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Landmark trees in Austria
No full text<p>This data repository contains 72 pictures of landmark trees and their surroundings from nine different regions in Austria, which we classified into alpine (i.e., taken in mountainous areas including Salzburg, Tyrol, and Styria) and flatland, which included locations in Lower Austria and Vienna. All pictures were taken in the summer of 2023. By landmark trees, we refer to remote trees that aid in wayfinding during outdoor navigation. </p><p>The data set consists of one folder with the 72 pictures and one CSV file containing the following attributes for each tree: </p><ul><li>Geographical coordinates (latitude, longitude) in the WGS84 coordinate reference system (CRS).</li><li>Species classification, determined either by a biologist, utilizing PlantNet [1], or data sourced from the Vienna Baumkataster [2].</li><li>Species categorization (deciduous or coniferous).</li><li>Environment designation (alpine or flatland) indicating the location type where the tree was situated.</li></ul><p> </p><p>[1]: Garcin, Camille, Alexis Joly, Pierre Bonnet, Jean-Christophe Lombardo, Antoine Affouard, Mathias Chouet, Maximilien Servajean, and Titouan Lorieul. "Pl@ntNet-300K: A Plant Image Dataset with High Label Ambiguity and a Long-Tailed Distribution," n.d.</p><p>[2]: "Baumkataster bzw. Bäume Standorte Wien - data.gv.at." Accessed February 18, 2024. <a href="https://www.data.gv.at/katalog/dataset/stadt-wien_baumkatasterderstadtwien">https://www.data.gv.at/katalog/dataset/stadt-wien_baumkatasterderstadtwien</a>.</p>
LongEval 2024 Test Collection
No full text<p>The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. All the data was collected over June 2023 and August 2023. In total, the collection contains 1,925 test queries. The set of documents consist of 4,321,642 downloaded, cleaned and filtered Web Pages. Translations of the webpages and queries into English will be added when available. The collection serves as the official test collection for the 2024 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF.</p>
Data FWF project I 4508 (CO2 fixation in extreme conditions)
No full text<p>Supporting and additional data to FWF project I 4508 "CO2 fixation in extreme conditions". Data in this ZIP file are organized and structured by the DOI numbers of the respective published articles they are referring to. All data are licensed under the CC BY 4.0 license. The software required to open and work with the files is: opju files --> OriginLab; xlsx files --> Excel</p>
NMR data - C2TCO
No full text<h2>NMR data - FID files</h2>
<p>The provided data includes the FID files for all recorded NMR spectra as reported in <a href="https://doi.org/10.1021/jacs.0c07922">10.1021/jacs.0c07922</a> (freely accessible). The NMR datasets are organized as obtained directly after the measurement, allowing further processing with commonly used software, such as <a href="https://mestrelab.com/main-product/nmr">Mnova</a> or <a href="https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html">TopSpin</a>. The files and folders are named after the original experiment. For the assignment of the data to the respective compound numbers as specified in <a href="https://doi.org/10.1021/jacs.0c07922">10.1021/jacs.0c07922</a> please see the provided PDF file ‘file_names_for_compound_numbers.pdf’.</p>
<p>For details on the used compounds and all compound numbers see <a href="https://doi.org/10.1021/jacs.0c07922">10.1021/jacs.0c07922</a>.</p>
<h2>Technical information</h2>
<p><sup>1</sup>H and <sup>13</sup>C NMR spectra were recorded on a Bruker AC 200 MHz, a Bruker Avance UltraShield 400 MHz or a Bruker Ascend 600 MHz spectrometer at 20 °C.</p>
<h2>Summary of results</h2>
<p>The obtained data confirmed the chemical structures of all synthesized compounds. Results of data analysis are provided in <a href="https://doi.org/10.1021/jacs.0c07922">10.1021/jacs.0c07922</a> (freely accessible).</p>
Supplimentary Data for "Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures"
No full text<h1>This is the supplimentary data for the publication: "Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures" in Biomimetics</h1>
<h2>Contents of the folders:</h2>
<h3>confocal</h3>
<p>Measurements done with the Nanofocus usurf explorer of: Silver Ants, shrimp shells (pre and post climate chamber exposure), PVS stamp and chitsoan films</p>
<h3>FTIR</h3>
<p>averaged data (exported from LabCognition panorama 4.0 to excel) of FTIR measurments (Bruker Lumos) of structured and unstructured chitosan films. Python scripts for generating reflectance diagrams. Reflectance diagrams.</p>
<h3>optical</h3>
<p>image of chitosan film with CD structure taken with a digital camera (Canon EOS R10). Two Silver Ant Speciman under an optical microscope.</p>
<h3>SEM</h3>
<p>SEM Findings include serveral micrographs of shrimp shells as well as micrographs of an investigated Silver Ant gaster. Instrument used: ThermoFisher Scios II</p>
<h3>miscellaneous</h3>
<p>Graphs and illustrations used in the article</p>
<p> </p>
<h2>Technical Details</h2>
<p>The uploaded data uses common file formats (pdf, xlsx, PNG & JPG) which should require no special software to read.</p>
<p>The python files are functional with the following package versions: </p>
<div>Python 3.12</div>
<div>* matplotlib 3.9.2</div>
<div>* numpy 2.1.0</div>
<div>* pandas 2.2.2</div>
<div>* openpyxl 3.1.5</div>
<h2>License</h2>
<p>The CC-BY license applies to all the data. All distributed code is under the MIT license.</p>
ESA CCI SM GAPFILLED Long-term Climate Data Record of Surface Soil Moisture from merged multi-satellite observations
No full text<p>This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: <a title="ESA CCI SM website" href="https://climate.esa.int/en/projects/soil-moisture/" target="_blank" rel="noopener">https://climate.esa.int/en/projects/soil-moisture/</a></p>
<p>This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.</p>
<h2>Dataset Paper (Open Access)</h2>
<p>A description of this dataset, including the methodology and validation results, is available at:</p>
<p><em>Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, <a href="https://doi.org/10.5194/essd-17-4305-2025" target="_blank" rel="noopener">https://doi.org/10.5194/essd-17-4305-2025</a>, 2025. </em></p>
<h2>Abstract</h2>
<p>ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.<br>However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.<br>Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.</p>
<h3>Summary</h3>
<ul>
<li>Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling</li>
<li>Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology</li>
<li>Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.</li>
<li>More information: See Preimesberger et al. (2025) and <a title="ESA CCI SM ATBD" href="https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)</a></li>
</ul>
<h2>Programmatic Download</h2>
<p>You can use command line tools such as <a href="https://www.gnu.org/software/wget/">wget</a> or <a href="https://curl.se/">curl</a> to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory <em>~/Download</em> on Linux or macOS systems.</p>
<blockquote>
<div>
<pre>#!/bin/bash<br><br># Set download directory<br>DOWNLOAD_DIR=~/Downloads<br><br>base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"<br><br># Loop through years 1991 to 2023 and download & extract data<br>for year in {1991..2023}; do<br> echo "Downloading year.zip..."<br> wget -q -P "DOWNLOAD_DIR" "year.zip"<br> unzip -o "year.zip" -d DOWNLOAD_DIR<br> rm "DOWNLOAD_DIR/$year.zip"<br>done</pre>
</div>
</blockquote>
<h2>Data details</h2>
<p>The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:</p>
<blockquote>
<p>ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc</p>
</blockquote>
<h3>Data Variables</h3>
<p>Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:</p>
<ul>
<li><strong>sm</strong>: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).</li>
<li><strong>sm_uncertainty</strong>: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.</li>
<li><strong>sm_anomaly</strong>: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)</li>
<li><strong>sm_smoothed</strong>: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.</li>
<li><strong>gapmask</strong>: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.</li>
<li><strong>frozenmask</strong>: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.</li>
</ul>
<p>Additional information for each variable is given in the netCDF attributes.</p>
<h3>Version Changelog</h3>
<p>Changes in <em>v9.1r1</em> (previous version was <em>v09.1</em>):</p>
<ul>
<li>This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).</li>
</ul>
<h3>Software to open netCDF files</h3>
<p>These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:</p>
<ul>
<li><a title="xarray" href="https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray </a>(python)</li>
<li><a title="netCDF4" href="https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 </a>(python)</li>
<li><a title="esa_cci_sm" href="https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm </a>(python)</li>
<li>Similar tools exists for other programming languages (Matlab, R, etc.)</li>
<li>Software packages and GIS tools can open netCDF files, e.g. <a href="https://code.mpimet.mpg.de/projects/cdo" target="_blank" rel="noopener">CDO</a>, <a href="http://nco.sourceforge.net/" target="_blank" rel="noopener">NCO</a>, <a href="https://www.qgis.org/" target="_blank" rel="noopener">QGIS</a>, ArCGIS</li>
<li>You can also use the GUI software <a href="https://www.giss.nasa.gov/tools/panoply/" target="_blank" rel="noopener">Panoply</a> to view the contents of each file</li>
</ul>
<h3>References</h3>
<ul>
<li>Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, <a href="https://doi.org/10.5194/essd-17-4305-2025">https://doi.org/10.5194/essd-17-4305-2025</a>, 2025. </li>
<li>Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8320869</a></li>
<li>Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: <a href="https://doi.org/10.1016/j.csda.2009.09.020" target="_new">https://doi.org/10.1016/j.csda.2009.09.020</a></li>
<li>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.</li>
</ul>
<h2>Related Records</h2>
<p>The following records are all part of the<a href="https://researchdata.tuwien.ac.at/communities/soilmoisture-climaterecords/records"> ESA CCI Soil Moisture science data records </a>community</p>
<table>
<tbody>
<tr>
<td>1</td>
<td>
<p>ESA CCI SM MODELFREE Surface Soil Moisture Record </p>
</td>
<td><a href="https://doi.org/10.48436/svr1r-27j77" target="_blank" rel="noopener">https://doi.org/10.48436/svr1r-27j77</a></td>
</tr>
<tr>
<td>2</td>
<td>
<p>ESA CCI SM RZSM Root-Zone Soil Moisture Record </p>
</td>
<td><a href="https://doi.org/10.48436/v8cwj-jk556">https://doi.org/10.48436/v8cwj-jk556</a></td>
</tr>
</tbody>
</table>
<p> </p>
Ligand engineering enhances (photo) electrocatalytic activity and stability of zeolitic imidazolate frameworks via in-situ surface reconstruction
No full text<p><strong>Context and Methodology</strong></p>
<p>This dataset contains the primary experimental and theoretical data supporting the research article published in <em>Nature Communication</em> (DOI: 10.1038/s41467-024-53385-0).</p>
<p>The files provide the raw and processed data used to characterize the structural, optical, and electrochemical properties of the synthesized materials, as well as supporting Density Functional Theory (DFT) calculations. For detailed synthesis protocols, experimental setups, and comprehensive discussion of these results, please refer to the original publication and its Supplementary Information.</p>
<p><strong>Technical Details</strong> <strong>1. Dataset Structure:</strong> The dataset is organized by <strong>characterization technique</strong> and <strong>measurement type</strong>.</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>XRD.xlsx</code>:</strong> Powder X-ray Diffraction (PXRD) patterns used for crystal structure analysis.</p>
</li>
<li>
<p><strong><code>IR.xlsx</code>:</strong> Infrared Spectroscopy (FTIR) data.</p>
</li>
<li>
<p><strong><code>XPS.xlsx</code>:</strong> X-ray Photoelectron Spectroscopy data, detailing surface elemental composition and oxidation states.</p>
</li>
<li>
<p><strong><code>NMR.xlsx</code>:</strong> Nuclear Magnetic Resonance spectra data.</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>UV-vis.xlsx</code>:</strong> Ultraviolet-Visible diffuse reflectance spectra (UV-vis DRS) and band gap calculations.</p>
</li>
<li>
<p><strong><code>PL.xlsx</code>:</strong> Photoluminescence spectroscopy data used to investigate charge carrier separation/recombination.</p>
</li>
<li>
<p><strong><code>OER.xlsx</code>:</strong> Electrochemical data for the Oxygen Evolution Reaction (OER), including CV curves and stability tests.</p>
</li>
<li>
<p><strong><code>DFT.xlsx</code>:</strong> Data output from Density Functional Theory calculations, including electronic density of states (DOS) and free energy diagrams.</p>
</li>
</ul>
<p><strong>3. Software Requirements:</strong> 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>
<p><strong>Further Details</strong> Users are kindly requested to cite the original article when reusing any part of this dataset.</p>
Inventory of hazardous substance concentrations in different environmental compartments in the Danube river basin
No full text<p>The data set contains an SQL-dump of a PostgreSQL data base. This data base contains concentrations of hazardous substances and other water quality parameters in different environmental compartments:</p><ul><li>river water (water and suspended sediments)</li><li>ground water</li><li>waste water (treated and untreated) and sewage sludge</li><li>storm water runoff from combined and separate sewer systems</li><li>atmospheric deposition</li><li>soil</li></ul><p>Data from many different data sources were collected, cheked and combined and meta data were harmonized to allow for a combined data evaluation.</p><p>The SQL-file was exported from a PostgreSQL 15.2 data base and compressed using 7zip into a zip-file (dhm3c<i>inventory</i>V2.zip). Text-encoding is UTF-8.</p><p>A short documentation (documentation_inventory_db_V2.0.pdf) and a listof known issues with the data which could not be resolved before publication (List_of_known_issues_V2.0.pdf) are enclosed as PDF files.</p><p>This Version 2.0.0 of the database contains more data as for further data sets a publication agreement was reached and some data were reimported to resolve some errors created during data preparation for import. The database structure was extended and corrected at different points, leading to an improved data model.</p>
Interview with Trine Jensen, IAU
No full text<h2>Interview with Trine Jensen</h2>
<p>Trine Jensen leads the work on the strategic priority Higher Education & Digital Transformation at the International Association of Universities (IAU). She is spearheading projects spanning policy-shaping, strategy, advocacy, and monitoring of the digital transformation of HE with global partners and is the author of the IAU Global Monitoring Report on Higher Education in the Digital Era: the Current State of Transformation around the World (January 2020) and leads on the IAU Policy Statement: “Transforming Higher Education for the Global Common Good in a Digital World” in collaboration with an international Expert Advisory Group. In 2019, she launched a new IAU programme entitled: “Institutional site visits” fostering international peer-to-peer learning in relation to digital transformation of higher education institutions. She is also co-editor of the Associations’ magazine IAU Horizons . Finally, she works with the Secretary General on the IAU events and Administrative Board meetings. Trine Jensen worked several years for UNESCO as part of the Bureau for Strategic Planning before she joined the IAU in 2012.<br><br></p>
<h2>IAU 2024 International Conference</h2>
<p>This interview was held ahead of this year’s conference, hosted by Sophia University, which brought together over 200 representatives from institutions across 80 countries to address the theme: "University Values in a Changing World." The conference underscored the pivotal role of core values, highlighting how they guide universities in decision-making, ethical conduct, and meaningful societal engagement. Additionally, sessions explored how these values can be leveraged to address the grand challenges facing societies worldwide.</p>
Efficient synthesis of 2-arylpropionitriles via selective monomethylation of aryl acetonitriles using an easy to handle methylation agent
No full text<p><strong>Analytical Data and Compound Numbering (in paper numbering vs. ELN entries) for the Publication entitled:<br><em>"</em>Efficient synthesis of 2-arylpropionitriles via selective monomethylation of aryl acetonitriles using an easy to handle methylation agent<em>"</em></strong></p>
<p>The paper was published on 2024-09-08 in the European Journal of Organic Chemistry</p>
<p>Eur. J. Org. Chem. 2024, e202400693</p>
<p>DOI: <a href="https://doi.org/10.1002/ejoc.202400693">10.1002/ejoc.202400693</a></p>
<p>Authors: Eleni Papaplioura, Johanna Templ, Nina Wildhack, Michael Schnürc</p>
<p>Funded by the Austrian Science Fund (FWF, project number P33064-N) and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie, Grant Agreement No. 860762</p>
<p><strong>Context and methodology</strong></p>
<p>A convenient and safe methylation protocol employing quaternary ammonium salts (PhMe3NI) as alternative methylating agents for the selective α-methylation of arylacetonitriles is presented. This approach allows for the selective α-methylation of arylacetonitriles, overcoming the limitations of existing techniques, while offering a practical and sustainable solution for late-stage functionalization in medicinal chemistry. The straightforward and safe nature of this methodology makes it particularly well-suited for applications in drug discovery. In our report, we present a diverse set of 18 examples, achieving yields of up to 76%.</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 docx file named Eur. J. Org. Chem. 2024, e202400693_compound number list.docx<strong><em> </em></strong>is uploaded, that should clearly link the compound number given in the paper to the respective entry in the ELN and the respective analytical data files. </p>
<p><strong>Technical details</strong></p>
<p>The files uploaded 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>