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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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": "\n
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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\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": "Climate Exposure Maps and Distributions of Austrian Power Grid Overhead Lines", "publisher": "TU Wien", "publication_date": "2026-08-26", "subjects": [{"subject": "climate change"}, {"subject": "power infrastructure"}, {"subject": "climate exposure"}, {"subject": "spatial correlation"}, {"subject": "cumulative distribution"}, {"subject": "OpenStreetMap"}], "dates": [{"date": "2026-08-26", "type": {"id": "updated", "title": {"ar": "\u0645\u062d\u064a\u0651\u0646", "cs": "Aktualizov\u00e1no", "de": "Aktualisiert", "en": "Updated", "es": "Actualizado", "sv": "Uppdaterad"}}, "description": "The dataset now includes images and distributions for all 41 climate indicators. Due to misplaced comments in the script, some were missing in the first version."}], "related_identifiers": [{"identifier": "10.5281/zenodo.14697703", "scheme": "doi", "relation_type": {"id": "isderivedfrom", "title": {"ar": "\u0645\u0634\u062a\u0642 \u0645\u0646", "cs": "Je odvozen od (\u010deho)", "de": "Wird abgeleitet von", "en": "Is derived from", "es": "Se deriva de", "sv": "H\u00e4rr\u00f6r fr\u00e5n"}}, "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"}}, {"id": "odbl-1.0", "title": {"en": "ODC Open Database License v1.0"}, "description": {"en": ""}, "props": {"url": "https://spdx.org/licenses/ODbL-1.0", "scheme": "spdx"}}], "description": "The data contains figures (jpg data format) displaying the maps and cumulative curves of overhead line length vs. 41 climate indicators
\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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"grid.5329.d", "scheme": "grid"}, {"identifier": "0000 0004 1937 0669", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "K\u00e1n, Peter", "given_name": "Peter", "family_name": "K\u00e1n", "identifiers": [{"identifier": "0000-0001-7437-9955", "scheme": "orcid"}]}, "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": "Kaufmann, Hannes", "given_name": "Hannes", "family_name": "Kaufmann", "identifiers": [{"identifier": "0000-0002-0322-9869", "scheme": "orcid"}]}, "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": "No Wolf in the Meadow (NWM)", "publisher": "TU Wien", "publication_date": "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": "https://creativecommons.org/licenses/by/4.0/", "scheme": "spdx"}}], "description": "No Wolf in the Meadow (NWM) is a real-world outdoor image dataset of a flood control system in Vienna, Austria. 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.
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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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{"resource_type": {"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": 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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
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| 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
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| 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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"title": "A LCK/PPARG/SCD Lipid Metabolism Axis Promotes Invasion in Human Oral Cancer", "publisher": "TU Wien", "publication_date": "2026-07-30", "subjects": [{"subject": "lipidomics"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?1.6", "subject": "Biological sciences", "scheme": "FOS"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?3.4", "subject": "Health biotechnology", "scheme": "FOS"}, {"subject": "oral carcinoma"}], "dates": [{"date": "2022-02", "type": {"id": "collected", "title": {"ar": "\u0645\u062c\u0645\u0651\u0639", "cs": "Sebran\u00e9", "de": "Gesammelt", "en": "Collected", "es": "Recolectado", "sv": "Samlade"}}}, {"date": "2023-12", "type": {"id": "collected", "title": {"ar": "\u0645\u062c\u0645\u0651\u0639", "cs": "Sebran\u00e9", "de": "Gesammelt", "en": "Collected", "es": "Recolectado", "sv": "Samlade"}}}], "identifiers": [{"identifier": "unpublished data", "scheme": "other"}], "related_identifiers": [{"identifier": "10.1186/s12943-021-01384-w", "scheme": "doi", "relation_type": {"id": "cites", "title": {"ar": "\u064a\u0633\u062a\u0634\u0647\u062f \u0628\u0640", "cs": "Cituje (co)", "de": "Zitiert", "en": "Cites", "es": "Citas", "sv": "Citerar"}}, "resource_type": {"id": "publication-article", "title": {"de": "Zeitschriftenartikel", "en": "Journal Article"}}}], "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": "This dataset contains raw files as .zip from negative scan mode, untargeted lipidomics analysis of either differentially invasive human oral squamous cell carcinoma cell line (SAS) clones in biological triplicates (files starting with D, C, F, H, L, S) and negative scan untargeted lipidomics of siRNA control and siLCK parental SAS cells treated with PPARgamma agonist (Rosiglitasone) or antagonist (GW966; files starting with JP) in quadruplicates per condition/treatment. In addition, metadata file and Metaboscape exports of annotated features are included.
\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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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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"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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the Bitumen Black Space Model (B2SM), a mathematical framework developed to describe the rheological behaviour of bituminous binders in Black Space Diagrams derived from Dynamic Shear Rheometer (DSR) measurements. The model provides a continuous, bounded representation of the relationship between complex shear modulus (|G*|) and phase angle (δ), enabling quantitative analysis of unmodified and polymer-modified binders.
\nThe software accompanies the associated research publication and provides the implementation used for model development, parameter estimation, and validation.
\nThe repository provides an open-source implementation of the B2SM methodology described in the accompanying publication. It enables researchers to
\nDetailed instructions for installation, data formatting, workflow, and execution are provided in the repository README.
\nThe repository serves as the reference software implementation accompanying the associated publication and archived research dataset.
\nThe software implements three related Black Space models:
\nIn addition, the repository contains routines for constructing rheological master curves using Kaelble temperature shift factors and a modified CAM modulus model, allowing simultaneous prediction of complex modulus and phase angle over a wide reduced-frequency range.
\nThe implementation is intended as a transparent research reference rather than a general-purpose engineering software package. It is designed to support reproducible research, comparison of alternative modelling approaches, and future development of Black Space modelling methodologies.
\nThe B2SM is a descriptive rheological model rather than a constitutive material model. Consequently, the fitted parameters should be interpreted as descriptors of Black Space curve geometry rather than intrinsic material properties. Model quality depends on the quality and coverage of the experimental DSR data and assumes measurements are obtained within the linear viscoelastic region.
\nIn addition to the software, the repository includes a small set of representative example DSR datasets that demonstrate the required input format and allow users to reproduce the analysis workflow. The complete experimental dataset used in the accompanying publication is archived separately in the associated research data repository.
\nThe GNU General Public License holds for the python script. The Creative Commons Attibution is here for the dataset provided with exemplary data for the model.
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"given_name": "Bernhard", "family_name": "Hofko", "identifiers": [{"identifier": "0000-0002-8329-8687", "scheme": "orcid"}]}, "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": "Grothe, Hinrich", "given_name": "Hinrich", "family_name": "Grothe", "identifiers": [{"identifier": "0000-0002-2715-1429", "scheme": "orcid"}]}, "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": "Bitumen Black Space Model (B2SM) for Thermorheologically Complex Materials (dataset)", "publisher": "TU Wien", "publication_date": "2026-07-15", "subjects": [{"subject": "Bitumen"}, {"subject": "Polymer modified bitumen"}, {"subject": "Black Space Diagram"}, {"subject": "Bitumen Black Space Model (B2SM)"}, {"subject": "Thermorheological complexity"}, {"subject": "Viscoelasticity"}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "10.48436/awmzv-fe916", "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": "model", "title": {"de": "Modell", "en": "Model"}}}], "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": "This dataset was created in the field of bitumen rheology and pavement materials research. It supports the development and validation of the Bitumen Black Space Model (B2SM), a mathematical framework for quantitatively describing the rheological behaviour of unmodified, polymer-modified and oil modified bituminous binders using Black Space Diagrams derived from Dynamic Shear Rheometer (DSR) measurements.
\nThe dataset provides experimental rheological data collected from multiple independent research groups to evaluate the robustness and applicability of the proposed B2SM across a wide range of binder types, ageing conditions, and testing methodologies. By combining data originating from several laboratories, the dataset captures a broad spectrum of rheological responses representative of both conventional and polymer-modified binders.
\nThe data are intended for:
\nvalidation of the Bitumen Black Space Model (B2SM);
\ncomparison of rheological behaviour between different binder types;
\ninvestigation of ageing effects on binder rheology;
\ndevelopment and benchmarking of alternative rheological models;
\nreproducible research in bitumen rheology.
\nThe dataset consists of Dynamic Shear Rheometer (DSR) frequency sweep measurements collected by six independent research groups (Research Groups A–F), representing four academic research institutions and two industrial partners. In total, 172 binder datasets are included.
\nThe dataset contains both unmodified, polymer-modified and oil-modified binders originating from various crude oil sources, refinery processes, penetration grades, and polymer modifications. The modified binders range from linear or radial SBS, to polypropylene, to also rejuvenated binders or oil modified, like re-refined engine oil bottoms (REOB), all with varying degrees of dosages.
\nMany binders were evaluated at multiple laboratory ageing states. These ranged from short term protocols like the Rolling Thin Film Oven Test (RTFOT) and the Viennese Binder Ageing equivalent PreVBA; to also long-term Pressure Ageing Vessel (PAV) at 20 to 80h of PAV conditioning and specialised ageing protocols such as the Viennese Binder Ageing (VBA) with Ozone, NOx, light, 75g/m3 H2O humidity and Reactive Oxygen Species (ROS). The Viennese Binder Ageing protocols have been developed since several years to better mimic environmental conditions of asphalt pavements, rather than the conventional thermal ageing. Additionally, field aged binders were analysed as well, marked as for example F018, representing the 18 months that these binders were subjected to natural environmental conditions of ageing.
\nAlthough all data originate from DSR frequency sweep measurements, the participating research groups employed different experimental settings, including varying temperature ranges, frequency ranges, step sizes, and plate geometries. These variations are preserved within the dataset, providing a realistic representation of inter-laboratory variability while maintaining the original measurement conditions.
\nThe repository contains the experimental datasets together with documentation and an example implementation of the Bitumen Black Space Model (B2SM).
\nThe data are organised by research group (A–F), with individual files corresponding to specific binders, modifications and ageing conditions. File names identify the respective binder and testing condition, allowing each measurement to be traced to its source while maintaining a consistent naming convention throughout the repository.
\nEach dataset contains the rheological quantities required for Black Space Diagram analysis, including the complex shear modulus (|G*|), phase shift angle (δ), and the corresponding testing conditions (frequency and temperature).
\nThe experimental data are provided in standard file formats (.csv) that can be accessed using common spreadsheet software or scientific computing environments.
\nA separate repository entry provides a reference Python implementation of the Bitumen Black Space Model (B2SM). The implementation demonstrates how to load the dataset, apply the mathematical formulation of the B2SM, and reproduce representative Black Space Diagram analyses. It is intended as a transparent reference implementation to support reproducibility and may be adapted or extended for future research.
\nA separate repository entry contains the reference implementation of the Bitumen Black Space Model (B2SM), while the corresponding scientific publication provides the theoretical background, mathematical derivation of the model, description of the fitting procedure, and interpretation of the model parameters. Together, these resources complement the experimental dataset and support its reuse for rheological analysis and model development.
\nThe data were collected by multiple independent research groups using their respective experimental protocols. Consequently, differences in testing temperatures, frequency ranges, measurement resolution, and laboratory procedures should be regarded as inherent characteristics of the dataset rather than inconsistencies
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This dataset comprises a set of images of gene trees analysing the polyphenol oxidase (PPO) and amino acid transporter (AAT) gene families across angiosperms, in particular legumes. The purpose of this set of figures is to provide the full gene trees that are the source of subsampled gene trees presented in Chapter 3 of the PhD thesis of Denise Blume, Understanding Specialized Metabolism in Faba Bean: From Biosynthetic Pathways to Crop Improvement.
\nThis dataset includes:
\nSupplementary Figure 13. Taxon-labeled maximum likelihood legume PPO gene tree with amino acid branch lengths and support values. Branch values are support from 1000 rapid bootstrap replicates and the SH-aLRT, respectively (RBS/SH-aLRT). Branch lengths are expected substitutions per site.
\nSupplementary Figure 14. Taxon-labeled maximum likelihood angiosperm PPO gene tree inferred from amino acid sequences. Branch values are support from 1000 rapid bootstrap replicates and the SH-aLRT, respectively (RBS/SH-aLRT). Branch lengths are expected substitutions per site.
\nSupplementary Figure 15. Taxon-labeled maximum likelihood angiosperm PPO gene tree inferred from nucleotide sequences. Branch values are support from 1000 rapid bootstrap replicates and the SH-aLRT, respectively (RBS/SH-aLRT). Branch lengths are expected substitutions per site.
\nSupplementary Figure 16. Taxon-labeled maximum likelihood angiosperm AAT gene tree with amino acid branch lengths and support values. Branch values are support from 1000 rapid bootstrap replicates and the SH-aLRT, respectively (RBS/SH-aLRT). Branch lengths are expected substitutions per site.
\nImage files are provided in Portable Document Format (PDF/A) and can be opened using any standard PDF viewer (e.g. Adobe Acrobat Reader, Preview on macOS, Adobe Acrobat Pro, or Foxit PDF Editor).
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contacto", "sv": "Kontaktperson"}}, "affiliations": [{"name": "TU Wien"}]}], "title": "TuwTSG.v1 Benchmark for Time Series Clustering: Dataset Collection, Generator, and Evaluation Experiments", "publisher": "TU Wien", "publication_date": "2026-07-25", "related_identifiers": [{"identifier": "https://github.com/CN-TU/TuwTSG", "scheme": "url", "relation_type": {"id": "continues", "title": {"ar": "\u062a\u0633\u062a\u0643\u0645\u0644", "cs": "Pokra\u010duje (\u010d\u00edm)", "de": "Setzt fort", "en": "Continues", "es": "Contin\u00faa", "sv": "Forts\u00e4tter"}}, "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": "This dataset collection was created as part of the research presented in the paper \"On the Synthetic Evaluation of Time Series Clustering.\" The project addresses the need for controlled, synthetic benchmarks to evaluate time series clustering algorithms, complementing existing real-world archives such as the UCR Time Series Archive.
\nThe purpose of this artifact is twofold: (1) to provide TuwTSG.v1, a benchmark collection of synthetic time series datasets organized into eight categories (EVOLVING, MIXED, SCALE, ARTIFACTS, PHASE, PERIODICITY, MSHAP, and SHAPE), each designed to isolate specific properties relevant to clustering evaluation; and (2) to enable full reproducibility of the experimental evaluation reported in the paper, both on TuwTSG.v1 and on the UCR archive.
\nThe datasets were generated using TuwTSG, a custom time series generator developed for this purpose, driven by JSON configuration files that specify the characteristics of each collection (e.g., number of clusters, signal shapes, perturbations, scale/phase variations). An HTML-based configuration wizard is also provided to assist users in creating their own custom configurations. The evaluation experiments were carried out using a dedicated evaluation pipeline (clustering algorithms, distance measures, and validation metrics) applied to both TuwTSG.v1 and the UCR archive.
\nPlease, if you use the TuwTSG.v1 benchmark or the TuwTSG tool in your research, cite our work as follows:
\n\n\n@inproceedings{IglesiasZseby2026TuwTSG,
\n
author = {Iglesias, F{\\'e}lix and Zseby, Tanja},
title = {On the Synthetic Evaluation of Time Series Clustering},
booktitle = {Proceedings of the 29th International Conference on Discovery Science (DS 2026)},
year = {2026},
address = {Mainz, Germany},
month = oct,
note = {To appear},
}
The repository is organized into five separate ZIP archives, which can be downloaded independently depending on the user's needs:
\nTuwTSG.v1.zip — the TuwTSG.v1 synthetic dataset collection.generator.zip — the TuwTSG generator code, configuration files, and the HTML configuration wizard.evaluation.zip — the code, modules, and scripts used to run the evaluation experiments.docker.zip — Docker configuration files (Dockerfile, docker-compose.yml, Makefile, requirements.txt) to build the reproducible environment.docker_image.zip — a pre-built Docker image, provided to facilitate reproducibility without needing to rebuild the environment from scratch.TuwTSG.v1.zip if just the dataset collection is needed) and extract them into the same local folder, so that the expected directory structure is preserved and all components work together consistently.The generator and evaluation code require Python 3.9.6 (Linux or macOS recommended), with dependencies listed in generator/requirements.txt and evaluation/requirements.txt. Alternatively, the provided Docker image/configuration removes the need for a manual Python environment setup. Detailed step-by-step instructions for generation, evaluation, and Docker usage are provided in the accompanying README.md.
The TuwTSG tool is also available in the Github repo: https://github.com/CN-TU/TuwTSG
\nThe UCR datasets referenced in the evaluation pipeline were obtained from the TSB-Clustering and UCRArchiveFixes repositories. While the TSB-Clustering repository's source code is MIT-licensed, this does not necessarily extend to the licensing of the underlying UCR datasets themselves; users should consult the original UCR Archive for the authoritative licensing and attribution terms.
\nFull instructions for setup, dataset generation, environment configuration, and running the evaluation experiments (including expected folder structure) are documented in the README.md included with this artifact.