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Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings"
No full text<h2>AFM Statistical data extraction for insect wing nano topology</h2><p>Supplementary data for "Verifying antibacterial properties of nanopillars on cicada wings" (1.0.0) [Data set]. TU Wien. https://doi.org/10.1007/s13204-024-03030-5</p><p>Extract the zip file, there the files with the extension ".ibw" are the original binary AFM scans the ".txt" variant of the file is the same file saved as the ASCII data matrix from Gwyddiom. The _xy.txt file stores the pillar's top positions from Fiji see https://doi.org/10.34726/hss.2022.99685 for the description of how to export this file from Fiji. The files with the extensions "_xy.csv", "_xy_normal.txt" and "_z.csv" are generated with the python script outing the command "python3 histogram.py" in the python directory. </p><p>The last execution was with python3-numpy (1.21.5-1ubuntu22.04.1) with python3 (3.10.6-1~22.04) all provided paths are relative to the locations in the provided zip.</p><p>The diagrams themself are generated utilising tikz in latex to generate a pdf containing the diagrams execute "latexmk figures.tex -pdf -f" in the latex_table_generation directory. The last execution was with version 4.76-1 on a Ubuntu installation 22.04.1.</p><h3>Licences</h3><p>CC License applies to all images and text files</p><p>MIT License applies to the python program</p>
Result Data of a Systematic Literature Review on Evaluation Methods of IoT Interoperability
No full text<p>The data in this repository contains the result set of a Systematic Literature Review (SLR) on Evaluation Methods in the context of IoT interoperability. The data set consists of 5 files:</p><ol><li><strong>SLR-Paper-Set-and-Assignment-of-IDs.csv:</strong> Lists all papers of the SLR's result set. The SLR-ID was assigned in no specific order and ranges from A1-A50. The IDs are used to identify the respective paper in the other files. The respective year of a paper represents the year of the conference or journal the paper was published.</li><li><strong>Assessment-of-Qualitative-and-Quantitative-Measures.csv:</strong> Indicates if a specific paper uses a qualitative or quantitative evaluation approach.</li><li><strong>Detailed-Assessment-of-Quantitative-Measures.csv:</strong> Shows the clustering results when focusing on the applied quantitative evaluation methods.</li><li><strong>Assessment-of-Technical-Approach.csv:</strong> Shows the clustering results when focusing on the applied technical approach.</li><li><strong>Assessment-of-Application-Domain.csv:</strong> Shows the clustering results when focusing on the applied domain.</li></ol>
Raumbedürfnisse im nutzungsgemischten Stadtkern
No full text<h2>Ergebnisse der Umfrage „Dein Raumbedarf in Wien?“</h2>
<h3>Forschungskontext:</h3>
<p>Die Online-Umfrage „Raumbedarf für Nicht-Wohn-Nutzungen in Wien“ ist Teil einer multimethodischen empirischen Grundlagenerhebung im Projekt „Nachhaltige und sozial innovative Quartiersentwicklung im nutzungsgemischten Stadtkern“, Kurztitel: Stadtkern_PLUS, FFG-Projektnr. FO999892013. </p>
<h3>Forschungsziel:</h3>
<p>Ziel dieser Erhebung ist es, Grundlagenwissen über die Bedürfnisse nach leitbaren Räumen für Nicht-Wohn-Nutzungen in Wien zu gewinnen.</p>
<h3>Zielgruppe der Befragung:</h3>
<p>Personen, die Interesse an bzw. Bedürfnis nach leistbaren Räumen für Nicht-Wohn-Nutzungen in Wien haben. </p>
<h3>Erhebungsmethode:</h3>
<p>Online-Befragung mit einem Mix aus qualitativen und quantitativen sowie geschlossenen und offenen Fragen. Verbreitung der Befragung über eigene Netzwerke sowie über Netzwerke von Kooperationspartner*innen.</p>
<h3>Erhebungstool:</h3>
<p><a href="https://www.limesurvey.org/">LimeSurvey</a> (open source)</p>
<h3>Erhebungszeitraum:</h3>
<p>Juni bis August 2022</p>
Supporting Human-Robot Interaction by Projected Augmented Reality and a Brain Interface
No full text<p><strong>UPDATE - Files are restricted. The last version is V3</strong></p><p> </p><p>This repository stores the raw data and the videos of the manuscript titled "Supporting Human-Robot Interaction by Projected Augmented Reality and a Brain Interface".</p><p>The data.xlsx file contains the raw data collected during the user study.</p><p>The approaches.mp4 video shows the APA and NAPA approaches </p><p>The system.mp4 video shows the NeuroTag selection with the real manipulator.</p>
JavaScript IFC Escape Route Model Generator
No full text<h3>Context</h3>
<p>This research data contains the JavaScript project to generate escape route domain models in the IFC format created by researchers from the TU Wien Research Unit Digital Building Process.<br>It is linked to the research paper:<br> "Fischer, S., Urban, H., Schranz, C., Haselberger, M., & Schnabel, F. (2024).<br> Generation of new BIM domain models from escape route analysis results.<br> Developments in the Built Environment, 19, 100499. <a href="https://doi.org/10.1016/j.dibe.2024.100499">https://doi.org/10.1016/j.dibe.2024.100499</a>"</p>
<p>The research paper describes different ways of storing the results of escape route analysis in IFC models. Five different variants have been evaluated. This Python project contains the code to generate the most promising variant "Routes group Segments -- Group". The generated IFC models for all variants for a custom test model and a real-world model are also published, as well as the two initial models:<br>Custom test model for escape route analysis in IFC format: <a href="https://doi.org/10.48436/hx8gz-zw339">https://doi.org/10.48436/hx8gz-zw339</a><br>Real-world test model for escape route analysis in IFC format: <a href="https://doi.org/10.48436/fnmrh-crh59">https://doi.org/10.48436/fnmrh-crh59</a><br>Custom escape route models in IFC format: <a href="https://doi.org/10.48436/dpwd5-33k50">https://doi.org/10.48436/dpwd5-33k50</a><br>Real-World Escape Route Models in IFC format: <a href="https://doi.org/10.48436/rrd14-t1108">https://doi.org/10.48436/rrd14-t1108</a></p>
<h3>Technical details</h3>
<ul>
<li>The project uses the Programming Language JavaScript.</li>
<li>The project was successfully executed with node v22.7.0.</li>
<li>The project uses the package web-ifc (Version 0.0.42) for processing IFC.</li>
<li>The input data is provided by JSON files containing the escape route data of the two initial IFC models.</li>
</ul>
<p>Instructions on how to use the code are included in the README.md file in the zip folder.</p>
<p>All data files are licensed under CC BY 4.0, all software files are licensed under MIT License.</p>
<p>The IFC Escape Route Model Generator is also available for the Python programming language: <a href="https://doi.org/10.48436/j7apy-mvp41">https://doi.org/10.48436/j7apy-mvp41</a></p>
Revealing differences in public transport share through district-wise comparison and relating them to network properties [Data and Code]
No full text<h2>How to cite?</h2>
<p><a href="https://doi.org/10.4230/LIPIcs.COSIT.2024.10">Canestrini, M., Gogousou, I., Michail, D., & Giannopoulos, I. (2024). Revealing Differences in Public Transport Share Through District-Wise Comparison and Relating Them to Network Properties.<em> 16th International Conference on Spatial Information Theory (COSIT 2024)</em>. Schloss Dagstuhl–Leibniz-Zentrum für Informatik.</a></p>
<h2>Abstract</h2>
<p>Sustainable transport is becoming an increasingly pressing issue, with two major pillars being the reduction of car usage and the promotion of public transport. One way to approach both of these pillars is through the large number of daily commute trips in urban areas, and their modal split. Previous research gathered knowledge on influencing factors on the modal split mainly through travel surveys. We take a different approach by analysing the "raw" network and the time-optimised trips on a multi-modal graph. For the case study of Vienna, Austria we investigate how the option to use a private car influences the modal split of routes towards the city centre. Additionally, we compare the modal split across seven inner districts and we relate properties of the public transport network to the respective share of public transport. The results suggest that different districts have varying options of public transport connections towards the city centre, with a share of public transport between about 5% up to a share of 45%. This reveals areas where investments in public transport could reduce commute times to the city centre. Regarding network properties, our findings suggest, that it is not sufficient to analyse the joint public transport network. Instead, individual public transport modalities should be examined. We show that the network length and the direction of the lines towards the city centre influence the proportion of subway and tram in the modal split.</p>
<h2>How to use?</h2>
<p>The provided material includes data and scripts which were used for the analysis in the paper entitled "<a href="https://doi.org/10.4230/LIPIcs.COSIT.2024.10"><strong>Revealing differences in public transport share through district-wise comparison and relating them to network properties</strong></a>", accepted for COSIT 2024, the 16th Conference on Spatial Information Theory.</p>
<p>It comprises three folders within the zip file:</p>
<ol>
<li><strong>code</strong>: Includes script files essential for conducting the analysis. The scripts are written in Python.</li>
<li><strong>data</strong>: Contains the datasets for the analysis.</li>
<li><strong>results</strong>: Includes the outcomes showcased in the associated paper.</li>
</ol>
<p>Programming Language: Python </p>
<p>For reproducibility read the README.txt; for necessary libraries refer to the requirements.txt. Both files are included in the zip folder.</p>
<p>All data files are licensed under CC BY 4.0, all software files are licensed under MIT License.</p>
RAAV – Results of the Multi Criteria Decision Analysis (MCDA)
No full text<h2>General description</h2><p>As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), the listed data files serve to summarise the results of the stakeholder's integration process based on a Multi Criteria Decision Analysis (MCDA).</p><h3>Context and methodology</h3><p>In the RAAV project, five autonomous vehicles (AV)-based transport scenarios are designed in two rural areas, proposing autonomous bus lines, on-demand rideshare vans, and hybrid combinations of both over time and space. Their accessibility impacts are estimated through a modified space-time accessibility model, to highlight the most promising scenarios. Finally, a stakeholder integration process is conducted to complement the assessment of the AV scenarios and derive policy recommendations for those considered most preferable based on the accessibility model and stakeholder view.</p><p>The accessibility model results and the travel diaries on which this research is based on can be accessed here: </p><ul><li>Travel diaries: <a href="https://researchdata.tuwien.ac.at/records/hq7b7-xsa12">https://researchdata.tuwien.ac.at/records/hq7b7-xsa12</a></li><li>Accessibility model results: <a href="https://researchdata.tuwien.at/records/k1ce7-hrt53">https://researchdata.tuwien.at/records/k1ce7-hrt53</a></li></ul><h3>Technical details</h3><p>The dataset contains two Microsoft Excel files (one for Mühlwald, one for Sooß) containing multiple data sheets. In order to ensure data protection the stakeholder names have been deleted from the dataset. Other than Microsoft Excel, there is no additional software needed to investigate the data.</p>
Image Dataset of Domestic Organic Waste and Non-Organic Contaminants for Classification and Segmentation
No full text<h2>Image Dataset of Domestic Organic Waste and Non-Organic Contaminants for Classification and Segmentation</h2>
<p>We developed a <a title="Smart Trash Can: Easy Collection of Photos of Organic Waste in the Home" href="https://doi.org/10.1145/3626705.3631881" target="_blank" rel="noopener">Smart Trash Can</a> that allows to unobtrusively take photos of real waste in the producer's home. Over six months, a volunteering family collected a total of <strong>450 raw photos of domestic organic waste and non-organic contaminants</strong>, which were then <strong>manually labeled and segmented</strong> according to the captured waste types. Of the total of 450 images, 119 show pure organic waste while 324 also captured intentionally added contaminants. Another 7 photos show only the side walls or the plastic bag without any added waste, and are hence considered as background.</p>
<h3>Context and Methodology</h3>
<ul>
<li>Image dataset for the training of <strong>machine learning</strong>-models for <strong>computer vision</strong> aiming at<strong> waste quality management</strong> in the producer's home</li>
<li>Collection of domestic organic waste and non-organic contaminants with a <a title="Smart Trash Can: Easy Collection of Photos of Organic Waste in the Home" href="https://doi.org/10.1145/3626705.3631881" target="_blank" rel="noopener">Smart Trash Can</a></li>
<li>Images manually labeled and segmented according to the waste types: organic, non-organic, and background</li>
<li>The dataset was used in a continuative <a title="Smart Trash Can: Image Classification and Segmentation for Waste Quality Management in the Home" href="https://doi.org/10.1145/3703790.3703798" target="_blank" rel="noopener">publication</a> demonstrating <strong>two machine-learning approaches</strong> for computer vision, a <strong>binary classification</strong> (mean accuracy of 90.35 %) and a impurity <strong>segmentation</strong> (mean accuracy of 98.24 %, mean intersection over union value of 96.43 %)</li>
</ul>
<h3>Technical Details</h3>
<ul>
<li>A total of <strong>450 photos</strong>: 119 pure organic, 324 with intentionally added contaminants, 7 background</li>
<li>Domestic waste of a <strong>volunteering family</strong> (3 male, 2 female; 15-55 yrs; 33.2 ±16.2 yrs)</li>
<li>Subjects gave <strong>written consent</strong> to provide the image data for research purposes and publication</li>
<li><strong>Raw photos</strong> provided in a lossless *.png format with compression level 0</li>
<li>Two folders 'images' and 'masks' containing the *.png images and the <em>*</em>.png masks for semantic segmentation, respectively</li>
<li>The images are classified and grouped in the three sub-folders 'background', 'organic', and 'non-organic'</li>
<li>The <strong>semantic segmentation labels</strong> associated with the colors in the masks are provided in the 'labelmap.csv' file (';' as separator)</li>
</ul>
Raw data, R scripts and R datasets for statistical analyses from the research article 'Advancing Glycyrrhiza glabra L. cultivation and hairy root transformation and elicitation for future metabolite overexpression'
No full text<h1>Dataset description</h1>
<p>This dataset was created during the research carrried out for the PhD of Negin Afsharzadeh and the subsequent manuscript arising from this research. The main purpose of this dataset is to create a record of the raw data that was used in the analyses in the manuscript.</p>
<p>This dataset includes:</p>
<ul>
<li>raw data generated from experiments stored in an Excel spreadsheet with each sheet corresponding to a specific experiment or part of an experiment (Afsharzadeh_et_al_2024.xlsx) </li>
<li>R script used to analyse the raw data in the software, R (Afsharzadeh_et_al.R)</li>
<li>datasets that were used to analyse the data in the statistical software, R (germindata.txt, light.txt)</li>
</ul>
<h1>Context and methodology</h1>
<h2>Brief description of experiments: </h2>
<p>In this study, we aimed to optimize approaches to improve the biotechnological production of important metabolites in <em>G. glabra</em>. The study is made up of four experiments that correspond to particular figures/tables in the manuscript and data, as described below.</p>
<h3>Experiment 1: </h3>
<p>We tested approaches for the cultivation of <em>G. glabra</em>, specifically the breaking of seed dormancy, to ensure timely and efficient seed germination. To do this, we tested the effect of different pretreatments, sterilization treatments and growth media on the germination success of <em>G. glabra</em>.</p>
<p>This experiment corresponds to: </p>
<ul>
<li>Manuscript: Table 1 and Figure 1</li>
<li>Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_1'); Afsharzadeh_et_al.R; germindata.txt</li>
</ul>
<h3>Experiment 2 (Table 2):</h3>
<p>We aimed to optimize the induction of hairy roots in <em>G. glabra</em>. Four strains of <em>R. rhizogenes</em> were tested to identify the most effective strain for inducing hairy root formation and we tested different tissue explants (cotyledons/hypocotyls) and methods of <em>R. rhizogenes</em> infection (injection or soaking for different durations) in these tissues.</p>
<p>This experiment corresponds to: </p>
<ul>
<li>Manuscript: Table 2</li>
<li>Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_2')</li>
</ul>
<h3>Experiment 3 (Figure 2):</h3>
<p>Eight distinct hairy root lines were established and the growth rate of these lines was measured over 40 days. </p>
<p>This experiment corresponds to: </p>
<ul>
<li>Manuscript: Figure 2, Table S2</li>
<li>Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Figure_2')</li>
</ul>
<h3>Experiment 4 (Figure 3): </h3>
<p>We aimed to test different qualities of light on hairy root cultures in order to induce higher growth and possible enhanced metabolite production. A line with a high growth rate from experiment 3, line S, was selected for growth under different light treatments: red light, blue light, and a combination of blue and red light. To assess the overall impact of these treatments, the growth of line S, as well as the increase in antioxidant capacity and total phenolic content, were tracked over this induction period.</p>
<p>This experiment corresponds to: </p>
<ul>
<li>Manuscript: Figure 3, Figure S4</li>
<li>Data: Afsharzadeh_et_al_2024.xlsx (Sheets 'Figure_3_FW', 'Figure_3_FRAP', 'Figure_3_Phenol'); Afsharzadeh_et_al.R; light.txt</li>
</ul>
<h1>Technical details</h1>
<p>To work with the .R file and the R datasets, it is necessary to use R: A Language and Environment for Statistical Computing and a package within R, aDHARMA. The versions used for the analyses are R version 4.4.1 and aDHARMA version 0.4.6.</p>
<p>The references for these are: </p>
<p>R Core Team, R: A Language and Environment for Statistical Computing 2024. <a href="https://www.R-project.org/" target="_blank" rel="noopener">https://www.R-project.org/</a></p>
<p>Hartig F, DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models 2022. <a href="https://CRAN.R-project.org/package=DHARMa" target="_blank" rel="noopener">https://CRAN.R-project.org/package=DHARMa</a></p><p>Licenses: The CC-BY license applies to all the data. All distributed code is under the specified GNU GPLv3+ 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>
<blockquote>
<h2><a href="../records/s5j4q-rpd32/latest"><code>A newer version of this record is available. Click here for the latest version!</code></a></h2>
</blockquote>
<h2>Dataset paper (public preprint)</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 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. </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.</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</li>
<li>More information: <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>Technical 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-fv09.1.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>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 satellite observations, on which the interpolation is based (this variable is experimental and will change in future versions of the record). </li>
<li><strong>gapmask</strong>: (0 | 1) Indicates grid cells where a satellite observation is available (0), and where the interpolated value is used (1) in the 'sm' field.</li>
</ul>
<p>Additional information for each variable is 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>
<h3>References</h3>
<ul>
<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>
</ul>
<h2>Related Records</h2>
<p>The following records are all part of the <a href="../communities/soilmoisture-climaterecords/records?q=&l=list&p=1&s=10&sort=newest">Soil Moisture Climate Data Records from satellites</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>