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Dataset of Publication "Malware Communication in Smart Factories: A Network Traffic Data Set"
No full text<p><strong>Note: If you use this dataset, please cite the following paper: </strong></p>
<p>Brenner, B., Fabini, J., Offermanns, M., Semper, S., & Zseby, T. (2024). Malware communication in smart factories: A network traffic data set. Computer Networks, 255, 110804.</p>
<p>or in BibTeX:</p>
<p>@article{brenner2024malware,<br> title={Malware communication in smart factories: A network traffic data set},<br> author={Brenner, Bernhard and Fabini, Joachim and Offermanns, Magnus and Semper, Sabrina and Zseby, Tanja},<br> journal={Computer Networks},<br> volume={255},<br> pages={110804},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
<p> </p>
<h3>Context and methodology</h3>
<p>Machine learning-based intrusion detection requires suitable and realistic data sets for training and testing. However, data sets that originate from real networks are rare. Network data is considered privacy-sensitive, and the purposeful introduction of malicious traffic is usually not possible.</p>
<p>In this paper, we introduce a labeled data set captured at a smart factory located in Vienna, Austria, during normal operation and during penetration tests with different attack types. The data set contains 173 GB of PCAP files, representing 16 days (395 hours) of factory operation. It includes MQTT, OPC UA, and Modbus/TCP traffic.</p>
<p>The captured malicious traffic originated from a professional penetration tester who performed two types of attacks:<br>(a) Aggressive attacks that are easier to detect.<br>(b) Stealthy attacks that are harder to detect.</p>
<p>Our data set includes the raw PCAP files and extracted flow data. Labels for packets and flows indicate whether they originated from a specific attack or from benign communication. </p>
<p>We describe the methodology for creating the dataset, conduct an analysis of the data, and provide detailed information about the recorded traffic itself. The dataset is freely available to support reproducible research and the comparability of results in the area of intrusion detection in industrial networks.</p>
<p> </p>
<h3>Technical details</h3>
<ul>
<li>readme.txt
<ul>
<li>Information about the data collection, format, necessary software and versions to access it.</li>
</ul>
</li>
<li>license.txt:
<ul>
<li>Licensing information.</li>
</ul>
</li>
<li>a_day1, a_day2, s_day1, s_day2, tf_a, and tf_s:
<ul>
<li> Main dataset, where files starting with "tf" are training files containing only benign, <br> operational data. All other files are attack files containing both operational data and <br> attack data.</li>
</ul>
</li>
<li>images.zip:
<ul>
<li>Contains descriptive images about the data.</li>
</ul>
</li>
<li>extractions.zip:
<ul>
<li>Contains extracted packets and flows in both labeled and unlabeled form.</li>
</ul>
</li>
<li>a_day_tuesday_dos.zip:
<ul>
<li>An additional day of attack traffic containing benign and attack data, including a DoS attack. This day is not labeled.</li>
</ul>
</li>
<li>list_of_extracted_features:
<ul>
<li>A complete list of features we extracted from the PCAP Files. All flow files contain these features.</li>
</ul>
</li>
<li>list_of_identified_protocols.csv:
<ul>
<li>A complete list of all protocols that we could identify within the PCAP files provided.</li>
</ul>
</li>
</ul>
<div> </div>
Photocatalytic Upcycling of PET into Methane, Hydrogen and High-value Liquid Products
No full text<h2>Raw data for "Photocatalytic Upcycling of PET into Methane, Hydrogen and High-value Liquid Products"</h2>
<p>This publication investigates the photocatalytic conversion of p<span>olyethylene terephthalate (</span>PET) microplastic powder using a P25 TiO2 photocatalyst. Hereby, the effect of a Pt co-catalyst, temperature, atmosphere and light source on the resulting gas- and liquid phase products, as well as the underlying mechanism, are elucidated. </p>
<p>The uploaded files ("Figure 1" and "Figure 3+4") summarise the raw data which is graphically shown in the respective figures. <br>These files include data obtained from <em>Total X-ray fluroescence spectroscopy</em> (TXRF), <em>Diffuse reflectance spectroscopy</em> (DRS) and subsequent <em>Kubelka Munk </em><em>analysis (KM)</em>, P<em>owder X-ray diffraction</em> (XRD), P<em>hotoluminescence spectroscopy</em> (PL) and <em>Nucelar magnetic resonance</em> (NMR) measurements, all of which are presented and discussed in the manuscript.<br>"Figure 1" focuses on the characterisation of the P25 (neat) and P25-Pt (with Pt co-catalyst) photocatalyst powder.<br>"Figure3+4" refers to data collected from samplers after irradiation. For the NMR measurement, the P25-Pt photocatalyst (0.5mg/mL) and the PET powder (1mg/mL) were added to a 1M NaOH solution and irradiated at 70°C. PL measurements were performed on solutions obtained after irradiation (at room temperature and under He (inert) atmosphere) , either without (see "P25-Pt") or with PET (see "PET+P25-Pt").</p>
<p>Further results can be found in the supplementary of the publication.</p>
<p> </p>
<p> </p>
<p> </p>
Mechanically Induced Sequential One-Pot Wittig Olefination–Diels-Alder Reaction: A Solvent-Free Approach to Complex Bicyclic Scaffolds
Full text linkoai:researchdata.tuwien.ac.at:ab694-b9p15<h3><strong>Analytical Data and Compound Numbering (numbering in publication vs. ELN entry) for the Publication entitled: <em>"Mechanically Induced Sequential One-Pot Wittig Olefination–Diels-Alder Reaction: A Solvent-Free Approach to Complex Bicyclic Scaffolds"</em></strong></h3>
<p>The paper was published on 2025-08-26 in RSC Meachnochemistry.</p>
<p><em>RSC Mechanochem.</em> <strong>2025</strong>, DOI: 10.1039/D5MR00087D.</p>
<p>Authors: Nina Biedermann and Michael Schnürch</p>
<h3>Context and methodology</h3>
<p>Herein we present a mechanically induced, solvent-free protocol that sequentially combines the Wittig olefination and Diels-Alder cycloaddition in one-pot and enables the synthesis of structurally complex bicyclic compounds. This method proceeds entirely under ball milling conditions without the requirement of any solvent while eliminating the need for intermediate purification. Careful optimization of the milling parameters and reagent addition enables efficient conversion of various α,β-unsaturated aldehydes and ketones with electron-deficient dienophiles to the corresponding cycloadducts via diene intermediates, demonstrating high stereoselectivity and yielding exclusively endo Diels-Alder adducts. Furthermore, the extension of the sequence by a solvent-free one-pot oxidation is exemplified, achieving a three-step synthesis in a single milling vessel without intermediate workup and purification, which exhibits excellent green metrics in comparison with solution-based methods. This operationally simple and sustainable approach demonstrates the potential of mechanochemistry to streamline multistep organic synthesis, while reducing solvent use and energy demand.</p>
<p>The publication and its Supplementary Information can be found as open-access files on the publisher's website (see DOI).</p>
<p>All files containing the analytical raw data for all compounds given in the Supplementary Information of the manuscript are uploaded. An additional file named<strong><em> RSC_Mechanochem_2025-compound_list.pdf </em></strong>is uploaded, that should clearly link the compound number given in the paper to the respective entry in the ELN (nbiederm) and the respective analytical data file.</p>
<h3>Technical details</h3>
<p>The files uploaded contain the FIDs of NMR spectra recorded by an in-house Bruker Spectrometer. Standard software (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>) is required to display NMR spectra.</p>
ESA CCI SM MEDIUM RESOLUTION (0.1°) 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 4 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>Abstract</h2>
<p>The ESA CCI Soil Moisture medium-resolution (MR) science product provides soil moisture at a finer spatial resolution of 0.1° x 0.1°. The MR product delivers COMBINED SSM data for the time period 2002-2024. The production of the MR product follows the same methodology as the main CCI product, with adaptations introduced to account for the higher resolution. A key objective of the MR product is to better capture mesoscale SSM patterns, because the variability at these scales plays an important role in triggering convective storms through land–atmosphere interactions (Chug, 2023). Particularly, SSM contrasts over distances of 10 to 40 km have a strong influence on the initiation of mesoscale convective systems (MCSs) in the Sahel (Taylor, 2011), where such systems account for most of the annual rainfall (Mathon, 2002). The main CCI product at 0.25° resolution has proven to be too coarse for resolving the land surface features to convective system initiations.</p>
<h3>Summary</h3>
<ul>
<li>Daily global estimates of volumetric surface soil moisture from 2002-2024 at 0.1° resolution</li>
<li>Based on a selection of active (ASCAT) and passive sensors (AMSR-E, AMSR-2, GMI, FengYun, SMOS and SMAP).</li>
<li>0.1° resolution obtained using a nearest neighbour resampling (passive) or a two-dimensional Hamming window approach (active)</li>
<li>For more information see: ATBD (link coming soon..) </li>
</ul>
<h2>Programmatic (bulk) download</h2>
<p>You can use command-line tools such as <a href="https://www.gnu.org/software/wget/" target="_blank" rel="noopener">wget</a> or <a href="https://curl.se/" target="_blank" rel="noopener">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>
<p>#!/bin/bash</p>
<p># Set download directory<br>DOWNLOAD_DIR=~/Downloads</p>
<p>base_url="https://researchdata.tuwien.at/records/k32ss-1kh79/files"</p>
<p># Loop through years 2002 to 2024 and download & extract data<br>for year in {2002..2024}; 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</p>
</blockquote>
<h2>Data details</h2>
<div>
<h3>Filename template</h3>
<p>The dataset provides global daily estimates for the 2002-2024 period at 0.1° (~10 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:</p>
<blockquote>
<p>ESACCI-SOILMOISTURE-L3S-MR-COMBINED-YYYYMMDD000000-fv9.2.nc</p>
</blockquote>
</div>
<h3>Data Variables</h3>
<p>Each netCDF file contains 3 coordinate variables</p>
<ul>
<li><strong>lon</strong>: longitude (WGS84), [-180,180] degree W/E</li>
<li><strong>lat</strong>: latitude (WGS84), [-90,90] degree N/S</li>
<li><strong>time: </strong>float, datetime encoded as "number of days since 1970-01-01 00:00:00 UTC"</li>
</ul>
<p> and 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 (0.1 degree).</li>
<li><strong>sm_uncertainty</strong>: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations. Derived using triple collocation analysis.</li>
<li><strong>dn_flag</strong>: (int) Indicator for satellite orbit(s) used in the retrieval (day/nighttime). 1=day, 2=night, 3=both</li>
<li><strong>flag</strong>: (int) Indicator for data quality / missing data indicator. For more details, see netcdf attributes.</li>
<li><strong>freqbandID</strong>: (int) Indicator for frequency band(s) used in the retrieval. For more details, see netcdf attributes.</li>
<li><strong>mode</strong>: (int) Indicator for satellite orbit(s) used in the retrieval (ascending, descending)</li>
<li><strong>sensor</strong>: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.</li>
<li><strong>t0</strong>: (float) Representative time stamp, based on overpass times of all merged satellites.</li>
</ul>
<p>Additional information for each variable are given in the netCDF attributes.</p>
<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>
<h2>References</h2>
<p><em>Chug, D., Dominguez, F., Taylor, C.M., Klein, C. and Nesbitt, S.W., 2023. Dry-to-wet soil gradients enhance convection and rainfall over subtropical South America. Journal of Hydrometeorology, 24(9), pp.1563-1581.</em></p>
<p><em>Taylor, C.M., Gounou, A., Guichard, F., Harris, P.P., Ellis, R.J., Couvreux, F. and De Kauwe, M., 2011. Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns. Nature Geoscience, 4(7), pp.430-433.</em></p>
<p><em>Mathon, V., Laurent, H. and Lebel, T., 2002. Mesoscale convective system rainfall in the Sahel. Journal of applied meteorology, 41(11), pp.1081-1092.</em></p>
K21-1063
No full text<h2>Related work</h2>
<p>This dataset is part of the digital documentation of the Tomb of Meret Neith, Umm el Qaab, Abydos, Egypt about 3000BC, its artefacts, and the reconstruction of the tomb. For an overview of the related work, please visit <a href="https://researchdata.tuwien.at/communities/meretneith/">https://researchdata.tuwien.at/communities/meretneith/</a>.</p>
<h2>Archaeological information</h2>
<h3>Object name</h3>
<p>Inscribed sealing fragment</p>
<h3>Object number(s)</h3>
<p>LN1126; K21-1063</p>
<h3>Description</h3>
<p>inscribed clay sealing fragment, impressed with seal of Meret-Neith</p>
<h3>Location of object at time of photographs</h3>
<p>MoTA storage</p>
<h3>Find location</h3>
<p>Tomb Y; L7 S2</p>
<h3>Bibliography</h3>
<p>N/A</p>
<h2>Technical Information</h2>
<p>For imaging, Canon RAW and Apple RAW images were captured.</p>
<h3>Physical properties</h3>
<table>
<tbody>
<tr>
<td>Length (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Width (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Height (cm)</td>
<td>N/A</td>
</tr>
<tr>
<td>Weight (g)</td>
<td>N/A</td>
</tr>
<tr>
<td>Volume (cm3)</td>
<td>N/A</td>
</tr>
<tr>
<td>Density (g/cm3)</td>
<td>N/A</td>
</tr>
</tbody>
</table>
<h3>Comments</h3>
<p>no photogrammetry, only images</p>
<h3>Files overview</h3>
<ul>
<li><b>images_videos.zip</b> contains photographs of the real object in RAW format.</li>
<li>[optional] <b>exports_{H,M,L}Q.zip</b> archives contain the scanned object as 3D model in OBJ format including MTL and texture files. HQ, MQ, and LQ refer to high, medium and low quality versions of the model.</li>
<li>[optional] <b>{objectName}_report.pdf</b> contains further technical information of the reconstruction process.</li>
</ul>
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 4 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>Known issues</h2>
<blockquote>
<p>Please note the following issue in this version of the dataset</p>
</blockquote>
<p>The location order of the mask of potentially frozen regions ("frozenmask") for the year 2024 can be wrong (as can clearly be seen when visualizing the "frozenmask" field for a random day in 2024). As a result, the wrong interpolation algorithm (DCT-PLS vs linear interpolation) may be used for the misclassified regions in the gap-filling process (linear interpolation is used for frozen conditions). This affects only regions where in a 5x5 degree region both frozen and unfrozen soil conditions appeared. Spatial subsets, where all locations are frozen or non-frozen, are not affected. Other years (before 2024) are not affected. Please contact us if you require more information.</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 v9) 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 1979-2024 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 (Bulk) 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 (<strong>~50 GB</strong>).</p>
<blockquote>
<div>
<pre>#!/bin/bash<br><br># Set download directory<br>DOWNLOAD_DIR=~/Downloads<br><br>base_url="https://researchdata.tuwien.at/records/c0wbr-xf278/files"<br><br># Loop through years 1979 to 2024 and download & extract data<br>for year in {1979..2024}; 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 1979-2024 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-fv09.2.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_original</strong>: Original measurements (with observation gaps) before gap-filling.</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>v9.2</em> (previous version was <em>v09.1r1</em>):</p>
<ul>
<li>Now based on the v9.2 COMBINED product</li>
<li>Years 1979 to 1990, and 2024 are now included as well</li>
<li>Algorithm is the same as described in Preimesberger et al. (2025)</li>
<li>sm_anomaly field is replaced with sm_original</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>
<p>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. </p>
<h2>Related Records</h2>
<p>This record and all related records are part of the<a href="https://researchdata.tuwien.ac.at/communities/soilmoisture-climaterecords/records"> ESA CCI Soil Moisture science data records </a>community.</p>
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><span>This is </span><strong><span>Version 3</span></strong><span> 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 contains photos of <strong>306</strong></span><strong><span> types of waste container systems</span></strong><span> (over 700 individual containers) installed in public and semi-public spaces, such as parks, shopping streets, libraries, train stations, museums, shopping centres, universities, hotels, and cinemas, from </span><strong><span>over 120 cities in 29 countries</span></strong><span>.</span></p>
<p><span>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.</span></p>
<p><span>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</span></p>
Data and Code for npj climate and atmospheric science article "Incomplete mass closure in atmospheric nanoparticle growth"
No full text<h2>Dataset and code for article "Incomplete mass closure in atmospheric nanoparticle growth" </h2>
<p>The article is published in npj climate and atmospheric science under the DOI: <a href="https://doi.org/10.1038/s41612-025-00893-5">10.1038/s41612-025-00893-5</a>.</p>
<h3>Context and methodology</h3>
<ul>
<li>The dataset is atmospheric science data.</li>
<li>The dataset contains new particle formation and growth data collected in Hyytiälä, Finland, Beijing, China, and Po-Valley Italy as well as laboratory data from the CERN CLOUD experiment.</li>
<li>The dataset stores the most relevant data for the above referenced publication and provides the code for recreating the main figures of the manuscript.</li>
<li>After data collection the raw data was analyzed as described in the publication. In this repository, the processed data related to the three main Figures of the article is stored.</li>
</ul>
<h3>Technical details</h3>
<p>There are two zip files in this dataset. One for the data and code related to the figures and one for the aerosol growth model which is the basis for many calculations within the analysis workflow.</p>
<ul>
<li><strong>figure-data-and-code-v1.0.0.zip</strong> contains three python files to recreate the main figures of the mansucript. In the subfolder <strong>./data</strong> all relevant processed data is stored.</li>
<li><strong>aerosolpy-release-v1.0.1.zip</strong> contains the python package aerosolpy (<a href="https://github.com/DominikStolzenburg/aerosolpy">https://github.com/DominikStolzenburg/aerosolpy</a>) which was used for the aerosol growth model simulations to create the figures in the above referenced manuscript. The zip file contains a complete image of the python module at the moment of the publication of the article. For follow ups refer to the GitHub page.</li>
</ul>
<h3>Licenses</h3>
<p>The data is licensed under CC-BY, the code is licensed under MIT.</p>
How the anisotropy of surface oxide formation influences the transient activity of a surface reaction - Dataset
No full text<p>Metadata for the open access publication <strong>"How the anisotropy of surface oxide formation influences the transient activity of a surface reaction"</strong> (https://doi.org/10.1038/s41467-020-20377-9)</p>
<p>A complete list of dataset files can be seen in "filelist.txt". The files contain the data that generated the figures in the referenced publication and are structured according their order of appearence in the publication. The dataset contains the following file types:</p>
<ul>
<li>standart image files (bmp, tif)</li>
<li>SPEM spectroscopy data (hdf, tsf)</li>
<li>PEEM video data (his, ROI)</li>
<li>QMS mass spectrometry data (SI-d)</li>
</ul>
<p>Additional context, methodology, and technical details is provided in the publication and its supporting information.</p>
Data for Publication: Iontronic Click-to-Release Enables Electrically Controlled Delivery of Drugs and Biomolecules Beyond Charge and Size Limitations
No full text<h3>Context and methodology</h3>
<p>This dataset was generated within a research project investigating the combination of iontronic delivery systems with bioorthogonal click-to-release chemistry to enable electronically controlled release of drugs and biomolecules. The corresponding peer-reviewed <a href="https://doi.org/10.21203/rs.3.rs-7520981/v1" target="_blank" rel="noopener">publication</a> describes the scientific background, experimental design, and conclusions in detail, while the Supporting Information provides extended methodological and analytical context. The dataset contains selected, curated data underlying the published figures, as well as raw NMR data supporting the structural characterization of all synthesized compounds.</p>
<p>The purpose of this dataset is to document the experimental data underlying the key findings reported in the associated publication. It supports results related to iontronic transport of charged tetrazines, electronically triggered click-to-release reactions, controlled release of both small-molecule and macromolecular payloads, and the structural characterization of all synthesized compounds by NMR spectroscopy.</p>
<p>The data were generated using established experimental techniques, including fabrication and operation of iontronic pumps, electrochemical measurements, fluorescence-based quantification assays, chromatographic analyses (HPLC/LC-MS), NMR spectroscopy for compound characterization, protein analysis by SDS–PAGE, and cell-based viability assays. Detailed experimental procedures and conditions are described in the accompanying publication and Supporting Information.</p>
<h3>Technical details</h3>
<p>The dataset consists of a structured Excel file containing selected quantitative data used for the generation of all figures presented in the associated manuscript. Individual worksheets correspond to specific figures or experimental sections, enabling straightforward mapping between the dataset and the published results.</p>
<p>In addition, the dataset includes raw NMR spectroscopy data (FID files) for all compounds reported in the associated publication. The NMR data are provided as obtained directly after measurement and allow independent reprocessing using commonly used NMR software such as Mnova or TopSpin. Files and folders are named according to the original experiments.</p>
<p>No proprietary data formats are used. The dataset can be accessed and analyzed using standard spreadsheet software (e.g., Microsoft Excel or equivalent open-source alternatives) and standard NMR processing software, respectively.</p>
<p>All additional experimental details, compound characterization, kinetic analyses, and supplementary datasets required for full interpretation of the data are provided in the Supporting Information associated with the <a href="https://doi.org/10.21203/rs.3.rs-7520981/v1" target="_blank" rel="noopener">publication</a>.</p>
<p>Assignment of NMR data to specific compounds and compound numbers is described in the associated publication and Supporting Information.</p>
<p>¹H and ¹³C NMR spectra were recorded on Bruker Avance UltraShield 400 MHz and Bruker Ascend 600 MHz spectrometers at 20 °C.</p>
<h3>Further details</h3>
<p>The data are suitable for reuse in comparative studies, methodological benchmarking, meta-analyses, or educational contexts, provided that appropriate attribution to the original <a href="https://doi.org/10.21203/rs.3.rs-7520981/v1" target="_blank" rel="noopener">publication</a> is given.</p>