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Materials of Erasmus+ TrainRDM Open Science training week
Get PDF<p>Presentations, video recordings and exercises of the <a href="https://rdmtraininghub.eu/ ">TrainRDM</a> Open Science train-the-trainer week. The event was held at TU Wien, Vienna, from 30 May to 3 June 2022. </p><h2>Acknowledgement</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>
Kaunertal dataset (part)
No full text<p>This is a part of a UAV-scan of Upper Kaunertal, Austria. Collected by VUX-1UAV. Acquisition platform: helicopter. </p>
ObChange Dataset
No full text<p>This dataset that can be used to evaluate methods, which are able to detect changed objects when comparing two recordings of the same environment at different time instances. Based on the labeled ground truth objects, it is possible to differentiate between static, moved, removed and novel objects.</p><h2>Dataset Description</h2><p>The dataset was recorded with an Asus Xtion PRO Live mounted on the HSR robot. We provide scenes from five different rooms or parts of rooms, namely a big room, a small room, a living area, a kitchen counter and an office desk. Each room is visited by the robot at least five times while between each run a subset of objects from the <a href="http://www.ycbbenchmarks.com/">YCB Object and Model Set</a> (YCB)[1] is re-arranged in the room. In total we generated 26 recordings. For each recording between three and 17 objects are placed (219 in total). Furthermore, furniture and permanent background objects are slightly rearranged. These changes are not labeled because for most service robot tasks, this is not relevant.</p><p>Assuming most objects are placed on horizontal surfaces, we extracted planes in each room in a pre-processing step (excluding the floor). For each surface, all frames from the recording where it is visible are extracted and used as the input for ElasticFusion[2]. This results in a total of 34 reconstructed surfaces.</p><p>We provide pointwise annotation of the YCB objects for each surface reconstruction from each recording.</p><p>Images of exemplary surface reconstructions can be found here: <a href="https://www.acin.tuwien.ac.at/vision-for-robotics/software-tools/obchange/">https://www.acin.tuwien.ac.at/vision-for-robotics/software-tools/obchange/</a></p><p> </p><h4>Dataset Structure</h4><p>The file structure of <i>ObChange.zip</i> is the following:</p><p>Room</p><p> - scene2 </p><p> - planes </p><p> - 0 </p><p> - merged_plane_clouds_ds002.pcd </p><p> - merged_plane_clouds_ds002.anno </p><p> - merged_plane_clouds_ds002_GT.anno </p><p> - 1 </p><p> - merged_plane_clouds_ds002.pcd </p><p> - merged_plane_clouds_ds002.anno </p><p> - merged_plane_clouds_ds002_GT.anno </p><p> - ... </p><p> table.txt</p><p> - scene3</p><p>The pcd-file contains the reconstruction of the surface. The <i>merged_plane_clouds_ds002.anno</i> lists the YCB objects visible in the reconstruction and<i> merged_plane_clouds_ds002_GT.anno</i> contains the point indices of the reconstruction corresponding to the YCB objects together with the corresponding object name. The last element for each object is a bool value indicating if the object is on the floor (and was reconstructed by chance). The <i>table.txt</i> lists for each detected plane the centroid, height, convex hull points and plane coefficients.</p><p>We provide the original input data for each room. The zip-files contain the rosbag file for each recording. Each rosbag contains the tf-tree and the RGB and depth stream, as well as the camera intrinsic. Additionally, the semantically annotated Voxblox[3] reconstruction created with SparseConvNet[4] is provided for each recording. </p><p> </p><p>You may also be interested in <a href="https://www.acin.tuwien.ac.at/vision-for-robotics/software-tools/object-change-detection-dataset-of-indoor-environments/">Object Change Detection Dataset of Indoor Environments</a>. It uses the same input data, but the ground truth annotation is based on a full room reconstruction instead of individual planes.</p><p> </p><h2>Acknowledgements</h2><p>The research leading to these results has received funding from the Austrian Science Fund (FWF) under grant agreement Nos. I3969-N30 (InDex), I3967-N30 (BURG) and from the Austrian Research Promotion Agency (FFG) under grant agreement 879878 (K4R).</p><h4>References</h4><p>[1] B. Calli, A. Singh, J. Bruce, A. Walsman, K. Konolige, S. Srinivasa, P. Abbeel, A. M. Dollar, Yale-CMU-Berkeley dataset for robotic manipulation research, The International Journal of Robotics Research, vol. 36, Issue 3, pp. 261 – 268, April 2017.</p><p>[2] T. Whelan, S. Leutenegger, R. Salas-Moreno, B. Glocker, A. Davison, ElasticFusion: Dense SLAM without a pose graph, Proceedings of Robotics: Science and Systems, July 2015.</p><p>[3] H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, J. Nieto, Juan, Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning, in Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1366-1373, 2017.</p><p>[4] B. Graham, M. Engelcke, L. van der Maaten, 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks, Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 9224 – 9232, 2018.</p>
Sentinel-1 based analysis of the Pakistan Flood in 2022
No full text<p>This dataset was generated by the Remote Sensing Group of the <a href="https://www.geo.tuwien.ac.at/">TU Wien Department of Geodesy and Geoinformation</a> (<a href="https://mrs.geo.tuwien.ac.at/">https://mrs.geo.tuwien.ac.at/</a>), within a dedicated project by the Join Research Centre (JRC) of the European Commission. Open use is granted under the <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>.</p><p>End of summer 2022 Pakistan was hit by one of the most severe floods in decades. The event was covered by multiple satellite-based emergency services, including the <a href="https://emergency.copernicus.eu/">Copernicus Emergency Management Service (CEMS)</a> <a href="https://www.globalfloods.eu/technical-information/glofas-gfm/">global flood mapping (GFM)</a> component. As part of the project's consortium, the Technische Universität Wien (TU Wien) developed a dedicated flood mapping algorithm (Bauer-Marschallinger et al. 2022) using the Synthetic Aperture Radar (SAR) satellite Sentinel-1 as an input. The published dataset contains the results of the TU Wien algorithm for the time period August 10 to September 23, 2022 and the covered area is located in the southern part of Pakistan. Besides the binary flood maps, the dataset contains retrieved statistics aiming for presenting the impact of the event as seen from satellite data. With the publication of this dataset, we want to share timely results of our algorithm and support further studies about the event.</p><p>It is planned to publish the dataset alongside of a dedicated paper in the journal for "Natural Hazards and Earth System Sciences". Within in this publication, the flood mapping results were evaluated based on the results of the CEMS rapid mapping component.</p><h2>Dataset Record</h2><h3><strong>Flood mapping results</strong></h3><p>The flood mapping results (<i>FLOOD-HM-MASKED.zip</i>) are sampled at 20 m pixel spacing, georeferenced to the <a href="https://github.com/TUW-GEO/Equi7Grid">Equi7Grid</a> and divided into tile of 300km extent ("T3"-tiles). The used folder structure splits up the single file per Equi7Grid tile and the used filenaming can be interpreted as follows:</p><blockquote><p>VAR_TIME__POL_ORBIT_TILE_GRID_VERSION_SENSOR_CREATOR.tif</p></blockquote><p>Where:</p><ul><li>VAR: Variable name ("FLOOD-HM-MASKED")</li><li>TIME: Acquisition time of the used Sentinel-1 scene</li><li>POL: Polarisation of the used Sentinel-1 scene ("VV")</li><li>ORBIT: Orbit direction ("A" for ascending and "D" for descending) and relativ orbit number of the used Sentinel-1 scene</li><li>TILE: Equi7Grid tile code</li><li>GRID: Equi7Grid continent subgrid</li><li>VERSION: Software version and run number</li><li>SENSOR: Used sensor ("S1")</li><li>CREATOR: Creator of the file ("TUWIEN")</li></ul><p>The values of each files can be interpreted like this:</p><ul><li>0: no flood</li><li>1: flood</li><li>255: masked</li></ul><h3><strong>Flood statistics</strong></h3><p>The dataset consists of two statistical layers: the flood frequency (<i>flood_frequency.tif) and the time of the first flood detection (first_detection.tif</i>). Both layers are available as merged file for the whole study area and georeferences in the WGS84 coordinate system.</p><p>The flood frequency is known as the ratio of number of flood detection and number of valid observations of a pixel and is given in percentage in this case. It provides insights about the continuity and duration of a flood classification at a pixel level. For instance, the area which was flooded at least once or during the whole time period can be extracted.</p><p>The time of the first flood detection is given as day-of-year (DOY) and indicates the day when the first flood detection was found for a specific pixel. This information can be used to get insights about the progress of the flood.</p><h2>Related Software</h2><ul><li>The flood mapping results are provided in the Equi7Grid, which is accessible by its dedicated open-source package: <a href="https://github.com/TUW-GEO/Equi7Grid">https://github.com/TUW-GEO/Equi7Grid </a></li><li>The folder structure and filenaming of the flood mapping results is defined and handled by: <a href="https://github.com/TUW-GEO/geopathfinder">https://github.com/TUW-GEO/geopathfinder</a></li><li>The metadata definition of the flood mapping results is collected in: <a href="https://github.com/TUW-GEO/medali">https://github.com/TUW-GEO/medali</a></li><li>The flood mapping results follow a dedicated folder and filenaming structure, which can be handled easily by: <a href="https://github.com/TUW-GEO/yeoda">https://github.com/TUW-GEO/yeoda</a></li></ul><h2>Acknowledgements</h2><p>This study was funded by TU Wien, with co-funding from the project "Provision of an Automated, Global, Satellite-based Flood Monitoring Product for the Copernicus Emergency Management Service" (GFM), Contract No. 939866-IPR-2020 for the European Commission's Joint Research Centre (EC-JRC). The computational results presented have been achieved using i.a. the Vienna Scientific Cluster (VSC).</p><h2>References</h2><p>Bauer-Marschallinger, B., Cao, S., Tupas, M. E., Roth, F., Navacchi, C., Melzer, T., Freeman, V., and Wagner, W.: Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube, Remote Sensing, 14, 3673, 2022.</p>
Work-Based Learning in Manufacturing Industry. A Sector-Based Meta-Analysis
No full text<p>This Whitepaper has been put forward by the Working Group of Work-Based Learning of the International Association of Learning Factories (IALF). It discusses a meta analysis of work-based learning approaches in manufacturing.</p>
Robotic Gaze and Human Views
No full text<p>Similar to human-human interaction, gaze is an important modality in conversational human-robot interaction settings. Previously, human-inspired gaze parameters have been used to implement gaze behavior for humanoid robots in conversational settings and indeed improve the user experience. Other robotic gaze implementations disregard social aspects of gaze behavior and pursue a technical goal, e.g., face tracking. However, it is unclear how deviating from human-inspired gaze parameters affects the user experience. In this study, we use eye-tracking, interaction duration, and self-reported attitudinal measures to study the impact of non-human inspired gaze timings on the user experience of the participants in a conversational setting. We show the results for systematically varying the gaze aversion ratio of a humanoid robot over a broad parameter range from almost always gazing at the human conversation partner to almost always averting the gaze. The main results reveal that on a behavioral level a low gaze aversion ratio leads to shorter interaction durations and that human participants change their own gaze aversion ratio to mimic the robot, however they do not copy the robotic gaze behavior strictly. Additionally, in the lowest gaze aversion setting, participants do not gaze back as much as expected, which indicates a user aversion to the robot gaze behavior. However, participants do not report different attitudes toward the robot during the interaction for different gaze aversion ratios. To summarize, the urge of humans in conversational settings with a humanoid robot to adapt to the perceived gaze aversion ratio is stronger than the urge of intimacy regulation through gaze aversion and high mutual gaze is not always a sign of high comfort, as suggested earlier. This result can be used as a justification to deviate from human-inspired gaze parameters when it is necessary for specific robot behavior implementations.</p>
Selective α-Methylation of Aryl Ketones Using Quaternary Ammonium Salts as Solid Methylating Agents
No full text<h2>Analytical Data and Compound Numbering (in paper numbering vs. ELN entries) for the Publication entitled:<br><em>"Selective α-Methylation of Aryl Ketones Using Quaternary Ammonium Salts as Solid Methylating Agents"</em></h2>
<p>The paper was published on 2022-03-07 in the Journal of Organic Chemistry.</p>
<p>J. Org. Chem. 2022, 87, 6, 4305–4315</p>
<p>DOI: <a href="https://doi.org/10.1021/acs.joc.1c03158">10.1021/acs.joc.1c03158</a></p>
<p>Authors: Johanna Templ and Michael Schnürch</p>
<p>Funded by the Austrian Science Fund (FWF, project number P33064-N)</p>
<h3>Context and methodology</h3>
<p>In this Publication, we describe the use of phenyl trimethylammonium iodide (PhMe3NI) as an alternative methylating agent for introducing a CH3 group in α-position to a carbonyl group. Compared to conventional methylating agents, quaternary ammonium salts have the advantages of being nonvolatile, noncancerogenic, and easy-to-handle solids. This regioselective method is characterized by ease of operational setup, use of anisole as green solvent, and yields up to 85%.</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 Word file named <em><strong>J. Org. Chem. 2022, 87, 6, 4305-4315_coumpound number list.pdf </strong></em>is uploaded, that should clearly link the compound number given in the paper to the respective entry in the ELN (jotempl) and the respective analytical data files. </p>
<h3>Technical details</h3>
<p>The files uploded 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>
GPS Dataset
No full text<p>Dataset used for the evaluation described in "A Systematic Evaluation of an RTK-GPS device for Wearable Augmented Reality".</p><p>The Dataset is divided into sheets:</p><ul><li>PT_LOCATION_TIME:<ul><li>PT: Positional Test</li><li>LOCATION: LOC_A or LOC_B</li><li>TIME: 60s or 300s</li></ul></li><li>ST_AT_DSM_LOCATION_SMARTPHONE_TIME:<ul><li>ST: Static condition</li><li>AT: Accuracy Test</li><li>DSM: Data Saving Modality</li><li>SMARTPHONE: SMA0 or SMA1</li><li>LOCATION: LOC_A or LOC_B</li><li>TIME: 60s or 300s</li></ul></li><li>DY_AT_DSM_LOCATION_SMARTPHONE_TIME:<ul><li>DY: Dynamic condition</li><li>AT: Accuracy Test</li><li>DSM: Data Saving Modality</li><li>SMARTPHONE: SMA0 or SMA1</li><li>LOCATION: LOC_A or LOC_B</li><li>TIME: 60s or 300s</li></ul></li><li>ST_AT_RTM_LOCATION_TIME:<ul><li>ST: Static condition</li><li>AT: Accuracy Test</li><li>RTM: Real-time Modality</li><li>LOCATION: LOC_A or LOC_B</li><li>TIME: 60s or 300s</li></ul></li><li>DY_AT_RTM_LOCATION_TIME:<ul><li>DY: Dynamic condition</li><li>AT: Accuracy Test</li><li>RTM: Real-time Modality</li><li>LOCATION: LOC_A or LOC_B</li><li>TIME: 60s or 300s</li></ul></li></ul>
Materials of Erasmus+ TrainRDM Open Science training week
Get PDF<p>Presentations, video recordings and exercises of the <a href="https://rdmtraininghub.eu/ ">TrainRDM</a> Open Science train-the-trainer week. The event was held at TU Wien, Vienna, from 30 May to 3 June 2022. </p><h2>Acknowledgement</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>
RT-Percept Sibenik Cathedral
No full text<p>Pre-rendered dataset used in <a href="https://jaliborc.github.io/rt-percept/">Training and Predicting Visual Error for Real-Time Applications</a> for the Sibenik Cathedral scene. Generated using the RT-Percept renderer and the RT-Percept scenes.</p>