Context and methodology
\nThis dataset was generated within a research project developing a concise and scalable synthetic route to iTCO, an intramolecularly controlled click-cleavable trans-cyclooctene linker for tetrazine-triggered bioorthogonal payload release. The associated manuscript describes the scientific background, synthetic strategy, and development and scale-up of the key flow photohydrolysis-iodolactonization sequence, while the Supporting Information provides detailed experimental procedures and analytical characterization.
\nThe purpose of this dataset is to provide the raw NMR spectroscopy data underlying the structural characterization of the compounds described in the associated manuscript and Supporting Information. The data were generated using standard NMR spectroscopy methods as part of the characterization of intermediates and products obtained during the synthesis of iTCO.
\nTechnical details
\nThe dataset consists of raw NMR spectroscopy data provided in their original instrument format. The data are organized by compound, with assignment to the corresponding compounds and experiments provided through the file and folder names and the compound numbering used in the associated manuscript and Supporting Information.
\nThe raw data allow independent processing and analysis using standard NMR spectroscopy software such as Mnova or TopSpin. Additional information required for interpretation of the deposited data, including molecular structures, compound numbering, experimental procedures and conditions, and analytical characterization, is provided in the associated manuscript and Supporting Information.
\nFurther details
\nThe data are suitable for independent inspection and reanalysis of the NMR spectroscopic characterization and for reuse in comparative or methodological studies, provided that appropriate attribution is given.
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{"id": "dataset", "title": {"de": "Datensatz", "en": "Dataset"}}, "creators": [{"person_or_org": {"type": "personal", "name": "Bicher, Martin", "given_name": "Martin", "family_name": "Bicher"}, "role": {"id": "contactperson", "title": {"ar": "\u0645\u0633\u0624\u0648\u0644 \u0627\u0644\u0627\u062a\u0635\u0627\u0644", "cs": "Kontaktn\u00ed osoba", "de": "Kontaktperson", "en": "Contact person", "es": "Persona de contacto", "sv": "Kontaktperson"}}, "affiliations": [{"id": "04d836q62", "name": "TU Wien", "identifiers": [{"identifier": "04d836q62", "scheme": "ror"}, {"identifier": "grid.5329.d", "scheme": "grid"}, {"identifier": "0000 0004 1937 0669", "scheme": "isni"}]}]}], "title": "Synthetic Household Contact- and Purification Plant- Network for Austria", "publisher": "TU Wien", "publication_date": "2026-09-10", "subjects": [{"subject": "Contact Network"}, {"subject": "Epidemiology"}, {"subject": "Synthetic Data"}, {"subject": "Households"}], "languages": [{"id": "eng", "title": {"en": 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The data is foundation to the work published in Model-Based Optimisation of Outbreak Detection via Wastewater Probing where it was used as input to a multipopulation-SIR compartment model.
\nBeyond its original purpose, the data is intended to be used for being used in dynamic models in which pysical inter-household contacts are required. This includes, but is not restricted to epidemiological models. The data should not be used for static network analysis, considering that it is not sufficiently validated in this respect.
\nThe synthetic household coordinates, their inhabitant counts as well as their contacts with other households via school, workplace and leisuretime are randomly sampled by the agent-based model, but are statistically representative for Austria for that date w.r. to data about population census, household distribution, regional number and size of work-places and school(classes), and average number (Polymod survey) and location (mobile phone data) for leisuretime contacts. For more details on the underlying data sources we refer to the SI of Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation where the agent-based epidemics model and its underlying data sources are described. For the generation of the household-household contact data, the agent-based epidemic model is run for 10 days counting the number of sampled contacts between members of different households.
\nIn addition, (almost) every synthetic household is linked to a wastewater treatment plant. The assignment of these plants is based on actual Austrian wastewater treatment plants and their catchment areas - see data.gv.at for details.
\nThe different datasets differ in terms of the network's scale:
\nEach network dataset is available in two versions: a human-readable CSV version and a version specifically designed to be loaded with Python3 and numpy.
\nThe following data was exported with Python (pickle) 3.12.2 and numpy 2.4.2
\nThis dataset was generated within a research project investigating intramolecular control of tetrazine-triggered click-to-release chemistry through the development of iTCO, a trans-cyclooctene linker equipped with a tautomerization-directing hydroxyl group. The corresponding publication describes the scientific background, molecular design, mechanistic investigations, and biological applications in detail, while the Supplementary Information provides extended experimental, analytical, and computational methods.
\nThe purpose of this dataset is to document the quantitative experimental data underlying the figures reported in the associated manuscript and Supplementary Information. The data support the development and characterization of iTCO, including its click-to-release efficiency and kinetics with structurally diverse tetrazines, the mechanistic analysis of post-click tautomerization and elimination pathways, the influence of pH and biological media on reaction performance, and the stability of iTCO conjugates. The dataset further supports experiments demonstrating bioorthogonal activation of iTCO-caged prodrugs in solution and in living cells, as well as real-time click-triggered cleavage of an antibody-conjugated iTCO probe from circulating red blood cells in vivo.
\nThe data were generated using established experimental techniques, including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), UV-Vis and fluorescence spectroscopy, NMR spectroscopy, cell-based viability assays, and fluorescence microscopy. Computational studies using density functional theory (DFT) and time-dependent DFT were additionally used to investigate the post-click reaction mechanism and photophysical properties of relevant intermediates and products. The optimized molecular geometries obtained from these calculations are provided as XYZ files. Raw NMR data supporting the structural characterization and mechanistic investigations are likewise included. Detailed experimental procedures, reaction conditions, analytical methods, and computational protocols are described in the accompanying manuscript and Supplementary Information.
\nThe dataset consists of a structured Excel workbook containing quantitative data underlying the experimental figures presented in the associated publication and Supplementary Information, together with raw NMR spectroscopy data and calculated molecular geometries from the computational studies.
\nThe Excel workbook contains individual worksheets corresponding to specific figures, enabling direct mapping between the deposited data and the graphical representations in the publication.
\nRaw NMR spectroscopy data are provided for the structural characterization of synthesized compounds and for NMR-based mechanistic experiments reported in the manuscript and Supplementary Information. The data are provided in their original instrument format and allow independent processing and analysis using standard NMR software such as Mnova or TopSpin. Assignment of the NMR datasets to specific compounds and experiments is described by the corresponding file and folder names and in the associated Supplementary Information.
\nCalculated molecular geometries from the DFT and time-dependent DFT studies are provided as XYZ files. These files contain the optimized Cartesian coordinates of the molecular structures investigated computationally and can be opened, visualized, and further processed using commonly available molecular visualization and computational chemistry software. Assignment of the calculated structures to the corresponding intermediates, products, and computational analyses is provided through the file names and the associated Supplementary Information.
\nThe Excel workbook can be accessed using standard spreadsheet software such as Microsoft Excel or equivalent open-source applications.
\nAll additional information required for interpretation of the deposited data, including molecular structures and compound numbering, experimental procedures and conditions, analytical methods, kinetic analyses, computational methods, and details of the cellular and in vivo experiments, is provided in the associated manuscript and Supplementary Information.
\nThe data are suitable for reuse in comparative studies, methodological benchmarking, meta-analyses, or educational contexts, provided that appropriate attribution to the original publication is given.
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\nIn Graphene on Ir(111).zip, most of the data are related to LEED, AES, and SEM-EDX measurements of graphene layers on Ir(111) surfaces.
\nAs some of the data have not yet been published, the uploaded files can only be accessed by authorized users with permission.
\nIm Allgemeinen adsorbiert das CO auf dem Metallteilchen, während Wasser, H2O, auf dem oxidischen Träger aktiviert wird, bzw. in OH* zersetzt wird. Traditionell werden zwei verschiedene Mechanismen diskutiert. Der sogenannte assoziative Mechanismus, bei dem CO* und OH* reagieren, was wahrscheinlich an der Metall/Oxidgrenzfläche passiert. Hierbei können Carboxylate (COOH*), Formiate (HCOO*) und/oder Carbonate (CO3*) gebildet werden, welche entweder als Intermediate oder inerte Spezies fungieren. Die Zersetzung von Carboxylat (COOH*) oder Formiat (HCOO*) bildet schließlich die Produkte CO2 und H2. Dies ist stark verschieden vom Redoxmechanismus, in dem CO direkt mit einem Sauerstoff des Oxides reagiert und direkt ein CO2 bildet. Die Fehlstelle, wo der Sauerstoff saß, aktiviert daraufhin das Wasser, H2O, dissoziiert und lässt den Wasserstoff, H2, frei.
\nDas Hauptziel dieses Projektes ist es, einen neuartigen Katalysator basierend auf bimetallischen NiZrOx Nanoteilchen zu präparieren und zu charakterisieren. Die Ni Partikel werden auf einer Graphenschicht verankert, welche durch Gasphasenabscheidung auf einer Ir(111) Schicht im Ultrahochvakuum aufgebracht wird. Dieser Katalysator hat die Oxidfunktionalität gleich in die Nanopartikel eingebaut. Die Oberflächenstruktur und Zusammensetzung wird durch mehrere Verfahren bestimmt: low energy electron diffraction (LEED), Auger Elektronen Spektroskopie (AES), Röntgen Photoelektronen Spektroskopie (XPS), Oberflächen Röntgenbeugung (SXRD) und Rastertunnelmikroskopie (STM). Die katalytische Aktivität, Adsorption der Ausgangsstoffe, sowie deren Koadsorption wird mit Hilfe von Summenfrequenz-Erzeugung (SFG) Laserspektroskopie und Massenspektroskopie gemessen. Mit Hilfe von SFG kann nun der Adsorptionsplatz, z.B. ein-, zwei-, oder dreifach koordiniert, sowie die Orientierung der adsorbierten Moleküle auf der Oberfläche bestimmt werden, was in Kombination mit einer rechnergestützten Modellierung dieser Oberflächenspezies Rückschlüsse auf den Mechanismus erlaubt.
\nMost important results
\nThis project investigated the water-gas shift (WGS) reaction, in which CO and H\u2082O react to form CO\u2082 and H\u2082, using bimetallic NiZrO\u2093 nanoparticles supported on graphene/Ir(111). To elucidate the reaction mechanism, we first investigated CO and H\u2082O adsorption on several reference surfaces.
\nFor CO on Ir(111), the resonant sum frequency generation (SFG) signal of the C–O stretch was approximately eight times stronger on a rough than on a smooth surface. We systematically evaluated possible origins, including coverage, lateral interactions, molecular hyperpolarizability, adsorption geometry, Fermi resonances, hot vibrational bands, and surface plasmons. We concluded that sputter-induced surface roughness, evidenced by scanning tunneling microscopy (STM), promotes localized surface plasmon resonances (LSPRs), resulting in the observed SFG enhancement. CO adsorption on Gr/Ir(111) showed no evidence of intercalation, confirming the integrity of the graphene layer.
\nFor atomic layer deposition (ALD) ZrO\u2082, Al\u2082O\u2083, and TiO\u2082 films, no OH groups were detected at room temperature, even after heating ZrO\u2082 in 10 mbar H\u2082O at 520 K. At 93–95 K, residual H\u2082O produced a free-OH signal at 3720 cm\u207b¹. To avoid this interference, we used D\u2082O to form amorphous solid water (ASW). At low coverage, the SFG spectra showed free OD (~2730 cm\u207b¹) and a broad feature (~2650 cm\u207b¹) assigned to water hydrogen-bonded to the oxide surface. With increasing coverage, water–water hydrogen bonding became dominant. H\u2082O-ASW exhibited a stronger and narrower surface-hydrogen-bonded OH signal but a weaker free-OH signal, indicating fewer free OH groups. No significant differences in the D\u2082O hydrogen-bonding networks were observed among the ALD films.
\nFinally, no CO SFG signal was detected on Ni\u2086\u2080/Zr foil at 100–200 K up to 26 mbar, suggesting either a low CO surface density or poor ordering on the Ni particles. D\u2082O-ASW on Ni\u2086\u2080/Zr also produced much weaker free-OD and hydrogen-bonded OD signals than the ALD oxides, while the absence of the ~2650 cm\u207b¹ feature further supports its assignment to water–oxide hydrogen bonding.
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This dataset contains correlated VLBI data of observations of a natural radio source at the frequency bands of the Genesis VLBI signal.
\nOn April 27, 2026, at 13:00 UTC the natural radio source 4C39.25 was observed with a network of VGOS telescopes, using a frequency setup that covers the radio signal to be transmitted by the Genesis satellite. This dataset contains 30 seconds of correlated data of the baseline ONSA13SW-WETTZ13S. The data were correlated with the DiFX software.
\nThese data follow the default naming convention of DiFX. The base directory contains the configuration files, and the subdirector contains the visibility data in the SWIN format along with the extracted phase-cal data. The data were generated with DiFX-2.5.5. A detailed description of the DiFX file formats can be found here.
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preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code."}, "props": {"url": "https://opensource.org/license/mit", "scheme": "spdx"}}], "description": "This record contains the measurement data and analysis code underlying the article \"Measurement-Driven Modeling of End-to-End Latency in an Indoor Private Standalone 5G Network\" (MDPI Network).
\nContext and methodology. End-to-end latency and throughput were measured in the private standalone 5G network of the TU Wien IFT TEC-Lab. Measurements were taken at seven fixed indoor locations, each situated directly beneath one of the seven pico radio units of the deployment. One-way latency was measured over UDP at six target traffic rates (1, 10, 50, 100, 200 and 500 Mbps) with five repetitions per condition, giving 210 runs; TCP throughput was measured with five repetitions per location, giving 35 runs. All measurements used iPerf 2.2.1 in enhanced-report mode, with 60-second runs and one-second reporting intervals. The latency campaign was carried out between 27 June and 3 July 2025 and the throughput campaign on 3 June, 27 June and 1 July 2025. Measurements characterise the complete path between a wired campus host and a 5G-attached endpoint, including campus routing and firewall functions, the private 5G core and radio access network, the customer premises equipment, and the wireless local-area link between the CPE and the receiving host.
\nTechnical details. data.zip contains the processed per-trial datasets used in the article, including the achieved throughput parsed from the raw iPerf server reports. raw_latency_logs.zip and raw_throughput_logs.zip contain the unmodified iPerf output files. code.zip contains the MATLAB analysis pipeline in execution order, covering parsing, model fitting, bootstrap confidence-interval estimation, cross-validation, baseline comparison and the trial-level distribution analysis, together with an independent Python re-implementation used to verify the published parameter estimates. The MATLAB scripts were developed and run with MATLAB R2025b and require the Optimization Toolbox (lsqcurvefit); no other toolboxes are used. The Python scripts were tested with Python 3.14.6 under Windows 11, using both NumPy 2.4.6 and NumPy 2.5.2; the results agree between the two NumPy versions to at least six significant figures. NumPy is their only third-party dependency, and it is also declared in code/python/requirements.txt. All paths in the scripts are resolved relative to the script location, so the archives can be extracted anywhere.
Licensing. This record is dual-licensed. The CC BY 4.0 license applies to all data, that is to data.zip, raw_latency_logs.zip, raw_throughput_logs.zip and the README. All distributed code, that is the MATLAB and Python scripts in code.zip, is licensed under the MIT License. The full license texts are given in LICENSE.md, and the MIT license is additionally included inside code.zip as code/LICENSE-MIT.txt.
Further details. Three runs (location Q1, 50 Mbps, repetitions 3–5) returned invalid one-way delay values caused by a 32-bit counter wrap-around arising from residual clock offset between the two hosts. These runs are flagged by valid_owd = 0 in latency_trials_raw_210.csv and were replaced by the cell mean in latency_210.csv; script 10_imputation_sensitivity.m quantifies the effect. See Section 4.3.1 of the article. Models are fitted using the achieved throughput reported by iPerf, not the configured target rate.
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Poster presented at 21st European Conference on Technology Enhanced Learning (ECTEL 2026) in Valencia, Spain.
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Poster presented at 24th Conference on Educational Technologies (DELFI 2026) in Potsdam, Germany.
\nRelated poster paper publication available via DOI: 10.18420/delfi26-42
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This repository accompanies the paper Closing the Effectiveness–Scalability Gap in Distributed Unsupervised Learning and contains the experimental data and results used to evaluate parallel versions of SDO and SDOclust (and alternatives) in anomaly detection and clustering under controlled conditions. Experiments include: comparisons under different scaling factors (cardinality, dimensionality, number of clusters, and rate of anomalies) with synthetic data, strong and weak scaling experiments, and tests with real network data in a stream data large-batch incremental setting.
\n\n\nClosing the Effectiveness–Scalability Gap in Distributed Unsupervised Learning, by Félix Iglesias, Julian Lampert, Tanja Zseby, and Arthur Zimek. Currently under review
\n
Experiments are prepared for running in both local and distributed environment. They were tested in:
\n\n
Real datasets are stored in the `packetdata/` directory. Eperimental results are provided in the `results_SlurmCluster_16workers_Aug2026.zip` and `results_local_8cores_160GbRAM_Aug2026.zip` files. The artifact includes Python scripts for data generation and experiment execution, together with configuration and metadata files.
\nThe experiments require Python 3.9.6 and the dependencies listed in `code/requirements.txt`. A pre-built Docker and Apptainer images and the corresponding configurations are also provided to facilitate reproducibility.
\nUpdated parallel SDO and SDOclust codes can be found in: https://github.com/CN-TU/parallel-SDO
\n\n
See the `README.md` (within `scripts.zip`) for detailed instructions on how to reproduce the experiments.
\n\n
code_and_docs.zip: Source code, scripts, and documentation required to run and analyze the project.
\npacketdata.zip: Input network packet data used in the experiments.
\nreproducible-env.sif`: Apptainer image containing the software environment required to reproduce the experiments.
\nreproducible-env.tar.gz: Compressed archive of the reproducible software environment and its required dependencies.
\nresults_SlurmCluster_16workers_Aug2026.zip: Experimental results obtained on a Slurm cluster using 16 workers in August 2026.
\nFIRST_OF_ALL.txt: File containing initial instructions and important information for reproducing the experiments.
\nthird_party_LICENSE.md: License and attribution information for Malware Communication in Smart Factories: A Network Traffic Data Set (CC BY 4.0)
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Global reference frame solution (TRF + EOP + CRF) from Very Long Baseline Interferometry based on S/X-band, VGOS and K-band sessions until 2026.0. Analyzed with VieVS software package.
\nS/X and VGOS: IVS data centers https://cddis.nasa.gov/archive
\nK band
\nPlain ASCII files.
\nThe International VLBI Service for Geodesy and Astrometry and all its components are acknowledged for providing the S/X and VGOS data.
\nThe use of the Very Long Baseline Array under the US Naval Observatory's time allocation is acknowledged, which supports USNO's ongoing research into the celestial reference frame and geodesy. The VLBA is operated by the National Radio Astronomy Observatory, which is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
\nThis study also made use of data collected from radio telescopes at HartRAO, Hobart, Yebes Observatory, the Korean VLBI network (KVN), the Australian Telescope Compact Array, Mopra, Parkes, Ceduna, Tidbinbilla, Sejong, and Tianma. Radio telescopes observing K-band sessions within the Astro2Geo project are acknowledged https://sites.google.com/sarao.ac.za/k-bandastrogeovlbi/network
\nWork done within the Astro2Geo K-band collaboration.
\nRelated journal article is under review.
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VieSOFT is a software package for the removal of source structure in very long baseline interferometry (VLBI) data. It contains tools to model the structure from images in the FITS format and to remove the interferometric phase terms from VLBI visibility data in the SWIN (DiFX) format. This software was originally developed for the purpose of correcting the impact of source structure as a systematic error in geodetic VLBI observations, in particular of the VLBI Global Observing System (VGOS). Application of the software to other kinds of VLBI observations is possible. The software has been tested under Linux environments. A user's guide containing installation instructions and details of usage is included.
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\n\n
This repository accompanies the paper When Does the Algorithm Matter? Data-Driven Performance in Anomaly Detection and Clustering and contains the experimental data and results used to evaluate anomaly detection and clustering algorithms under controlled conditions. Experiments include synthetic datasets with OFAT and factorial analyses, as well as real-world dataset collections from ADBench and ClusBench.
\n\n\nWhen Does the Algorithm Matter? Data-Driven Performance in Anomaly Detection and Clustering, by Andjela Dejelic, Tanja Zseby, and Félix Iglesias. Currently under review
\n
\n
Datasets are stored in the `datasets/` directory, and experimental results are provided as CSV files in `results_Aug2026.zip`. The artifact includes Python scripts for data generation and experiment execution, together with configuration and metadata files.
\nThe experiments require Python 3.9.6 and the dependencies listed in `docker/requirements.txt`. A pre-built Docker image and the corresponding Docker configuration are also provided to facilitate reproducibility.
\ndatasets.zip — dataset subsets from ADBench and ClusBench collections.
docker.zip — Docker configuration and dependency files for the reproducible environment.
docker_image.zip — Pre-built Docker image with all required dependencies.
FIRST_OF_ALL.md — Initial instructions and information for using the artifact.
results_Aug2026.zip — Complete experimental results reported in the paper.
scripts.zip — Python scripts for data generation and experiment execution.
third_party_licenses.zip — License and attribution information for third-party datasets and resources.
\n
See the `README.md` (within `scripts.zip`) for detailed instructions on the dataset structure, software requirements, and how to reproduce the experiments.
\n\n
Within the CS4SEHAG project we developed a citizen science app to engage citizens into the documentation of high-alpine regions. To drive (initial) engagement, we estimated the position and orientation of over 100 historical terrestrial images from the AKON postcard portal of the Austrian National Library. This dataset contains the estimated camera parameters (position, orientation, focal length) as well as reference points, which were used to calculate the camera positions. For the estimation of the camera parameters we used moniQue, which is a QGIS plugin for the estimation of camera parameters and monoplotting. The positions in object space are provided in meters, whereas in image space (for the reference points) the coordinates are in pixels. For the Euler angles we used radians. All attributes with the suffix \"_std\" represent the standard deviations of the esimated parameter. For the reference points the residuals in image space are represented by the two attributes with the suffices \"_dx\" and \"_dy\". The coordinate system for each camera is given by its EPSG code and is either 3035 (ETRS89-extended / LAEA Europe) or 25832 (ETRS89 / UTM zone 32N).
\nThe images are mainly located in Tirol and South Tyrol and were acquired between 1890 and 1950. To get more information for each image we provide the link to the original metadata within the cameras.json. Furthermore, we provide example code to enable users to transform the provided Euler angels (describing the orientation of the camera) into a rotation matrix that can be further used.
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"2026-08-11", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?1.2", "subject": "Computer and information sciences", "scheme": "FOS"}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "https://tobiasbat.github.io/ChronoFuseGS/", "scheme": "url", "relation_type": {"id": "ispublishedin", "title": {"ar": "\u0646\u0634\u0631 \u0641\u064a", "cs": "Je publikov\u00e1n v", "de": "Ist ver\u00f6ffentlicht in", "en": "Is published in", "es": "Se publica en", "sv": "\u00c4r publicerad i"}}, "resource_type": {"id": "publication-conferencepaper", "title": {"de": "Konferenzbeitrag", "en": "Conference Paper"}}}], "rights": [{"id": "cc-by-4.0", "title": {"en": "Creative Commons Attribution 4.0 International"}, "description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "props": {"url": 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The site covers approximately 100 × 200 meters and includes weir and flood control structures built from stone, wood, steel, and concrete, multiple river branches, and surrounding vegetation.
\nThe dataset spans 8 recording days over 6.5 months (August 2025 – March 2026), capturing the site through seasonal vegetation changes, snow cover, flooding events, and active reinforcement and renovation work. All recordings were made with a consumer drone following pre-recorded flight plans, with 4K video exported as frames at regular intervals and downscaled to 1600 × 900 pixels. The dataset also includes detailed close-up recordings of specific scene areas. The dataset was created to evaluate ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization (currently under review)
\n\n
The dataset is distributed as individual zip archives. Three types of archives are provided:
\nnwm_<YYYY-MM-DD>.zip — Images and camera info (camera_info.json) for a single timestep.nwm_pre_trained_timestep_<YYYY-MM-DD>.zip — COLMAP model and pretrained 3DGS and PuP-3DGS models for a single timestep, used as input for ChronoFuseGS.nwm_<subset>.zip — Multi-temporal model trained and fused with ChronoFuseGS for a given subset (complete, autumn, flooding, snow, car).\n
We split the data into 11 timesteps in total. The two detail timesteps recorded at 2026-01-10 (2026-01-10_detail_1, 2026-01-10_detail_2) are only included in the Snow subset. The list of timesteps is shown below:
| Timestep | \nNumber of Input Images | \nEnvironmental Conditions | \n
|---|---|---|
| 2025-08-25 | \n226 | \nGreen vegetation | \n
| 2025-11-11 | \n271 | \nAutumn foliage | \n
| 2025-11-22 | \n255 | \nBare trees | \n
| 2025-11-23 | \n283 | \nBare trees, light snow, sunset | \n
| 2025-12-04_1 | \n284 | \nBare trees | \n
| 2025-12-04_2 | \n198 | \nBare trees | \n
| 2025-12-08 | \n223 | \nBare trees | \n
| 2026-01-10 | \n273 | \nSnow cover | \n
| 2026-01-10_detail_1 | \n180 | \nSnow cover | \n
| 2026-01-10_detail_2 | \n155 | \nSnow cover | \n
| 2026-03-28 | \n208 | \nEarly spring, bare trees, flooding | \n
Each timestep's image set is split into training and test images. Every 8th image is held out as a test image; the remaining images are used for training. The type field in camera_info.json indicates the split for each image (\"train\" or \"test\").
\n
We grouped these individual timesteps into the multi-temporal datasets of NWM, including the Complete dataset which covers all recording days, and smaller focused subsets. The table includes the number of registered images in the combined multi-temporal models.
\n| Subset | \nNum T. | \nDays | \nTimesteps | \nRegistered Images | \n
|---|---|---|---|---|
| Complete | \n9 | \n8 | \n2025-08-25, 2025-11-11, 2025-11-22,2025-11-23, 2025-12-04_1, 2025-12-04_2,2025-12-08, 2026-01-10, 2026-03-28 | \n2,221 | \n
| Autumn | \n4 | \n4 | \n2025-08-25, 2025-11-11,2025-11-22, 2025-12-08 | \n975 | \n
| Flooding | \n3 | \n3 | \n2025-12-04_2, 2025-12-08, 2026-03-28 | \n629 | \n
| Snow | \n3 | \n1 | \n2026-01-10, 2026-01-10_detail_1,2026-01-10_detail_2 | \n608 | \n
| Car | \n3 | \n1 | \n2025-12-04_1, 2025-12-04_2, 2025-12-08 | \n705 | \n
nwm_<YYYY-MM-DD>.zip — Contains the extracted drone video frames and camera metadata for a single recording day.
nwm_<YYYY-MM-DD>/\n\u2514\u2500\u2500 data/\n \u251c\u2500\u2500 input/ # extracted drone video frames\n \u2502 \u251c\u2500\u2500 <image>.png\n \u2502 \u2514\u2500\u2500 ...\n \u2514\u2500\u2500 camera_info.json\nnwm_pre_trained_timestep_<YYYY-MM-DD>.zip — Extends the individual timestep archive with a COLMAP sparse reconstruction, a pretrained 3DGS model, and PuP-3DGS compressed models.
nwm_pre_trained_timestep_<YYYY-MM-DD>/\n\u251c\u2500\u2500 data/\n\u2502 \u251c\u2500\u2500 images/ # images registered by COLMAP\n\u2502 \u251c\u2500\u2500 input/ # original extracted frames\n\u2502 \u251c\u2500\u2500 sparse/ # COLMAP sparse reconstruction\n\u2502 \u2514\u2500\u2500 camera_info.json\n\u251c\u2500\u2500 output/\n\u2502 \u2514\u2500\u2500 point_cloud/\n\u2502 \u251c\u2500\u2500 init_0/ # initial 3DGS model\n\u2502 \u2514\u2500\u2500 iteration_30000/ # model after 30,000 refinement iterations\n\u2502 \u251c\u2500\u2500 point_cloud.ply\n\u2502 \u251c\u2500\u2500 activation.ply\n\u2502 \u2514\u2500\u2500 camera.json\n\u2514\u2500\u2500 pup/ # PuP-3DGS compressed models\nnwm_<subset>.zip — Contains the ChronoFuseGS multi-temporal model trained and fused for a given subset. The structure mirrors the pretrained timestep archives without the pup/ folder.
nwm_<subset>/\n\u251c\u2500\u2500 data/\n\u2502 \u251c\u2500\u2500 images/\n\u2502 \u251c\u2500\u2500 input/\n\u2502 \u251c\u2500\u2500 sparse/\n\u2502 \u2514\u2500\u2500 camera_info.json\n\u2514\u2500\u2500 output/\n \u2514\u2500\u2500 point_cloud/\n \u251c\u2500\u2500 init_0/\n \u2514\u2500\u2500 iteration_30000/\n \u251c\u2500\u2500 point_cloud.ply\n \u251c\u2500\u2500 activation.ply\n \u2514\u2500\u2500 camera.json\nThe key output files are:
\npoint_cloud.ply — 3D Gaussian attributes (position, color, covariance, opacity, etc.) without any time-specific information.activation.ply — Per-Gaussian, timestep-dependent data: the light compensation vector and opacity manipulation vector.camera.json — Camera parameters for the trained model.camera_info.json — Per-image metadata including GPS coordinates, altitude, and train/test split. See the Camera Positions section for a full description.We extracted GPS coordinates from the camera logs and provide per-image coordinate information in the corresponding camera_info.json file.
[\n {\n \"image\": \"006_DJI_20250829153742_0593_D_1.png\",\n \"video_frame\": 60,\n \"src_video\": \"006_DJI_20250829153742_0593_D.MP4\",\n \"latitude\": 48.206373,\n \"longitude\": 16.230399,\n \"rel_alt\": 20.0,\n \"abs_alt\": 317.406,\n \"ecef\": [4089114.54278001, 1190350.6104078596, 4732436.830631929],\n \"registered\": true,\n \"type\": \"test\",\n \"t\": 0\n },\n {\n \"image\": \"006_DJI_20250829153742_0593_D_2.png\",\n \"video_frame\": 180,\n \"src_video\": \"006_DJI_20250829153742_0593_D.MP4\",\n \"latitude\": 48.206436,\n \"longitude\": 16.2306,\n \"rel_alt\": 20.0,\n \"abs_alt\": 317.406,\n \"ecef\": [4089105.352015008, 1190363.495616108, 4732441.499502797],\n \"registered\": true,\n \"type\": \"train\",\n \"t\": 0\n },\n ...\n]\nThis dataset contains time-resolved paired-end RNA-seq data from Escherichia coli BL21(DE3) cultivated under two process strategies: R2/A (instant_nofeed) and R3/B (ramp). Five matched process stages (T1–T5) were sampled for both strategies, covering the end of fed-batch cultivation through 6 hours of induced fed-batch cultivation. An additional mid-ramp sample (T1_5) is available only for R3/B.
The dataset includes raw reads, reference files, quality-control and mapping outputs, gene counts, normalized counts, annotated differential-expression results, and experimental-design files. The original processing scripts were not supplied by the analysis provider, but the available logs and software-version information are included. Sample definitions and further documentation are provided in the README.
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Licensed works, modifications, and larger works may be distributed under different terms and without source code."}, "props": {"url": "https://opensource.org/license/mit", "scheme": "spdx"}}], "description": "This repository contains the data presented in the manuscript entitled \"Suppression of sputtering in tungsten fuzz: Interplay of porosity and surface roughness\". It showcases the final data, hosts the jupyter notebooks to navigate the data set, plot the figures in the form that appear in the paper, and gives information on how, i.e., with which inputs, the data was generated in the first place.
\nThe directory ./Data/ contains the data that is illustrated and discussed in the above mentioned manuscript. The CC BY 4.0 license applies to these data.
The files ./Data/Porous_Sputter_Yields.csv and ./Data/Rough_Sputter_Yields.csv host the sputter yields of porous and solid but rough tungsten targets, respectively. They are mere text files in the form of Comma Separated Value tables. A header with column names is given in the first row. Units are eV for energy, degrees for angles and atoms/ion for sputter yields and uncertainties of sputter yields. The jupyter notebook Interactive_Visualisation.ipynb provides an interactive widget with which to navigate, select and plot parts of the data set.
The remaining files (./Data/Lopez_Cupak_AFM_1keV_Ar.txt, ./Data/Lopez_Cupak_AFM_2keV_Ar.txt, ./Data/Lopez_Cupak_Exp_1keV_Ar.txt, ./Data/Lopez_Cupak_Exp_2keV_Ar.txt) present data measured and simulated at TU Wien that are already published in \"Comparative study regarding the sputtering yield of nanocolumnar tungsten surfaces under Ar+ irradiation\". They were used in the discussion section and compared to original data our study.
The jupyter notebook Plotting.ipynb contains the python code used for data evaluation and repproduces the figures as they appear in the study. The resulting images are saved in the directory ./Figures/ as Portable Document Format files.
Data were obtained via the simulation suite SDTrimSP-3D. An exemplary input file is provided (./Data_generation/tri.inp). For the study the parameters symbol, alpha0, e0 and nr_pproj were varied, corresponding to the target element, angle of incidence, incidence projectile energy and simulation statistics. For a full explanation of all parameters as well as the underlying physical models, see the reports/manuals on SDTrimSP as well as SDTrimSP-3D.
In addition to the input of the physical model parameters, information on the three-dimensional structures of the targets is necessary. We used porous structures of different volume filling factors in the study. For each volume filling factor, we carried out three simulations for the sake of statistics. The corresponding 3D input files are given in the subdirectory of ./Data_generation/Fuzz_structures/, where each following directory name corresponds to the volume filling factor. The files 1.inp, 2.inp and 3.inp are the target input files we used. SDTrimSP-3D deals with only one target at a time; therefore, when running the simulations, only one of these three files per volume filling should be present in the simulation directory and they have to be renamed to fig.inp.
The directory ./Surface_Characteristics/ contains information on the surface envelopes of the otherwise porous fuzz structures. Samples were created in three runs, reflected by the naming of the subdirectories. The naming of the subsequent subdirectories give the nominal volume filling factor. In every one of these, the file rauh.inp is the corresponding SDTrimSP-3D input file, siad.txt gives the surface angle inclination distribution as extracted using Gwyddion, surface.pdf is a visualization and surface_xyz.txt gives the height values of the surface.
The notebook ./Surface_Characteristics/surface_characteristics.ipynb is used to extract data from these individual files and plots the figures that appear in the manuscript.
The notebooks Interactive_Visualisation.ipynb and Plotting.ipynb contain python code used to evaluate and plot (parts of) the data set. They were developed and tested in Python 3.10.12. The MIT license applies to these 2 files. Required packages in the corresponding versions can be installed via the provided file requirements.txt via the command pip install -r requirements.txt.
The matplotlib style sheet sets the default font to Roboto. Ensure that you have Roboto installed. Alternatively, change the line font.family : Roboto in single_column.mplstyle to a font of your choice or delete it entirely to revert to the matplotlib defaults.
To run the simulations, SDTrimSP-3D is necessary. Contact sdtrimsp@ipp.mpg.de for an academic license, or info@max-planck-innovation.de for a commercial license. For usage of SDTrimSP, see also the corresponding manuals (SDTrimSP as well as SDTrimSP-3D).
\n*.inp: Plain text. Input files for SDTrimSP or SDTrimSP-3D simulatitons.*.mplstyle: Plain text. Style sheet defining parameters for matplotlib to achieve the styling of the figures. The figures use the Roboto typeface, which might not be installed by default. This dataset contains a curated list of 49 research papers focused on Conversational Recommender Systems using Generative Models (Gen-CRS). The collection covers publications from 2018 to 2025 and reflects the rapid evolution of generative approaches in conversational recommendation scenarios.
\n
The dataset was compiled in the context of the literature review “Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review” and the tutorial “A Tutorial on Recent Advances in Generative Conversational Recommender Systems”, presented at the ACM RecSys conference 2025. It serves as the bibliographic foundation for both contributions and is intended to support transparency, reproducibility, and further research in this area.
Each entry in the dataset corresponds to a single paper relevant to Gen-CRS, and the selection process, collection methodology, and inclusion criteria are provided in the accompanying literature review paper.
\nThe dataset is organized in a tabular format, where each row corresponds to a single publication included in the literature collection. Rows contain the essential bibliographic metadata required to identify and retrieve the original paper.
\nIn 2026, the dataset was extended with an additional file containing 55 new publications from 2025 and 2026. This extension was compiled in the context of an updated edition of the tutorial “A Tutorial on Recent Advances in Generative Conversational Recommender Systems”, presented at IJCAI-ECAI 2026.
\nThe extension complements the original collection with research that was not included in the initial dataset and captures recent developments in Generative Conversational Recommender Systems published in 2025 and 2026. It contains 36 publications from 2025 and 19 publications from 2026 and follows the same tabular structure as the original dataset.
\nThe original dataset of 49 publications remains unchanged, preserving the bibliographic collection underlying the literature review and the ACM RecSys 2025 tutorial.
\nTogether, the original dataset and its 2026 extension comprise 104 unique publications covering the period from 2018 to 2026.
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{"id": "dataset", "title": {"de": "Datensatz", "en": "Dataset"}}, "creators": [{"person_or_org": {"type": "personal", "name": "Rhomberg-Kauert, Jan", "given_name": "Jan", "family_name": "Rhomberg-Kauert", "identifiers": [{"identifier": "0009-0001-5214-9697", "scheme": "orcid"}]}, "role": {"id": "contactperson", "title": {"ar": "\u0645\u0633\u0624\u0648\u0644 \u0627\u0644\u0627\u062a\u0635\u0627\u0644", "cs": "Kontaktn\u00ed osoba", "de": "Kontaktperson", "en": "Contact person", "es": "Persona de contacto", "sv": "Kontaktperson"}}, "affiliations": [{"id": "04d836q62", "name": "TU Wien", "identifiers": [{"identifier": "04d836q62", "scheme": "ror"}, {"identifier": "grid.5329.d", "scheme": "grid"}, {"identifier": "0000 0004 1937 0669", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Dammert, Lucas", "given_name": "Lucas", "family_name": "Dammert", "identifiers": [{"identifier": "0009-0007-0029-2372", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Theresa, Himmelsbach", "given_name": "Himmelsbach", "family_name": "Theresa", "identifiers": [{"identifier": "0009-0005-4504-4247", "scheme": "orcid"}]}, "affiliations": [{"name": "Universit\u00e4t Innsbruck"}]}, {"person_or_org": {"type": "personal", "name": "Florian, P\u00f6ppl", "given_name": "P\u00f6ppl", "family_name": "Florian", "identifiers": [{"identifier": "0000-0001-9397-4370", "scheme": "orcid"}]}, "affiliations": [{"name": "RIEGL Laser Measurement Systems GmbH"}]}, {"person_or_org": {"type": "personal", "name": "Martin, Pfennigbauer", "given_name": "Pfennigbauer", "family_name": "Martin"}, "affiliations": [{"name": "RIEGL Research and Defense GmbH"}]}, {"person_or_org": {"type": "personal", "name": "Mandlburger, Gottfried", "given_name": "Gottfried", "family_name": "Mandlburger", "identifiers": [{"identifier": "0000-0002-2332-293X", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}], "title": "Evaluation of full-waveform bottom return extraction in whitewater rapids using bathymetric LiDAR dataset", "publisher": "TU Wien", "publication_date": "2026-08-10", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?1.2", "subject": "Computer and information sciences", "scheme": "FOS"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?1.5", "subject": "Earth and related environmental sciences", "scheme": "FOS"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?2", "subject": "Engineering and technology", "scheme": "FOS"}], "related_identifiers": [{"identifier": "10.1117/12.3068055%20", "scheme": "doi", "relation_type": {"id": "isdescribedby", "title": {"ar": "\u062a\u0645\u0651 \u0648\u0635\u0641\u0647 \u0628\u0648\u0627\u0633\u0637\u0629", "cs": "Je pops\u00e1n (\u010d\u00edm)", "de": "Wird beschrieben von", "en": "Is described by", "es": "Es descrito por", "sv": "Beskrivs av"}}, "resource_type": {"id": "publication-conferencepaper", "title": {"de": "Konferenzbeitrag", "en": "Conference Paper"}}}, {"identifier": "10.1002/rra.70109%20", "scheme": "doi", "relation_type": {"id": "issupplementedby", "title": {"ar": "\u064a\u064f\u0633\u062a\u0643\u0645\u0644 \u0628\u0640", "cs": "M\u00e1 dopln\u011bk (co)", "de": "Wird erg\u00e4nzt durch", "en": "Is supplemented by", "es": "Es complementado por", "sv": "Supplementerad av"}}, "resource_type": {"id": "publication-article", "title": {"de": "Zeitschriftenartikel", "en": "Journal Article"}}}, {"identifier": "https://github.com/JanRhoKa/_whitewater_LiDAR", "scheme": "url", "relation_type": {"id": "isversionof", "title": {"ar": "\u0647\u0648 \u0625\u0635\u062f\u0627\u0631 \u0644\u0640", "cs": "Je verz\u00ed (\u010deho)", "de": "Ist eine Version von", "en": "Is version of", "es": "Es versi\u00f3n de", "sv": "\u00c4r version av"}}, "resource_type": {"id": "software", "title": {"de": "Software", "en": "Software"}}}], "rights": [{"id": "cc-by-4.0", "title": {"en": "Creative Commons Attribution 4.0 International"}, "description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "props": {"url": "https://creativecommons.org/licenses/by/4.0/", "scheme": "spdx"}}, {"id": "mit", "title": {"en": "MIT License"}, "description": {"en": "A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code."}, "props": {"url": "https://opensource.org/license/mit", "scheme": "spdx"}}], "description": "Dataset used in the study \"Evaluation of full-waveform bottom return extraction in whitewater rapids using bathymetric LiDAR\" (Rhomberg-Kauert et al., 2025a).
\nStudy Abstract
The application of LiDAR and remote sensing methods has long been considered challenging or unfeasible in the turbulent waters of whitewater rapids. Therefore, this study presents a method to survey whitewater rapids using signal processing of full-waveform LiDAR recordings. The subtraction of an idealized water column backscattering response from the recorded waveform and further analysis of the remaining signal allows the extraction of bottom echoes on a single-waveform basis. To evaluate the extracted points, we surveyed a block ramp with both total station measurements and helicopter-based bathymetric LiDAR. We could demonstrate an increase in the water bottom coverage in whitewater areas through the application of the presented method, which is able to extract bottom echoes for 70% of the waveforms in the surveyed area. This improves LiDAR full-waveform processing by extracting bottom echoes where current processing algorithms were not able to and thus reducing the mean absolute vertical distance to the reference data from 43.9 cm to 13.3 cm.
| \n | id | \nwfm | \n
|---|---|---|
| 0 | \n562282.0 | \n[[535, 69], [536, 316], [537, 753], [538, 1260... | \n
| 1 | \n562363.0 | \n[[538, 238], [539, 1402], [540, 2794], [541, 2... | \n
| \n | x | \ny | \nz | \nwfm_sbl_id | \nreflectance | \namplitude | \n... | \nwfm_ampl_offset | \n
|---|---|---|---|---|---|---|---|---|
| 0 | \n-3023.950680 | \n-6780.087890 | \n260.26425 | \n3206350.0 | \n-17.820000 | \n18.250000 | \n... | \n-8.128573e-08 | \n
| 1 | \n-3021.864260 | \n-6781.127930 | \n260.35101 | \n3210972.0 | \n-14.090000 | \n21.980000 | \n... | \n-8.128468e-08 | \n
| \n | x | \ny | \nz | \n
|---|---|---|---|
| 0 | \n-3029.306114 | \n-6776.488996 | \n259.104533 | \n
| 1 | \n-3029.149941 | \n-6775.951857 | \n259.128578 | \n
\n
To run the notebooks and the provided code of the method, the following folder structure has to be implemented after downloading the files:
\nAdditionally, to run the method, the following packages are required (requirements.txt):
\n-----
matplotlib==3.10.8
numpy==2.2.6
pandas==2.3.3
scipy==1.15.3
seaborn==0.13.2
scikit-learn==1.7.2
-----
IPython 8.38.0
jupyter_client 8.8.0
jupyter_core 5.9.1
jupyterlab 4.5.2
notebook 7.5.2
-----
The development of new, broadly applicable methods for amide bond formation remains a central objective in synthesis, particularly given the prevalence of amides in medicinally relevant compounds. Despite the value of 2-ynamides as versatile structural motifs, existing aminocarbonylative approaches rely exclusively on Pd catalysis and require relatively harsh conditions. Herein, we disclose a visible-light-initiated, Ni-catalyzed aminocarbonylation of alkynyl iodides that proceeds under low carbon monoxide pressure, providing an earth-abundant catalytic platform for accessing 2-ynamides. Mechanistic investigations support the light-triggered generation of catalytically active Ni(I) species that initiate a thermally sustained carbonylative Ni(I)/Ni(III) manifold. The method features operational simplicity and a broad substrate scope, accommodating a wide range of alkynyl iodides and amines to afford the corresponding 2-ynamides in good to excellent yields.
\nA compound list is provided, matching each compound name and number with the corresponding experiment number. The uploaded ZIP files contain the NMR spectra for all substrates and products, as well as ATR-IR and HRMS data for all new compounds. Specialized software, such as MestreNova and MassHunter, is required to open and process the NMR and HRMS data. ATR-IR data are provided as CSV files.
\nThe publication and its Supporting Information can be found as open-access files on the publisher's website.
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\nOral cancer patients diagnosed with advanced metastatic disease face a 5-year survival rate of approximately 30 %. Although metastasis is the leading cause of cancer-related mortality, effective treatment options remain limited. In previous studies, we leveraged intratumoral heterogeneity of oral cancer cells to isolate clonal lines with distinct invasive properties. Integrative expression analyses identified lymphocyte cell-specific protein-tyrosine kinase (LCK) as a driver and therapeutic target for metastasis. However, its downstream mechanisms remained unclear. Here, we show that LCK modulates expression of peroxisome proliferator-activated receptor gamma (PPARG), a key regulator of lipid metabolism. Using 3D invasion assays, untargeted lipidomics, and transcriptomics, we found that PPARG activity reduced lipid storage while enhancing fatty acid synthesis, membrane remodeling, and cellular invasiveness downstream of LCK. Notably, PPARG upregulated stearoyl-CoA desaturase (SCD), which converts saturated to monounsaturated fatty acids, thereby limiting lipid peroxide accumulation, a trigger of ferroptosis. Importantly, SCD inhibition reduced cancer cell viability, particularly upon PPARG activation by rosiglitazone. Together, our findings identify an previously undescribed oncogenic LCK/PPARG/SCD axis that modulates lipid composition to promote metastasis. While this gives concerning context to ongoing studies of PPARG activation as anti-carcinogenic, targeting this signaling pathway offers a promising strategy for anti-metastatic therapy in oral cancer.
\n4x10^5 cells were grown for 24 h on a 6-well plate were gently scraped off the plates in PBS, centrifuged and resuspended in 150 µl water. Consequently, 200 µl methanol, 400 µl chloroform and 15 µl 1 M hydrochloric acid were added and the solution was vortexed and incubated on a shaker for 1 h at 550 rpm. Subsequently, after centrifugation at 2000 g for 10 min, the organic phase was transferred to a new vial. The aqueous phase was mixed with another 100 µl methanol, 200 µl chloroform and 10 µl 1 M hydrochloric acid and extraction was repeated. The combined organic phases were dried under a nitrogen stream and dissolved in 50 µL dichloromethane.
\nThe employed frontend was a Thermo Fisher Scientific Vanquish H UHPLC with a Waters Acquity BEH C18 column (150 mm × 1 mm × 1.7 µm; Waters Corporation, Milford, MA, USA). Mobile phase A was 60% acetonitrile in aqueous 10 mM ammonium formate; mobile phase B was 10% acetonitrile in isopropanol supplemented with 10 mM ammonium formate. The following gradient was employed at a constant flow rate of 70 µL/min and 40 °C: 0 min; 2% B, 7 min; 2% B, 20 min; 100% B, 25 min; 100% B, followed by 5 min re-equilibration to starting conditions. Lipids were analyzed employing an untargeted lipidomics workflow with separate positive and negative polarity runs on a Bruker timsTOF Pro equipped with a VIP-HESI source (Bruker Corporation, Billerica, MA, USA). Mass spectrometry (MS) was operated in PASEF mode (collision energy 30 eV) with a full scan in the range of 100-2200 m/z followed by two PASEF ramps resulting in a total cycle time of 0.32 s.
\nData were analyzed with Bruker Compass MetaboScape 2022b Version 9.0.1 (Build 11878). Features were annotated by the rule based MCube LipidSpecies annotation algorithm and the MS-DIAL LipidBlast (version 68) spectral databases.
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"Kontaktperson"}}, "affiliations": [{"name": "TU Wien"}]}], "title": "Sensitivity Analysis of SDOclust Hyperparameters", "publisher": "TU Wien", "publication_date": "2026-08-01", "subjects": [{"subject": "Clustering"}, {"subject": "Distance-Based Methods"}, {"subject": "Parameter Sensitivity"}, {"subject": "Robustness Analysis"}], "rights": [{"id": "mit", "title": {"en": "MIT License"}, "description": {"en": "A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code."}, "props": {"url": "https://opensource.org/license/mit", "scheme": "spdx"}}, {"id": "cc-by-4.0", "title": {"en": "Creative Commons Attribution 4.0 International"}, "description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "props": {"url": "https://creativecommons.org/licenses/by/4.0/", "scheme": "spdx"}}], "description": "This artifact was created for the paper \"Limits of Clustering Models Based on Distance Similarity: A Sensitivity Study of SDOclust\" (Khazari, Zseby & Iglesias, SISAP 2026). It studies under which conditions SDOclust — a lightweight, model-based, noise-robust clustering method — remains reliable, by analyzing the sensitivity of its main hyperparameters (model size k\u2080, connectivity rank χ, minimum representativeness e, and global-local weighting ζ) across controlled scenarios of dataset size, dimensionality, number of clusters, cluster overlap/density, non-globular shapes, and outlier proportion/dispersion. The artifact provides full reproducibility of this analysis.
\nPlease cite our work as:
\n@inproceedings{KhazariZsebyIglesias2026SDOclustSensitivity,\n author = {Khazari, Sabina and Zseby, Tanja and Iglesias, F{\\'e}lix},\n title = {Limits of Clustering Models Based on Distance Similarity: A Sensitivity Study of SDOclust},\n booktitle = {Proceedings of the 19th International Conference on Similarity Search and Applications (SISAP 2026)},\n year = {2026},\n address = {Brno, Czechia},\n month = oct,\n note = {To appear},\n}\nThe artifact provides code and results for the full sensitivity analysis:
\nouts, clus, inspread, outspread, dims, factorial, size), each run producing ALL_combinations_LONG.csv (ARI, execution time, effective parameters) and args.json.Full setup and usage instructions are in the included README.md.
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"name": "Gospodaric, J", "given_name": "J", "family_name": "Gospodaric"}, "role": {"id": "datacollector", "title": {"ar": "\u0645\u062c\u0645\u0651\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a", "cs": "Sb\u011bratel dat", "de": "DatensammlerIn", "en": "Data collector", "es": "Recolector de datos", "sv": "Datainsamlare"}}}, {"person_or_org": {"type": "personal", "name": "Novik, E", "given_name": "E", "family_name": "Novik"}, "role": {"id": "datacollector", "title": {"ar": "\u0645\u062c\u0645\u0651\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a", "cs": "Sb\u011bratel dat", "de": "DatensammlerIn", "en": "Data collector", "es": "Recolector de datos", "sv": "Datainsamlare"}}}, {"person_or_org": {"type": "personal", "name": "Kvon, Z.D.", "given_name": "Z.D.", "family_name": "Kvon"}, "role": {"id": "datacollector", "title": {"ar": "\u0645\u062c\u0645\u0651\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a", "cs": "Sb\u011bratel dat", "de": "DatensammlerIn", "en": "Data collector", "es": "Recolector de datos", "sv": "Datainsamlare"}}}], "title": "Band structure near the Dirac point in HgTe quantum wells with critical thickness", "publisher": "TU Wien", "publication_date": "2026-08-04", "subjects": [{"subject": "Dirac bands"}, {"subject": "Cyclotron resonance"}, {"subject": "Terahertz"}, {"subject": "HgTe"}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "10.3390/nano12142492", "scheme": "doi", "relation_type": {"id": "issupplementto", "title": {"ar": "\u0645\u0644\u062d\u0642 \u0644\u0640", "cs": "Je dopl\u0148kem k (\u010demu)", "de": "Erg\u00e4nzt", "en": "Is supplement to", "es": "Es un suplemento de", "sv": "\u00c4r supplement till"}}, "resource_type": {"id": "dataset", "title": {"de": "Datensatz", "en": "Dataset"}}}], "rights": [{"id": "cc-by-4.0", "title": {"en": "Creative Commons Attribution 4.0 International"}, "description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "props": {"url": "https://creativecommons.org/licenses/by/4.0/", "scheme": "spdx"}}], "description": "These are the data for the paper:
\n A. Shuvaev, V. Dziom, J. Gospodari\u010d, E. G. Novik, A. A. Dobretsova, N. N. Mikhailov, Z. D. Kvon, and A. Pimenov
“Band structure near the Dirac point in HgTe quantum wells with critical thickness”
Nanomaterials 12, 2492 (2022). https://doi.org/10.3390/nano12142492
\n
File format: \"OriginPro\" (OriginLab Corporation)
\nFor the free Viewer, see the link: https://www.originlab.com/viewer/ . All the necessary information is in the Q&A section.
\nThe file \"Cyclotron_Resonance_HgTe.oggu\" provides the magneto-transmission of a circularly polarized light in HgTe quantum wells with critical
thickness at T = 1.8K and n = 950 GHz. Sample #1 was measured as function of the gate voltage, sample #2 as function of the illumination time, with the charge density is varied via light illumination. Negative and positive magnetic fields correspond to negatively and positively charged quasiparticles, respectively. Acronyms: D.E.—Dirac electrons, D.H.—Dirac holes, M.H.— massive holes (see text for details).
The file \"Mass_Density.oggu\" provides the parameters of the charge carriers as extracted from the Drude fits of the cyclotron resonance.
The file \"Band_Structure_HgTe.oggu\" shows the band structure of HgTe films with critical thickness. Symbols are experimental data from
two samples, lines - kp-theory. The solid and dashed lines are split bands due to asymmetric potential and to bulk inversion asymmetry. The
different slopes for electrons and holes are due to a lifted hole degeneracy. The flattening of the hole dispersion at high values of k is due
to approaching of the hole pockets around k ~ 0.5 1/nm, E ~15meV.
These are the data for the paper:
\n A. Shuvaev, V. Dziom, J. Gospodari\u010d, E. G. Novik, A. A. Dobretsova, N. N. Mikhailov, Z. D. Kvon, and A. Pimenov
“Band structure near the Dirac point in HgTe quantum wells with critical thickness”
Nanomaterials 12, 2492 (2022). https://doi.org/10.3390/nano12142492
\n
Abstract: Mercury telluride (HgTe) thin films with a critical thickness of 6.5nm are predicted to possess a gapless Dirac-like band structure. We report a comprehensive study on gated and optically doped samples by magnetooptical spectroscopy in the THz range. The quasi-classical analysis of the cyclotron resonance allowed the mapping of the band dispersion of Dirac charge carriers in a broad range of electron and hole doping. A smooth transition through the charge neutrality
point between Dirac holes and electrons was observed. An additional peak coming from a second type of holes with an almost density-independent mass of around 0.04 m_0 was detected in the hole-doping range and attributed to an asymmetric spin splitting of the Dirac cone. Spectroscopic evidence for disorder-induced band energy fluctuations could not be detected in present cyclotron
resonance experiments.
This folder includes raw and treated data resulting from measurements performed in the near-field zone of the Rhône River mouth in Lake Geneva, as well as MATLAB scripts that can be used for their analysis. The raw data folder includes moored ADCP measurement data of the velocity field and signal backscatter in the Rhône River subaqueous channel in Lake Geneva from May until August 2017, as well as infrared remote imagery data of the Lake Geneva surface near the Rhône River mouth for 30 May 2017, 15 June 2017 and 2 July 2019. The treated data folder includes processed lake, river and inflow data (temperature, discharge, salinity, sediment concentration, density, Froude number, relative density difference and densimetric Froude number) that was based on data originally generated by the Commission Internationale pour la Protection des Eaux du Léman (CIPEL; Rimet et al., 2020) at the monitoring station SHL2 and the Swiss Federal Office for the Environment (FOEN) at the monitoring station at Porte du Scex (https://www.hydrodaten.admin.ch/de/2009.html).
\nThese datasets were reported in the paper \"Direct Observations of Lacustrine Turbidity Currents Triggered by Delta-lip Failures\" and can be downloaded here for validation and further analysis. The scripts used to open, treat and visualize the data as they are in the paper are included, as well as the figures included in the paper and the processed data visualized in the figures. The structure and contents of the folders are described in the README file. In order to run the scripts (.m) and open some of the treated data (.mat) successfully, the MATLAB software package is necessary. The MATLAB version used for the data treatment is MATLAB R2025b.
\nAll data are protected under the Creative Commons Attribution 4.0 International license. All scripts are protected under the GNU General Public License v3.0 (or later)
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