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Manufacturing Settings - Intermediate Results
No full text<p>The CO-VERSATILE project aims at increasing the adaptation capacity, resilience and flexibility of the European manufacturing sector, focusing on vital medical supplies and equipment, to support Europe in improving its response and preparedness to deal with pandemics. CO-VERSATILE aims to demonstrate how existing technologies and results of previous European projects can be customised, adapted and reused in order to deliver flexible 48-hour industrial response capability at scale. The delivery of such response capacity is demonstrated on seven Manufacturing Settings implemented within the project. These anufacturing Settings target to produce a wide range of vital medical supplies, including face masks, respiratory devices and disinfectant spray systems. </p><p>CO-VERSATILE implements its Manufacturing Settings based on a highly agile approach. The implementation is performed in six iterative cycles, each including build, measure and learn phases (Build-Measure-Learn = B-M-L). As we are currently at month 12 of the project duration, this deliverable presents the technical activities and results of the first year of the project.</p><p>Each Manufacturing Setting is introduced first, presenting the setting focus, objectives, and methodology. Then, the main technical activities performed so far in the project are detailed, highlighting the targets, the methodology and the tools utilized to execute them. The main results and their impact on the manufacturing setting development are discussed, in order to provide a clear picture of the status of the project evolution towards the final objectives in each of the settings. This is achieved also through a visualization of the main results and lessons learned at the end of each B-M-L cycle, which provide an overview of the progress of the activities in each setting. In parallel, the work plan and objectives for the next B-M-L cycles are proposed and discussed.</p><p> </p>
Concentrations of major ions in wet precipitation samples in Austria
No full text<p>This dataset was compiled by the Research Group for Environmental Analytics - Institute of Chemical Technologies and Analytics, TU Wien. The precipitation data was gathered as part of the Austrian precipitation chemistry network. It contains precipitation amount, concentrations of major ions, pH and conductivity at several sites in Lower Austria, Salzburg, Styria and Tyrol.</p><p>The dataset includes the following columns:<br><i>Datum </i>– date of the precipitation event, sampling takes place at the next day<br><i>Ort </i>– sampling site location<br><i>NS </i>– precipitation amount in mm<br><i>LF </i>– conductivity in µS/cm<br><i>pH </i>– pH value<br><i>NH4 </i>– concentration NH4+ in mg/L<br><i>Na </i>– concentration Na+ in mg/L<br><i>K </i>– concentration K+ in mg/L<br><i>Ca </i>– concentration Ca2+ in mg/L<br><i>Mg </i>– concentration Mg2+ in mg/L<br><i>Cl </i>– concentration Cl- in mg/L<br><i>NO3 </i>– concentration NO3- in mg/L<br><i>SO4 </i>– concentration SO42- in mg/L<br><i>Pb </i>– concentration Pb2+ in µg/L<br><i>Cd</i> – concentration Cd2+ in µg/L</p><p>In addition there are flag columns for every analyte. Used flags are:<br><i>1 </i>– valid<br><i>4 </i>– contaminated<br><i>7 </i>– not available</p><p>The sampling was performed with <i>WADOS – Wet And Dry Only Samplers</i> by Kroneis. Anions and cations were mostly quantified with chromatographic systems. Lead and Cadmium concentrations are not available for every station and were measured with ICP-OES. pH and conductivity were measured with respective electrodes. For more detailed information on measurement see the annual reports on wet deposition, published by the federal governments of Lower Austria, Salzburg, Styria and Tyrol.</p><h3>Code Availability</h3><p>Python code for correct calculation of deposition is available at the <a href="https://tiss.tuwien.ac.at/adressbuch/adressbuch/orgeinheit/4686">Research Group for Environmental Analytics</a>.</p><h3>Acknowledgements</h3><p>We would like to thank our partners at the federal governments of Lower Austria, Salzburg, Styria and Tyrol for funding and our former and current colleagues at TU Wien who collaborated to produce this dataset.</p>
The Sentinel-1 Global Backscatter Model (S1GBM) - Mapping Earth's Land Surface with C-Band Microwaves
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 European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>.</p><p>With this dataset publication, we open up a new perspective on Earth's land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns.</p><p>At TU Wien, we processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the designand verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping.</p><p>We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure.</p><p>Please be referred to our <a href="https://www.nature.com/articles/s41597-021-01059-7">peer-reviewed article at Nature Scientific Data</a> for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark.</p><h2>Dataset Record</h2><p>The VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the <a href="https://github.com/TUW-GEO/Equi7Grid">Equi7Grid</a> and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent ("T1"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB.</p><p>The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.</p><p>In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, twelve zipped dataset-collections per continent are available for download.</p><h2>Web-Based Data Viewer</h2><p>In addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (<a href="https://eodc.eu/">EODC</a>) under <a href="http://s1map.eodc.eu/">http://s1map.eodc.eu/</a>. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics.</p><h2>Code Availability</h2><p>We encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at <a href="https://github.com/TUW-GEO/yeoda">https://github.com/TUW-GEO/yeoda</a>.</p><p>Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at <a href="https://github.com/TUW-GEO/Equi7Grid">https://github.com/TUW-GEO/Equi7Grid</a>. More details on the grid reference can be found in <a href="https://www.sciencedirect.com/science/article/pii/S0098300414001629">https://www.sciencedirect.com/science/article/pii/S0098300414001629</a>.</p><h2>Acknowledgements</h2><p>This study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.</p>
The CLEF-IP 2010 Test Collection
No full text<h3>CLEF-IP: Cross-Language Evaluation Forum - Intellectual Property</h3><p>The CLEF-IP track was launched in 2009 to investigate IR techniques for patent retrieval and it is part of the CLEF 2010 evaluation campaign.The track utilizes a collection of more than 1.3M patent documents (~2.6 million files) derived from EPO (European Patent Office) sources, and published before 2001. The collection contains documents in English, French and German with at least 150,000 documents in each language. The task is to find patent documents that constitute prior art.</p><p>There are two tasks in the 2010's track. The first one is to find patent documents that are candidates to constitute prior art for a given document. The second task is to classify a given document according to the International Patent Classification system (IPC). Relevance judgements are produced using the patent citations and meta-data (bibliographic data).</p><h3>Files</h3><ol><li><strong>Document Collection</strong><br>The collection contains over 2.6 million XML files.</li><li><strong>Topics and Answers</strong><br>Both the training and the test topic sets contain also the relevance assessments for the topics.</li><li><strong>Guidelines</strong><br>Detailed explanation on how to work with the tasks from the corpus.</li></ol><h3> </h3>
The CLEF-IP 2013 Test Collection
No full text<h3>CLEF-IP: Cross-Language Evaluation Forum - Intellectual Property</h3><p>The CLEF-IP track ran from 2009 to 2013 and aimed to investigate IR techniques for patent retrieval.The track utilizes a collection of more than 1.3M patent documents (~2.6 million files) derived from EPO (European Patent Office) sources and EuroPCT Applications (more than 400K documents) published by WIPO (World Intelectual Property Organization). The collection contains documents in English, French and German with at least 150,000 documents in each language, all published before 2001.</p><p>There was one task in 2013: The first one was to find patent documents that are candidates to constitute prior art for a given claim taken from a patent document. </p><p>Files</p><ol><li><strong>Document Collection</strong><br>The corpus consists of two parts. The first one is a set of XML files representing a total of over 1.3 million patent documents - this collection is to be used for the first task.<br>NOTE: the document collection is the same as the one published for CLEF-IP 2011, excluding images.</li><li><strong>Topics and Answers</strong><br>Both the training and the test topic sets contain also the relevance assessments for the topics.</li></ol><p> </p>
FAIR Office Austria in a nutshell
No full text<p><a href="https://fair-office.at/">FAIR Office Austria</a> was created as part of <a href="https://forschungsdaten.at/en/fair-data-austria/">FAIR Data Austria</a>, a project funded by the Austrian Federal Ministry of Education, Science and Research (BMBWF) under the "Digital and Social Transformation" call for proposals. This presentation gives an overview of the goals of the organisation. Since June 2021, FAIR Office Austria is a <a href="https://www.go-fair.org/go-fair-initiative/go-fair-offices/">GO FAIR National Support and Coordination Office</a><a href="https://www.go-fair.org/go-fair-initiative/go-fair-offices/go-fair-austria-office/">.</a></p><p> </p>
Introductory Workshop on TU Data
No full text<p>On the 1st of June 2021, representatives of FAIR2environment introduced the project and gave some insights into the status quo of TU Data, the institutionary data repository of TU Wien. The focus of the workshop was set on the usage of the repository as a platform for publishing (geo-)data in accordance with the FAIR principles, which stand for <i>findable</i>,<i> accessible</i>,<i> interoperable </i>and <i>reusable </i>data. For further reading, the underlying slides of the the presentations are attached below.</p>
VODCA2GPP
No full text<p>The data descriptor paper can be accessed here: <a href="https://doi.org/10.5194/essd-14-1063-2022">https://doi.org/10.5194/essd-14-1063-2022</a></p><p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</p><p>Gross Primary Productivity (GPP) describes the amount of carbohydrates that is produced by vegetation's synthesis of CO2 and is therefore crucial in the assessment of the global carbon cycle. VODCA2GPP represents the first microwave remote sensing derived GPP dataset and covers the period between 1988-2020. The data is sampled on a regular 0.25°x 0.25° grid and is based on the novel sink-driven GPP estimation approach introduced by <a href="https://doi.org/10.1016/j.rse.2019.04.022">Teubner et al. (2019)</a> and <a href="https://doi.org/10.5194/bg-18-3285-2021 ">Teubner et al. (2021)</a>. It utilizes the new merged-frequency Vegetation Optical Depth Climate Archive (VODCA CXKu; Zotta et al., in preparation) in combination with ERA5-Land air temperature data to produce a coherent long-term data record of global GPP. </p><p>The dataset also includes an uncertainty metric (var_name: <i>'Uncertainties'</i>) which indicates regions where VODCA2GPP estimates tend to be less robust. We advise users to take these uncertainties into account when analyzing the VODCA2GPP data. </p><p>For more details concerning the production of VODCA2GPP and its accuracy assessment please be referred to our dataset paper which was published in <i>Earth System Science Data</i> <a href="https://doi.org/10.5194/essd-14-1063-2022">Wild et al. (2022</a>).</p><p> </p>
Initial Measure, Learn and Plan of the Manufacturing Settings
No full text<p>CO-VERSATILE implements its Manufacturing Settings based on a highly agile approach. The implementation is performed in six iterative cycles, each including build, measure and learn phases (Build-Measure-Learn = B-M-L). As the first build cycle is planned to start at the very beginning of the project, intensive preparation for that cycle was required in the time between the submission of the project proposal and the official kick-off of the project. This deliverable presents the results of these preparatory activities.</p><p>The detailed specification and requirements of each Manufacturing Setting were collected first, followed by the evaluation of these requirements and detailed discussions with the supporting Competence Centers. The outcome of these discussions is a plan that outlines the first Minimum Viable Product (MVP) that will be implemented as an outcome of the first B-M-L cycle and matches the Manufacturing Settings with Competence Centers that will support them in the implementation.</p><p><strong>Please note:</strong></p><p>This document is only available to the members of the CO-VERSATILE consortium. For further information please contact the project PI Robert Lovas/SZTAKI via <a href="[email protected]">[email protected]</a></p><p> </p>
Problem Instances for 'Exact and Meta-Heuristic Approaches for Unrelated Parallel Machine Scheduling'
No full text<p>This dataset contains the following instance sets from the <a href="https://hdl.handle.net/20.500.12708/11486">Master's Thesis</a> and <a href="https://doi.org/10.1007/s10951-021-00714-6">Journal Paper</a> 'Exact and Meta-Heuristic Approaches for Unrelated Parallel Machine Scheduling': Training instances, Validation instances (with reference solutions), and Real-Life instances</p>