TU Wien Research Data
Not a member yet
512 research outputs found
Sort by
Radio telescope dimensions for geodetic and astrometric VLBI thermal expansion modelling
No full text<h2>Description</h2>
<p>The dataset contains the dimensions of structural parts of radio telescopes used for geodetic and astrometric VLBI for the purpose of thermal expansion modelling in geodetic and astrometric VLBI Level-2 data analysis. New version as of 2025-09-17.</p>
<h3>Changes</h3>
<ul>
<li>SEJONG axis offset updated</li>
<li>Entries added for site names existing in the <em>gsfc_itrf2020_v2020Apr14.axo</em><br>even though they may have been used only for experimental purposes: ALASKANO, HARTDBBC, LEFTKOK, NOTDBBC, NOTOVDIF, NOTOX, NYALDBBC, ONSALAAN, ONSAVDIF, WETTVDIF, WIDE85_3, YEBESDBC (always entered below common name). N.B.: Axis offsets corrected by taking them over from telescopes with common names.</li>
</ul>
<h3>Context and methodology</h3>
<ul>
<li>The dataset belongs to publication: A<em>xel Nothnagel (2009) Conventions on thermal expansion modelling of radio telescopes for geodetic and astrometric VLBI; Journal of Geodesy, Vol. 83(3), 787-792, DOI: <a href="https://doi.org/10.1007/s00190-008-0284-z">10.1007/s00190-008-0284-z</a></em>, where the general concept of thermal expansion modelling is described.</li>
<li>The data was collected from on-site personnel who measured the dimensions of the structural parts or extracted them from construction drawings.</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>The data is stored in fixed format ASCII, with the data of each telescope in a single line. The format is described in the header of the file. </li>
<li>No special software is needed to read and use the data.</li>
</ul>
Radio telescope dimensions for geodetic and astrometric VLBI thermal expansion modelling
No full text<h2>Description</h2>
<p>The dataset contains the dimensions of structural parts of radio telescopes used for geodetic and astrometric VLBI for the purpose of thermal expansion modelling in geodetic and astrometric VLBI Level-2 data analysis.</p>
<h3>Context and methodology</h3>
<ul>
<li>The dataset belongs to publication: A<em>xel Nothnagel (2009) Conventions on thermal expansion modelling of radio telescopes for geodetic and astrometric VLBI; Journal of Geodesy, Vol. 83(3), 787-792, DOI: <a href="https://doi.org/10.1007/s00190-008-0284-z">10.1007/s00190-008-0284-z</a></em> where the general concept of thermal expansion modelling is described.</li>
<li>The data was collected from on-site personnel who measured the dimensions of the structural parts or extracted them from construction drawings.</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>The data is stored in fixed format ASCII with the data of each telescope in a single line. The format is described in the header of the file. </li>
<li>No special software is needed to read and use the data.</li>
</ul>
Radio telescope dimensions for geodetic and astrometric VLBI thermal expansion modelling
No full text<h2>Description</h2>
<p>The dataset contains the dimensions of structural parts of radio telescopes used for geodetic and astrometric VLBI for the purpose of thermal expansion modelling in geodetic and astrometric VLBI Level-2 data analysis. The 2025-02-21 version is the pre 2025-01-21 version for ITRF2020-u2025.</p>
<h3>Context and methodology</h3>
<ul>
<li>The dataset belongs to publication: A<em>xel Nothnagel (2009) Conventions on thermal expansion modelling of radio telescopes for geodetic and astrometric VLBI; Journal of Geodesy, Vol. 83(3), 787-792, DOI: <a href="https://doi.org/10.1007/s00190-008-0284-z">10.1007/s00190-008-0284-z</a></em> where the general concept of thermal expansion modelling is described.</li>
<li>The data was collected from on-site personnel who measured the dimensions of the structural parts or extracted them from construction drawings.</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>The data is stored in fixed format ASCII with the data of each telescope in a single line. The format is described in the header of the file. </li>
<li>No special software is needed to read and use the data.</li>
</ul>
Forschungsinfrastruktur in Österreich
No full text<p>Die Forschungsinfrastruktur-Datenbank ist Open for Collaboration und bietet eine Informationsplattform zu Forschungsinfrastrukturen in Wissenschaft, Forschung und Wirtschaft. Durch die Datenbank lassen sich kooperationsfähige Forschungsinfrastrukturen finden oder auch anbieten. Anhand der Forschungsinfrastruktur-Datenbank wird die Gesamtentwicklung öffentlich sichtbarer und kooperationsfähiger Forschungsinfrastrukturen in Österreich im Zeitraum von Anfang 2016 bis Anfang 2025 als Infografik dargestellt.</p>
RAAV - Model logic and implementation structure of PT-STA and CSTA
No full text<p><strong>Context and methodology</strong><br>This dataset was created in the context of the RAAV research project (Rural Accessibility and Automated Vehicles), funded by the FWF (I 5224), in collaboration between TU Wien and Eurac Research. The project explores how automated public transport solutions can enhance accessibility in rural areas. The dataset serves as a conceptual and technical reference for two accessibility models developed during the project: <strong>PT-STA</strong> (Public Transport Space-Time Accessibility) and <strong>CSTA</strong> (Collective Space-Time Accessibility). The dataset was compiled based on the model design and implementation in ArcGIS ModelBuilder and documents all relevant model components, inputs, processing steps, and output structures used in RAAV’s accessibility analysis.</p>
<p><strong>Technical details</strong><br>The dataset consists of a single Excel file (RAAV_model_details.xlsx) with multiple sheets. It outlines:</p>
<ul>
<li>
<p>Required model inputs (e.g. stop locations, population data, time windows)</p>
</li>
<li>
<p>Step-by-step logic of spatial and temporal calculations</p>
</li>
<li>
<p>Structural comparison of the PT-STA and CSTA model layers</p>
</li>
<li>
<p>Variable descriptions and modelling assumptions</p>
</li>
</ul>
<p>There is <strong>no specific software requirement</strong> to open the file (standard spreadsheet reader is sufficient). However, the models described in the file were implemented in <strong>ArcGIS ModelBuilder</strong>, which is required for full replication. No code or geoprocessing scripts are included in this file, but the logic can be adapted to other platforms.</p>
<p><strong>Further details</strong><br>This documentation is intended to support reuse and transfer of the model structure to other research projects or practical applications in accessibility planning. It complements RAAV’s publicly available datasets and publications.<br>If reused, please cite the dataset using the DOI and acknowledge the RAAV project and TU Wien as the source.</p>
Domänenwissen - Data Stewardship Training an der Universität Graz
Get PDF<p>Das Poster stellt das Data Stewardship-Zertifikatsprogramm der Universität Graz vor, das Fachkräfte im professionellen Umgang mit Forschungsdaten ausbildet. Es betont die zentrale Rolle von Data Stewards bei der Qualitätssicherung, Organisation und Nachvollziehbarkeit wissenschaftlicher Daten, um Forschende effektiv zu unterstützen. Das Programm vermittelt praxisnahe Kompetenzen zu Themen wie Datenmanagement, IT-Grundlagen und rechtlichen Aspekten und richtet sich an zahlreiche Disziplinen. Ziel ist es, die Zuverlässigkeit und Effizienz wissenschaftlicher Forschung durch qualifiziertes Datenmanagement zu steigern.</p><p>The poster presents the Data Stewardship certificate program at the University of Graz, which trains professionals in the professional handling of research data. It emphasizes the central role of Data Stewards in ensuring quality, organization, and traceability of scientific data to effectively support researchers. The program imparts practical skills on topics such as data management, IT fundamentals, and legal aspects, and is aimed at a wide range of disciplines. The goal is to increase the reliability and efficiency of scientific research through qualified data management.</p>
In vitro and in vivo characterization of a new-to-nature pathway for formaldehyde assimilation in methylotrophic yeast Komagataella phaffii
Get PDF<p>This repository contains the raw data, processed data used for analysis, and the scripts for generating the figures and tables presented in manuscript, <strong><em><span lang="EN-US">In vitro</span></em></strong><strong><span lang="EN-US"> and <em>in vivo</em> characterization of a new-to-nature pathway for formaldehyde assimilation in methylotrophic yeast <em>Komagataella phaffii. </em></span></strong></p><p><span lang="EN-US">Formolase (FLS) is the first synthetic enzyme to catalyse the formose reaction, wherein formaldehyde is converted to dihydroxyacetone (DHA). It is thus uniquely suited for the construction of synthetic methanol assimilation cascades, proceeding via methanol oxidation to formaldehyde condensation to DHA, and finally ATP-dependent conversion to dihydroxyacetonephosphate (DHAP). Compared to the native xylulose monophosphate (XuMP) cycle of methylotrophic yeasts, this pathway produces DHAP in fewer catalytic steps, without the need for acceptor recycling and at the cost of less ATP. Here, we implement FLS-based formaldehyde assimilation in <em>Komagataella phaffii</em><span>, a methylotrophic yeast used on an industrial scale to produce bioproducts, particularly proteins. </span>To this end, an optimized FLS gene with a peroxisomal targeting signal (PTS1) under the control of a methanol-inducible promoter was integrated into the genome of a XuMP-deficient <em>K. phaffii</em> strain<span>. Transformants with high copy numbers of the FLS gene (11 ± 1) produced up to 53.65 ± 2.15 µM.min<sup>-1 </sup>DHA in cell-free extract (CFE). </span>In the fed-batch phase of cultivations on methanol feed, the FLS-producing strain showed a biomass yield on methanol of 0.27 ± 0.09 g.g<sup>-1</sup> and a biomass formation rate of 0.0113 g.h<sup>-1</sup>. This work lays the foundation for the implementation of a more energy-efficient methanol assimilation pathway as the basis for sustainable bioproduction in yeasts. </span></p>
ESA CCI SM PASSIVE Daily Gap-filled Root-Zone Soil Moisture from merged multi-satellite observations
No full text<p>This dataset provides global daily estimates of <strong>Root-Zone Soil Moisture (RZSM)</strong> content at 0.25° spatial grid resolution, derived from gap-filled merged satellite observations of 14 passive satellites sensors operating in the microwave domain of the electromagnetic spectrum. Data is provided from January 1991 to December 2023.</p>
<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>. Operational implementation is supported by the Copernicus Climate Change Service implemented by ECMWF through C3S2 312a/313c.</p>
<h2>Studies using this dataset [preprint]</h2>
<p>This dataset is used by <em>Hirschi et al. (2025)</em> to assess recent summer drought trends in Switzerland.</p>
<p><em>Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., and Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on measurements from the SwissSMEX network, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2025-416" target="_blank" rel="noopener">https://doi.org/10.5194/essd-2025-416</a>, in review, 2025. </em></p>
<h2>Abstract</h2>
<p>ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations from various microwave satellite remote sensing sensors (<em>Dorigo et al., 2017, 2024; Gruber et al., 2019</em>). This version of the dataset uses the PASSIVE record as input, which contains only observations from passive (radiometer) measurements (scaling reference AMSR-E). The surface observations are gap-filled using a univariate interpolation algorithm (<em>Preimesberger et al., 2025</em>). The gap-filled passive observations serve as input for an exponential filter based method to assess soil moisture in different layers of the root-zone of soil (0-200 cm) following the approach by <em>Pasik et al. (2023)</em>. The final gap-free root-zone soil moisture estimates based on passive surface input data are provided here at 4 separate depth layers (0-10, 10-40, 40-100, 100-200 cm) over the period 1991-2023.</p>
<h3>Summary</h3>
<ul>
<li>Gap-free root-zone soil moisture estimates from 1991-2023 at 0.25° spatial sampling from passive measurements</li>
<li>Fields of application include: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, agriculture and meteorology</li>
<li>More information: See <em>Dorigo et al. (2017, 2024) </em>and <em>Gruber et al. (2019)</em> for a description of the satellite base product and uncertainty estimates, <em>Preimesberger et al. (2025) </em>for the gap-filling, and <em>Pasik et al. (2023) </em>for the root-zone soil moisture and uncertainty propagation algorithm.</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>~/Downloads</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.ac.at/records/8dda4-xne96/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>ESA_CCI_PASSIVERZSM-YYYYMMDD000000-fv09.1.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>rzsm_1</strong>: (float) Root Zone Soil Moisture at 0-10 cm. Given in volumetric units [m3/m3].</li>
<li><strong>rzsm_2</strong>: (float) Root Zone Soil Moisture at 10-40 cm. Given in volumetric units [m3/m3].</li>
<li><strong>rzsm_3:</strong> (float) Root Zone Soil Moisture at 40-100 cm. Given in volumetric units [m3/m3].</li>
<li><strong>rzsm_4</strong>: (float) Root Zone Soil Moisture at 100-200. Given in volumetric units [m3/m3].</li>
<li><strong>uncertainty_1</strong>: (float) Root Zone Soil Moisture uncertainty at 0-10 cm from propagated surface uncertainties [m3/m3].</li>
<li><strong>uncertainty_2</strong>: (float) Root Zone Soil Moisture uncertainty at 10-40 cm from propagated surface uncertainties [m3/m3].</li>
<li><strong>uncertainty_3</strong>: (float) Root Zone Soil Moisture uncertainty at 40-100 cm from propagated surface uncertainties [m3/m3].</li>
<li><strong>uncertainty_4</strong>: (float) Root Zone Soil Moisture uncertainty at 100-200 cm from propagated surface uncertainties [m3/m3].</li>
</ul>
<p>Additional information for each variable is given in the netCDF attributes.</p>
<h3>Version Changelog</h3>
<ul>
<li>v9.1
<ul>
<li>Initial version based on PASSIVE input data from ESA CCI SM v09.1 as used by <em>Hirschi et al. (2025)</em>.</li>
</ul>
</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><span lang="EN-GB">Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 10.1016/j.rse.2017.07.001, 2017</span></li>
<li>Dorigo, W., Stradiotti, P., Preimesberger, W., Kidd, R., van der Schalie, R., Frederikse, T., Rodriguez-Fernandez, N., & Baghdadi, N. (2024). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 09.0. Zenodo. <a href="https://doi.org/10.5281/zenodo.13860922" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13860922</a></li>
<li>Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, <a href="https://doi.org/10.5194/essd-11-717-2019">https://doi.org/10.5194/essd-11-717-2019</a>, 2019.</li>
<li>Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on the SwissSMEX network, 2025 (paper submitted)</li>
<li>Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, <a href="https://doi.org/10.5194/gmd-16-4957-2023,%202023">https://doi.org/10.5194/gmd-16-4957-2023, 2023</a></li>
<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 Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2024-610" target="_blank" rel="noopener">https://doi.org/10.5194/essd-2024-610</a>, in review, 2025.</li>
</ul>
<h2>Related Records</h2>
<p>Please see the <a href="../communities/soilmoisture-climaterecords/records?q=&l=list&p=1&s=10&sort=newest">ESA CCI Soil Moisture science data records</a> community for more records based on ESA CCI SM.</p>
<p> </p>
Shared Competences im Projekt "Shared RDM Services & Infrastructure"
Get PDF<p><span>Poster zu Shared Competences im Projekt „Shared RDM Services & Infrastructure“ präsentiert auf der Cluster Forschungsdaten Expo 2025 an der TU Wien am 16.01.2025.</span></p>
Dataset of micropollutant concentrations and standard water quality parameters in surface waters in Austria
No full text<h2><strong>Pre-release (Beta):</strong> Dataset of micropollutant concentrations and standard water quality parameters in surface waters in Austria</h2>
<p>Initial release of the dataset with some known issues.</p>
<h3>ABSTRACT</h3>
<p>This dataset includes micropollutant concentrations (up to 450 substances) and standard water quality parameters analysed in samples from two water quality monitoring stations in eastern Austria. Samples were collected eighter as grab samples or as multi-day composite samples from June 2023 to June 2024. </p>
<h3>DESCRIPTION OF THE DATA</h3>
<p>This dataset accompanies a publication where details regarding methods can be found.</p>
<ul>
<li>The following groups of micropollutants were analysed all of the samples:
<ul>
<li>8 metals total and filtered</li>
<li>4 pharmaceuticals</li>
<li>34 PFAS</li>
<li>404 pesticides</li>
</ul>
</li>
<li>Additionally, electric onductivity, dissolved oxygen, temperature, filterable matter, pH and total hardness are included in some or all of the samples.</li>
<li>The Wulka River is a medium-sized river in eastern Austria and the only significant tributary to Lake Neusiedl.</li>
<li>The analytical thresholds are reported with the data: Level of Detection (LOD) and Level of Quantitation (LOQ).</li>
<li>A description of the information contained in the individual columns is given below. </li>
</ul>
<h3>KNOWN ERRORS</h3>
<ul>
<li><strong>Error 1</strong>: Missing dissolved PTE sample "W-13-VC-F" due to mislabeling as total PTE for "W-13-VC-T".
<ul>
<li><strong>Affected Columns</strong>: ["Sample.Code"]</li>
<li><strong>Impact</strong>: Mislabelled sample data, which could lead to incorrect analysis or conclusions.</li>
<li><strong>Resolution in Version 1.0</strong>: The dissolved PTE sample "W-13-VC-F" has been correctly labeled and included in the dataset.</li>
</ul>
</li>
<li><strong>Error 2</strong>: Incorrect CAS numbers for "AMPA" and "PFPeS".
<ul>
<li><strong>Affected Columns</strong>: ["CAS"]</li>
<li><strong>Impact</strong>: Incorrect chemical identification, which could affect research or regulatory compliance.</li>
<li><strong>Resolution in Version 1.0</strong>: The CAS number for "AMPA" has been corrected to "1066-51-9" and for "PFPeS" to "2706-91-4".</li>
</ul>
</li>
</ul>
<h3>REFERENCES</h3>
<ul>
<li>The data is stored in comma-delimited csv-files with UTF-8 encoding. </li>
<li>The point is used as the decimal separator.</li>
<li>Time is given in UTC format (yyyy-mm-dd HH:MM:SS).</li>
</ul><h3>Struture of the dataset (water_data.csv)</h3>
<ul>
<li>Group: Substance/parameter group</li>
<li>Parameter: Abbreviation of the substance/parameter analysed</li>
<li>Parameter.en: Full name of the substance/parameter analysed</li>
<li>CAS: CAS registry number</li>
<li>Site: Sampling site identifier</li>
<li>Sampling.Start.Time: Start of sampling (yyyy-mm-dd HH:MM:SS)</li>
<li>Sampling.End.Time: End of sampling (yyyy-mm-dd HH:MM:SS)</li>
<li>Value: Measured value (Caution: Values below the limit of quantification (<LOQ) were replaced by LOQ & Values below the limit of detection (<LOD) were replaced by LOD)</li>
<li>Unit: Unit of the measured value and LOQ</li>
<li>belowLOD: Indication if measured value was below LOD (TRUE) or not (FALSE)</li>
<li>LOD: Limit of detection</li>
<li>belowLOQ: Indication if measured value was below LOQ (TRUE) or not (FALSE)</li>
<li>LOQ: Limit of quantification</li>
<li>Sample.Type: Type of sample</li>
<li>Sample.Code: Unique sample identifier (W = Wulka, N = Nodbach, ADD = Triplicates)</li>
<li>Triplet.Sample.Code: sample identifier for the triplet samples for lab uncertainty estimation</li>
<li>Analysis.Matrix: Analysis matrix</li>
<li>Analysis.Method: (Lab) analysis method</li>
<li>Comment</li>
<li>meanQ: Mean discharge during sampling [m³/s]</li>
<li>meanTSS: Mean total suspended solids during sampling [mg/l] (measured by turbidity probe)</li>
<li>meanTSS.fw: Mean flow-weighted total suspended solids during sampling [mg/l] (measured by turbidity probe)</li>
</ul>