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SDOclust Evaluation Tests v2
No full text<h2>SDOclust Evaluation Tests v2</h2><p>conducted for the paper: <strong>Parameterization-Free Clustering with Sparse Data Observers</strong></p><h3>Context and methodology</h3><p>SDOclust is a clustering extension of the Sparse Data Observers (SDO) algorithm. SDOclust uses data observers as graph nodes and cluster them considering connected components and local thresholding. Observers' labels are subsequently propagated to data points. </p><p>In this repository, SDOclust is evaluated with 235 datasets (both synthetic and real) taken from the literature about clustering evaluation, and compared with HDBSCAN, k-means--, CLASSIX, N2D (Deep Learning Clustering), Fuzzy Clustering, and Hierarchical Clustering algorithms.</p><p>This repository is framed within the research on the following domains: <i>algorithm evaluation, clustering, unsupervised learning, machine learning, data mining, data analysis. </i>Datasets and algorithms can be used for experiment replication and for further clustering evaluation and comparison.<i> </i> </p><h3>Technical details</h3><p>Experiments are conducted in Python 3. The file and folder structure is as follows:</p><ul><li>[datasets] contains datasets as CSV files (last column is the label).</li><li>[comparisons] contains boxplots and latex tables with algorithm comparisons summarized from the [results] folder.</li><li>[results] contains CSV files with tables that collect algorithms' performances obtained from running the "run.py" script.</li><li>[algorithms] contains scripts wrapping algorithm classes used and parameter adjustment phases.</li><li>[utils] contains scripts for clustering validation and measurement of dataset propierties.</li><li>"dependencies.sh" installs python dependencies.</li><li>"run.py" runs evaluation experiments.</li><li>"comparison.py" summarizes performances in TEX tables and boxplots.</li><li>"LICENSE" file.</li><li>"README.md" for further details, link to sources and instructions for reproducibility.</li></ul><h3>License</h3><p>The CC-BY license applies to all data generated with MDCgen. All distributed code is under the MIT license.</p><h3> </h3>
Rapid 3D printing of unlayered, tough epoxy-alcohol resins with late gel points via dual-color curing technology
No full text<h3>Context and methodology</h3>
<p>This dataset was collected during a research project on photopolymers for 3D printing at the Institute of Applied Technology, TU Wien. It entails full characterization of interpenetrating networks, which have been produced by semi-orthogonal photopolymerization via radical and cationic photopolymerization. The dataset is part of a peer-reviewed publication, where the experimental procedures for obtaining the presented data are described in detail.</p>
<p> </p>
<h3>Technical details</h3>
<p>The dataset entails the raw data of various analyses collected in an Excel file.</p>
<p>Tab 1: Absorption data of photoinitiators</p>
<p>Tab 2: Tensile testing and dynamic mechanical analysis (DMTA) data</p>
<p>Tab 3: Photo-dynamic scanning calorimetry (Photo-DSC) data</p>
<p>Tab 4: DMTA and tensile testing data</p>
<p>Tab 5: Tensile testing data for pure epoxy network</p>
<p>Tab 6: photo-DSC data for irradiation with omnicure light source</p>
<p>Tab 7: photo-DSC data for irradiation with LED light source</p>
Data for "Strawberry post-harvest colour development to improve the colour of strawberry nectars"
No full text<p>This data set contains the raw data used to produce the paper "Strawberry post-harvest colour development to improve the colour of strawberry nectars" as a part of the HiStabJuice Project. </p><h3>Context and methodology</h3><ul><li>This excel file contains all the data used to create the statistics, tables and figures for the paper.</li></ul><h3>Technical details</h3><ul><li>This excel file contains 3 tabs that each correspond to a sheet in SPSS that can be re-uploaded into SPSS. The Key contains the numerical codes used to denote the different parameters.</li></ul>
Trusted Research Environments: Analysis of Characteristics and Data Availability
No full text<p>Trusted Research Environments (TREs) enable analysis of sensitive data under strict security assertions that protect the data with technical organizational and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks & their slight technical variations. To shine light on these problems, we give an overview of existing, publicly described TREs and a bibliography linking to the system description. We further analyze their technical characteristics, especially in their commonalities & variations and provide insight on their data type characteristics and availability. Our literature study shows that 47 TREs worldwide provide access to sensitive data of which two-thirds provide data themselves, predominantly via secure remote access. Statistical offices make available a majority of available sensitive data records included in this study.</p><h2>Methodology</h2><p>We performed a literature study covering 47 TREs worldwide using scholarly databases (Scopus, Web of Science, IEEE Xplore, Science Direct), a computer science library (dblp.org), Google and grey literature focusing on retrieving the following source material:</p><ul><li>Peer-reviewed articles where available,</li><li>TRE websites,</li><li>TRE metadata catalogs.</li></ul><p>The goal for this literature study is to discover existing TREs, analyze their characteristics and data availability to give an overview on available infrastructure for sensitive data research as many European initiatives have been emerging in recent months.</p><h3>Technical details</h3><p>This dataset consists of five comma-separated values (.csv) files describing our inventory:</p><ul><li><strong>countries.csv</strong>: Table of countries with columns id (number), name (text) and code (text, in ISO 3166-A3 encoding, optional)</li><li><strong>tres.csv</strong>: Table of TREs with columns id (number), name (text), countryid (number, refering to column id of table countries), structureddata (bool, optional), datalevel (one of [1=de-identified, 2=pseudonomized, 3=anonymized], optional), outputcontrol (bool, optional), inceptionyear (date, optional), records (number, optional), datatype (one of [1=claims, 2=linked records]), optional), statistics_office (bool), size (number, optional), source (text, optional), comment (text, optional)</li><li><strong>access.csv</strong>: Table of access modes of TREs with columns id (number), suf (bool, optional), physical_visit (bool, optional), external_physical_visit (bool, optional), remote_visit (bool, optional)</li><li><strong>inclusion.csv</strong>: Table of included TREs into the literature study with columns id (number), included (bool), exclusion reason (one of [peer review, environment, duplicate], optional), comment (text, optional)</li><li><strong>major_fields.csv</strong>: Table of data categorization into the major research fields with columns id (number), life_sciences (bool, optional), physical_sciences (bool, optional), arts_and_humanities (bool, optional), social_sciences (bool, optional).</li></ul><p>Additionally, a MariaDB (10.5 or higher) schema definition .sql file is needed, properly modelling the schema for databases:</p><ul><li><strong>schema.sql</strong>: Schema definition file to create the tables and views used in the analysis.</li></ul><p>The analysis was done through Jupyter Notebook which can be found in our source code repository: <a href="https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb">https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb</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. Apart from their original French versions, the collection also contains translations of the webpages and queries into English. The collection serves as the official test collection for the 2024 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF.</p><p>The data is released under the <a href="https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License">Qwant LongEval Attribution-NonCommercial-ShareAlike License.</a></p>
Celestial reference frame 2023 from VGOS sessions
No full text<h2>Context and methodology</h2><ul><li>Celestial reference frame estimated from Very Long Baseline Interferometry VGOS-OPS and VGOS-R&D sessions.</li></ul><h3>Technical details</h3><ul><li>GENERATION TIME: 2024-02-03T18:59:32</li><li>DATA START: 2017-12-03T00:00:00</li><li>DATA END: 2023-12-28T00:00:00</li><li>ANALYSIS CENTER: VIE (TU Wien, Austria)</li><li>CONTACT: [email protected]</li><li>SOFTWARE: VieVS v3.2</li><li>TECHNIQUE: VLBI</li><li>FREQUENCY BANDS: VGOS</li><li>FORMAT: ICRF</li></ul>
MAB phases: high-throughput ab initio screening of phase stability and elastic properties
No full text<h2>Context</h2>
<p>The data coresponds to the publication:</p>
<p><strong>Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations; by Nikola Koutná, Lars Hultman, Paul H. Mayrhofer, Davide G. Sangiovanni;</strong></p>
<p>accessible at <a href="https://doi.org/10.1016/j.matdes.2024.112959">https://doi.org/10.1016/j.matdes.2024.112959</a></p>
<h2>Data</h2>
<p>The zip file contains a pdf of the publication and the supplementary file; relaxed structures (<a href="https://www.vasp.at/wiki/index.php/POSCAR">VASP POSCAR format</a>, computational parameters are described in the Methods), the calculated energies and volumes, raw elastic constants, the projected density of states, and phonon spectra of the low-energy systems for each combination of M and A elements.</p>
<h2>Licenses</h2>
<p>The data is licensed under CC-BY, the code is licensed under MIT.</p>
Numerical results for: Non-perturbative intertwining between spin and charge correlations: A "smoking gun" single-boson-exchange result
No full text<p>This data repository contains the original numerical (raw) data, and plot scripts to reproduce the figures from the publication "Non-perturbative intertwining between spin and charge correlations: A "smoking gun" single-boson-exchange result" at <a href="https://doi.org/10.21468/SciPostPhys.16.2.054">SciPost Physics.</a></p><p>The LaTeX source files of the preprint available on <a href=" https://doi.org/10.48550/arXiv.2212.09693">arXiv</a> can be found at the <a href="https://gitlab.tuwien.ac.at/e138/e138-02/papers/non-perturbative-intertwining-between-spin-and-charge-correlations-a-smoking-gun-single-boson-exchange-result">TU gitlab</a>.</p><h3>Licenses</h3><p>The data is licensed under CC BY 4.0, the code is licensed under MIT.</p>
ESA CCI SM RZSM Long-term Climate Record of Root-Zone Soil Moisture from merged multi-satellite observations
No full text<h3><strong>Context and methodology</strong></h3>
<div>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"). </div>
<div>It contains information on the Root-Zone Soil Moisture (RZSM) content at different depth layers as derived from Surface SM satellite observations of the ESA CCI SM products<em>.</em></div>
<div><em> </em></div>
<div>The RZSM estimates and relative uncertainties are derived using the method of Pasik et al. (2023) forced with observations of the ESA CCI SM Combined product (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021).</div>
<h3><strong>Technical details</strong></h3>
<div>The dataset provides global daily estimates for the 1978-2023 period at 0.25° (~25 km) horizontal resolution. The compressed downloadable rzsm_v09.1_1978_2023.tar.gz file is structured in sub-directories each including all files for a specific year.</div>
<div>Each netCDF file contains the data of a specific day (DD), month (MM), and year (YYYY) in a 2-dimensional (longitude, latitude) grid system. The file name has the following convention:</div>
<div>ESA_CCI_RZSM-YYYYMMDD000000-fv0.9.1.nc</div>
<div>The RZSM data reflects the estimates calibrated for 4 depth layers:</div>
<ul>
<li>rzsm1: 0-10 cm</li>
<li>rzsm2: 10-40 cm</li>
<li>rzsm3: 40-100 cm</li>
<li>rzsm4: 0-100 cm</li>
</ul>
<div>A package is available in python for reading the data as daily images and converting these images to time series and reading them. The source code for our python package and installation instructions are available here: <a href="https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm</a></div>
<ul>
<li>The package can be installed via pip using "pip install esa_cci_sm"</li>
<li>The documentation for this package is available here: <a href="https://esa-cci-sm.readthedocs.io/en/latest/" target="_blank" rel="noopener noreferrer">https://esa-cci-sm.readthedocs.io/en/latest/</a></li>
<li>The "parameter" argument (e.g., <a href="https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39</a>) can be specified to any of the layer variables (rzsm1, rzsm2, ...)</li>
</ul>
<div>Any software that can handle CF conform data should be able to import the raw netCDF files (e.g. <a href="https://code.mpimet.mpg.de/projects/cdo" target="_blank" rel="noopener noreferrer">CDO</a>, <a href="http://nco.sourceforge.net/" target="_blank" rel="noopener noreferrer">NCO</a>, <a href="https://www.qgis.org/" target="_blank" rel="noopener noreferrer">QGIS</a>, ArCGIS, Matlab, R, ...). You can also use the GUI software <a href="https://www.giss.nasa.gov/tools/panoply/" target="_blank" rel="noopener noreferrer">Panoply</a> to view each file.</div>
<h3>Reference</h3>
<p><strong>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, </strong><a href="https://doi.org/10.5194/gmd-16-4957-2023,%202023"><strong>https://doi.org/10.5194/gmd-16-4957-2023, 2023</strong></a></p>
<h3>Additional citations</h3>
<p>Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, <a href="https://doi.org/10.1016/j.rse.2017.07.001">https://doi.org/10.1016/j.rse.2017.07.001</a>.</p>
<p>Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science 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></p>
<p>Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W. (2021). Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.</p>
<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="../doi/10.48436/rqfmp-jp420">10.48436/rqfmp-jp420</a></td>
</tr>
<tr>
<td>2</td>
<td>
<p>ESA CCI SM GAPFILLED Surface Soil Moisture Record </p>
</td>
<td><a href="../doi/10.48436/hcm6n-t4m35">10.48436/hcm6n-t4m35</a></td>
</tr>
</tbody>
</table>
<p> </p>
Eggfit: An iterative algorithm to fit egg-shapes to object boundaries
No full text<h2>Fitting egg shapes to boundaries</h2>
<p>This is a snapshot of the research code that produced results for the following publication:</p>
<ul>
<li>J. Hladůvka and W. G. Kropatsch. Fitting egg-shapes to discretized object boundaries, in <em>Discrete geometry and mathematical morphology</em>, 2024, pp. 107–119, DOI:<a href="https://doi.org/10.1007/978-3-031-57793-2_9">10.1007/978-3-031-57793-2_9</a>.</li>
</ul>
<p>To reproduce the experiments, please:</p>
<ol>
<li>unzip the archive,</li>
<li>patch the package versions as described in the <strong>erratum </strong>below, and</li>
<li>follow the instructions in the README of the zip archive.</li>
</ol>
<p><strong>Erratum</strong> (2024, September)<strong>:</strong></p>
<p>During the follow-up work, it has been noted that the package version specifications in <code>eggfit/workflow/envs/eggfit.yaml</code> of the uploaded zip archive were accidentally commented out which may result in execution failure. To resolve this, please remove the comments <code>#</code> preceding the <code>=</code> sign or, equivalently, replace the content of the file with the following:</p>
<pre><code>channels:
- conda-forge
dependencies:
- python = 3.9.18
- numpy = 1.23.2
- scipy = 1.9.3
- shapely = 2.0.1
- pandas = 1.5.1
- matplotlib = 3.7.1
- scienceplots = 2.1.0
- seaborn = 0.12.2<br>
</code></pre>