Siedlungsraum für Einwohner:innen im Jahr 2020 (ASR-EW-2020) sowie für Beschäftigte des Jahres 2018 (ASR-BESCH-2018) wurde mit dem ÖV-Güteklassen (GKL) an Werktagen mit Schule (WTS) als auch an Werktagen in den Ferien (WTF) verschnitten.</p><p>Jede der vier Dateien (1) - (4) beinhaltet zwei Reiter:</p><ul><li>META: hier sind die Variablen aufgelistet und beschrieben.</li><li>Datentabelle: Datentabelle mit selbem Namen wie das File (1) - (4).</li></ul><p>Die vierstelligen Nummern zu Beginn der vier Dateinamen sind die Nummern des jeweiligen Bearbeitungsschrittes innerhalb des Projekts und dienen der Nachvollziehbarkeit. Sie haben keine darüber hinaus weiterführende Bedeutung.</p><h2>+++ Projektfinanzierung +++</h2><p>FLADEMO - Flächendeckende Mobilitäts-Servicegarantie, FFG-Projektnr. 884363.</p><h2>+++ Dateien +++</h2><p>(1) 0033_ASR-EW-2020_GKL_WTS.xlsx<br>(2) 0039_ASR-EW-2020_GKL_WTF.xlsx<br>(3) 0051_ASR-BESCH-2018_GKL_WTS.xlsx<br>(4) 0057_ASR-BESCH-2018_GKL_WTF.xlsx<br>(5) 2021-08-18_FLADEMO-task2-2_GIS_v1.pdf: Beschreibung der Daten, ihrer Quellen und der damit umgesetzten Operationen.</p>