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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"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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\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": "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": 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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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"Atmospheric science"}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "10.5194/egusphere-2026-2187", "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": "publication-preprint", "title": {"de": "Preprint", "en": "Preprint"}}}], "version": "1.0.0", "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 repository contains the reference implementation of 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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{"resource_type": {"id": "dataset", "title": {"de": "Datensatz", "en": "Dataset"}}, "creators": [{"person_or_org": {"type": "personal", "name": "Mortier, Lucas", "given_name": "Lucas", "family_name": "Mortier", "identifiers": [{"identifier": "0009-0003-5105-9686", "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": "Li, Bowen", "given_name": "Bowen", "family_name": "Li", "identifiers": [{"identifier": "0000-0002-8440-543X", "scheme": "orcid"}]}, "affiliations": [{"id": "04d836q62", "name": "TU 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{"identifier": "0000 0001 0790 3681", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Werkovits, Stefan", "given_name": "Stefan", "family_name": "Werkovits", "identifiers": [{"identifier": "0000-0003-0982-8018", "scheme": "orcid"}]}, "affiliations": [{"id": "01ee9ar58", "name": "University of Nottingham", "identifiers": [{"identifier": "01ee9ar58", "scheme": "ror"}, {"identifier": "grid.4563.4", "scheme": "grid"}, {"identifier": "0000 0004 1936 8868", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Mirwald, Johannes", "given_name": "Johannes", "family_name": "Mirwald", "identifiers": [{"identifier": "0000-0001-5025-7427", "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": "Hofko, Bernhard", "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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{"resource_type": {"id": "image", "title": {"de": "Bild", "en": "Image"}}, "creators": [{"person_or_org": {"type": "personal", "name": "Sheehan, Hester", "given_name": "Hester", "family_name": "Sheehan"}, "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": "Phylogenetic gene tree figures from Chapter 3 of the University of Copenhagen PhD thesis 'Understanding Specialized Metabolism in Faba Bean: From Biosynthetic Pathways to Crop Improvement'", "publisher": "University of Copenhagen", "publication_date": "2026-07-29", "subjects": [{"id": 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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.
This dataset was created as part of a study \"From Local Structure to Thermodynamics and Transport of Water with Machine Learning Force Fields\" evaluating how the exchange-correlation functional used in density functional theory influences the microscopic structure, thermodynamic properties, and transport behavior of liquid water described by machine-learning force fields.
\nThe dataset contains molecular dynamics simulation trajectories and derived analysis data for several machine-learning force fields trained on reference calculations using different density functional theory exchange-correlation (XC) functionals. Results from the machine-learning models are compared with simulations using the classical SPC/E water model.
\nThe investigated XC-functionals include:
\nPBE
\nPBE-D3
\nPBE-TS
\nRPBE
\nRPBE-D3
\nR2SCAN+rVV10
\nvdW-DF-cx
\nThe purpose of the dataset is to support the structural, thermodynamic, and transport-property analyses presented in the associated publication. In particular, the data were used to calculate radial and orientational pair-correlation functions, three-body structural descriptors, tetrahedral order parameters, excess-entropy contributions, self-diffusion coefficients, and viscosity.
\nFor each model, five independent molecular dynamics trajectories were generated using different initial velocity seeds. Atomic configurations were sampled every 10 fs, and each deposited trajectory contains 100,000 snapshots.
\nThe original machine-learning force-field simulations were performed using VASP. VASP stores trajectory data in XDATCAR files. To enable analysis with Python, including functionality provided by the MDAnalysis package, the original trajectories were converted to the LAMMPS trajectory format (.lammpstrj).
The full-resolution XDATCAR files contain configurations sampled every 1 fs and are approximately 60 GB per file. Because of their size, these files are not included in the public repository. Instead, the repository contains the converted .lammpstrj trajectories sampled every 10 fs. The original XDATCAR, OUTCAR, and other VASP output files are available from the authors upon request.
Stress-tensor data were recorded every 1 fs and are provided separately from the atomic trajectories.
\nThe repository is organized by simulation model and by data-processing stage. A representative directory structure is shown below:
\n.
\u251c\u2500\u2500 Data/ # NVT raw data for different functionals/force fields.
\u2502 \u251c\u2500\u2500 SPCE/
\u2502 \u2502 \u251c\u2500\u2500 trajectory/ # Trajectory files from 5 independent simulations.
\u2502 \u2502 \u2502 \u251c\u2500\u2500 trajectory_1.lammpstrj
\u2502 \u2502 \u2502 \u251c\u2500\u2500 trajectory_2.lammpstrj
\u2502 \u2502 \u2502 \u251c\u2500\u2500 ...
\u2502 \u2502 \u2514\u2500\u2500 stress/ # Stress tensor files from 5 independent simulations.
\u2502 \u2502 \u2502 \u251c\u2500\u2500 stress_tensor_SPCE_1.txt
\u2502 \u2502 \u2502 \u251c\u2500\u2500 stress_tensor_SPCE_2.txt
\u2502 \u2502 \u2502 \u251c\u2500\u2500 ...
\u2502 \u2502 \u2514\u2500\u2500 PCF/ # Generated PCFs for different nr of configurations/snapshots within independent simulation trajectories.
\u2502 \u2502 \u2502 \u251c\u2500\u2500 trajectory_1/
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_1_analyzed_02000.npy
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_1_counts_analyzed_02000.npz
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ...
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_1_analyzed_100000.npy
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_1_counts_analyzed_100000.npz
\u2502 \u2502 \u2502 \u251c\u2500\u2500 trajectory_2/
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_2_analyzed_02000.npy
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_2_counts_analyzed_02000.npz
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ...
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_2_analyzed_100000.npy
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 PCF_SPCE_trajectory_2_counts_analyzed_100000.npz
\u2502 \u2502 \u2502 \u251c\u2500\u2500 .../
\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ...
\u2502 \u251c\u2500\u2500 PBE/
\u2502 \u251c\u2500\u2500 PBE-D3/
\u2502 \u251c\u2500\u2500 PBE-TS/
\u2502 \u251c\u2500\u2500 RPBE/
\u2502 \u251c\u2500\u2500 RPBE-D3/
\u2502 \u251c\u2500\u2500 R2SCAN+rVV10/
\u2502 \u2514\u2500\u2500 vdW-DF-cx/
\u251c\u2500\u2500 Analysis/ # Analysis scripts to compute transport properties, three-body structure, pair correlation functions and entropy.
\u251c\u2500\u2500 Plotting/ # Final data output and generated figures.
\u251c\u2500\u2500 Training_data/ # Training data and trained force fields
\u2502 \u251c\u2500\u2500 PBE/
\u2502 \u251c\u2500\u2500 ...
\u251c\u2500\u2500 LICENSE-CODE
\u251c\u2500\u2500 LICENSE-DATA
\u2514\u2500\u2500 README.md
\u2514\u2500\u2500 REQUIREMENTS.txtThe Data/ directory contains the deposited atomic trajectories and stress-tensor data. The directory structure shown for SPCE/ is repeated for the other models. It also contains the cumulative pair correlation functions calculated from the trajectories. Files and folders containing the suffixes _1, _2, _... correspond to the independent molecular dynamics trajectories. Model names in filenames and directory names identify the corresponding force field. For example, stress_tensor_RPBE-D3_2.txt contains stress-tensor data from the second independent RPBE-D3 trajectory.
The Training_data/ directory contains the ab-initio simulation trajectories ML_AB used for the training of the force fields ML_FF.
The experimental reference data for the radial distribution functions of water provided in Plotting/Experiment is taken from (CC BY 3.0):
, The Radial Distribution Functions of Water as Derived from Radiation Total Scattering Experiments: Is There Anything We Can Say for Sure?, International Scholarly Research Notices, 2013, 279463, 67 pages, 2013. https://doi.org/10.1155/2013/279463
The repository contains the following principal file types and data formats:
\n.lammpstrj: Atomic trajectories in LAMMPS trajectory format.
.txt: Stress-tensor data and other plain-text numerical output.
.csv: Processed tabular data.
.npy: Multidimensional NumPy arrays.
.py: Python analysis scripts.
.ipynb: Jupyter Notebook analysis scripts.
ML_AB: Atomic configurations used to train the machine-learned force fields.
ML_FF: Trained machine-learned force fields.
The .lammpstrj files contain the simulation timestep, simulation-cell dimensions, atom identifiers, atom types, and atomic coordinates for every stored configuration. The stress-tensor files contain the time-dependent tensor components sampled every 1 fs.
The analysis scripts were written in Python 3.10.12 and use packages including:
\nNumPy
\nSciPy
\npandas
\nMatplotlib
\nMDAnalysis
\nExact software versions and dependencies are listed in requirements.txt.
The original molecular dynamics simulations using the generated ML-FFs were performed using VASP. Access to VASP is not required to inspect or analyze the deposited .lammpstrj trajectories. Simulations for SPCE were performed using LAMMPS. However, VASP is required to run new molecular dynamics simulations using the trained ML-FFs. The ML_AB files can be inspected with a standard text editor. Details can be found in the VASP Wiki.
The Analysis/ directory contains scripts for:
calculating the six-dimensional pair correlation functions (PCFs);
\nevaluating translational and orientational excess entropy from the generated PCFs;
\ncalculating three-body angle and tetrahedral-order distributions;
\ncalculation hydrogen-bond characteristics; and
\ncalculating self-diffusion coefficients and viscosity.
\nFurther methodological details are provided in the associated scientific publication and its Supporting Information.
\nThe Plotting/ directory contains the script that generates the figures shown in the publication.
Machine-readable citation information is provided in CITATION.cff.
The deposited trajectories are subsampled versions of the original VASP trajectories. Atomic positions are included every 10 fs, whereas the original XDATCAR files contain configurations every 1 fs. The deposited trajectories are suitable for the structural, entropy and transport analyses described in the associated publication. Analyses requiring atomic coordinates at a temporal resolution finer than 10 fs may require the original VASP output files, which are available upon reasonable request. Stress-tensor data are provided at a temporal resolution of 1 fs because this higher sampling frequency is required for the Green–Kubo viscosity analysis.
Each model was simulated using five statistically independent trajectories. The trajectory index should therefore be retained when estimating means, standard deviations, or uncertainties. The independent trajectories should not be treated as a single continuous trajectory. The processed data were derived from the deposited trajectories using the scripts included in the repository.
\nUsers should consult the associated publication and Supporting Information for complete definitions of the structural descriptors, entropy expressions, convergence procedures, and transport-property calculations.
\nThe simulation data, processed data, metadata, figures, and documentation are licensed under the Creative Commons Attribution 4.0 International License. See LICENSE-DATA.
The analysis scripts, conversion scripts, and other source code are licensed under the MIT License. See LICENSE-CODE.
When reusing the dataset or source code in scientific work, please cite the archived dataset and the associated publication using the citation information provided in CITATION.cff and the repository README.
KOLMOGOROV-SCALE FIBER TRAJECTORIES IN TURBULENT CHANNEL FLOW
=============================================================
Per-fiber Lagrangian trajectories (position, velocity, acceleration,
orientation, angular velocity) of rigid Kolmogorov-scale fibers in a turbulent
channel flow, at three friction Reynolds numbers. Each fiber is stored both raw
(measured) and filtered (translation and rotation filtered separately). The
files contain trajectories only; example loaders that rebuild the y+ profiles
are included.
Dataset version 1.0.
\nCONTENTS
--------
Re_tau=180 : near_wall, intermediate, center
Re_tau=360 : near_wall, intermediate, center
Re_tau=720 : near_wall, center (no intermediate region)
Per region: <Re_tau_###>/<region>/fiber_trajectories_Re###_<region>.h5
Per case: fiber_trajectories_Re###_datadictionary.json (field/units description)
LoadHDF5_Re_tau_###.py , LoadHDF5_Re_tau_###.m (example loaders)
Each region is an independent volumetric acquisition, with its own friction
velocity and viscous scales; always use the wall scales stored in the file you
are reading.
FILE LAYOUT (HDF5)
------------------
Ragged/columnar: within each group all fibers' samples are concatenated. Every
group also carries two bookkeeping datasets to slice a single fiber:
index int64 (nFiber x 3) = [fiber_id, start0, count], start0 is 0-based
fiber_id int64, the fiber id of each sample
There is also a root dataset /fiber_id listing all fiber ids. Missing values are
NaN. Time is redundant with the frame index: time = (frame-1)*dt.
/raw frame, time, pos[mm], e1, e2, e3,
curvature_kk0, length_mm (measured, unfiltered)
/filtered_translation frame, time, pos[mm], vel[mm/s], acc[mm/s^2],
tau_f_sec[s] (Mordant filter)
/filtered_rotation frame, time, e1_filt, e2_filt, e3_filt,
omega_body[rad/s], wall_distance[mm],
tau_f_sec[s] (SO(3) SG filter)
/wall_scales u_tau[m/s], viscous_length[m], viscous_time[s],
Re_tau, Temperature[degC]
The wall distance is column 2 of pos (see CONVENTIONS), so it is not stored
separately in /raw or /filtered_translation. /filtered_rotation has no position,
so it keeps wall_distance: the measured wall distance at that frame.
COMPLETENESS
------------
/raw holds every sample of every selected fiber. The two filtered groups
are subsets: filtering consumes samples at both ends of a trajectory, so a fiber
whose valid runs were shorter than the filter support has count = 0 in that
group's index and appears in /raw only. Root attributes n_fibers,
n_fibers_with_translation, n_fibers_with_rotation, n_fibers_raw_only and the
n_samples_* counts summarise this per region.
CONVENTIONS
-----------
Axes: x = streamwise, y = wall-normal, z = spanwise. The frame is
right-handed. +x points along the mean flow, so the streamwise position
increases with time. y is the distance from the nearest wall in mm,
always >= 0 and increasing away from the wall; it is therefore column 2
of pos, in both /raw (measured value) and /filtered_translation
(filtered value). The x and z origins are arbitrary.
pos/vel/acc columns = [streamwise, wall_normal, spanwise].
omega_body = [spin(e1), tumble(e2), tumble(e3)], in the fiber body frame, and
therefore independent of the lab-frame axis convention above.
Orientation: e1, e2, e3 are the body axes expressed in the lab frame and are
the COLUMNS of the rotation matrix, det(R) = +1. In /raw they are the
measured axes; in /filtered_rotation they are the filtered ones, stored
as e1_filt, e2_filt, e3_filt. Rebuild the matrix as
R(:,:,k) = [e1_filt(k,:)' e2_filt(k,:)' e3_filt(k,:)'] (MATLAB)
R = np.column_stack([e1[k], e2[k], e3[k]]) (Python)
and the lab-frame angular velocity, if needed, as omega_lab = R*omega_body.
ARRAY ORDER
-----------
The files are written from MATLAB. MATLAB reverses dimension order on disk, so
h5py reports the transposed shape: an (N,3) array such as pos, vel, acc,
omega_body or a body axis is read back with shape (3,N), and the (nFiber,3)
index as (3,nFiber). The included Python loader handles this.
HOW TO RUN
----------
Python (needs numpy, h5py, matplotlib). The loaders were written and tested with
Python 3.12.0 and the package versions pinned in requirements.txt
(h5py 3.16.0, numpy 2.5.1, matplotlib 3.11.0). To reproduce that environment:
python -m pip install -r requirements.txt
set DATA_DIR at the top of the script, then run interactively so the loaded
variables stay available:
python -i LoadHDF5_Re_tau_180.py (or LoadHDF5_Re_tau_360.py / LoadHDF5_Re_tau_720.py)
(plain \"python LoadHDF5_Re_tau_180.py\" runs to the end but then exits and the
variables are not saved in memory -- use -i.)
MATLAB:
set DATA_DIR at the top of LoadHDF5_Re_tau_###.m and press Run. The MATLAB
loaders and the processing scripts were written and tested with MATLAB R2024b.
Both leave in memory: DATA (case, Re_tau, regions), PROFILES (y+ statistics
computed from the trajectories), and one variable per region (near_wall,
intermediate, center) holding per-fiber arrays, one column per fiber, NaN-padded
to the longest fiber:
.raw_data pos, e1, e2, e3 (nSamp x nFib x 3); curvature,
length_mm, frame, time, wall_mm (nSamp x nFib)
.translation_filtered pos, vel, acc (nSamp x nFib x 3); tau_f, frame, time,
wall_mm (nSamp x nFib)
.rotation_filtered e1, e2, e3, omega_body (nSamp x nFib x 3); tau_f,
frame, time, wall_mm (nSamp x nFib)
wall_mm is provided by the loaders for convenience and is column 2 of pos in the
two groups that have a position. Each group also carries fiber_ids (nFib), the
fiber id of each column, so a fiber can be followed across groups.
The y+ profiles in PROFILES are computed by the loaders from the trajectories,
as an example of use; they are not stored in the files. Edit compute_profiles()
to change the binning or add quantities.
LICENSE
-------
The dataset is released under the Creative Commons Attribution 4.0 International
license (CC-BY-4.0). https://creativecommons.org/licenses/by/4.0/
The accompanying codes (the MATLAB processing scripts and the MATLAB and Python
loading scripts) are released under the MIT License, provided in LICENSE-CODE.txt.
https://opensource.org/licenses/MIT
HOW TO CITE
-----------
Please cite both the paper accompanying this dataset and the related paper below.
1) Accompanying paper (dataset descriptor):
@article{ColibanFiberDataset,
title = {Experimental dataset of Kolmogorov-scale fiber kinematics in turbulent channel flow},
author = {Coliban, Eliza and Zaza, Domenico and Soldati, Alfredo},
journal = {Scientific Data},
year = {2026},
note = {submitted}
}
2) Related paper:
@article{kcmw-5dph,
title = {Angular Velocity of Kolmogorov-Scale Fibers as Proxy for Turbulent Dissipation},
author = {Zaza, Domenico and Giurgiu, Vlad and Iovieno, Michele and Soldati, Alfredo},
journal = {Phys. Rev. Lett.},
volume = {136},
issue = {5},
pages = {054001},
year = {2026},
month = {Feb},
publisher = {American Physical Society},
doi = {10.1103/kcmw-5dph},
url = {https://link.aps.org/doi/10.1103/kcmw-5dph}
}
This dataset was created from original work conducted in the framework of a PhD project at University of Chemistry and Technology Prague and a CEEPUS-Exchange to TU Wien, and compiled in a publication. It provides the raw data of results presented and discussed therein.
\n\n
In the following, description of the uploaded data is available. Detailed experimental procedures to arrive at the materials is included in the corresponding publication but abbreviations of formulations and materials used in the files are also described below.
\nFolder \"NMR spectra\"
\nContains 1H, 13C and HSQC NMR spectra of the compounds UnBP and UnIC and 1H NMR spectra of compounds PE-BP1, PE-BP2, PE-BP3, PE-BP4, PE-IC1, PE-IC2, PE-IC3 and PE-IC4.
\nNMR files require the software MestreNova or TopSpin to open, which are community standard softwares for this data type.
\nExcel File \"Figure Data\"
\nThe file tabs are named according to the corresponding figure displaying the data in the manuscript and the data type displayed:
\nTab 1: Figure 2 UV-Vis spectrum
\nTab 2: Figure 4a Photo-dynamic scanning calorimetry (Photo-DSC)
\nTab 3: Figure 4b PhotoDSC
\nTab 4: Figure 5 Dynamic mechanical thermal analysis (DMTA) IBA series
\nTab 5: Figure 5 DMTA MIX series
\nTab 6: Figure 5 DMTA TDD series
\nTab 7: Figure 6 Tensile testing curves IBA series
\nTab 8: Figure 6 Tensile testing curves MIX series
\nTab 9: Figure 6 Tensile testing curves TDD series
\nFolder \"GPC\"
\nSize exclusion chromatography (SEC; gel permeation chromatography, GPC) data for the (functionalized) hyperbranched polyethylene macromonomers. Data is given in Malvern™ native formats including calibration files. To open the files, the Malvern™ software OmniSEC is required. Additionally, PDF reports are included for each of the samples.
\nFolder “GCMS”
\nGas chromatography with mass spectrometric detector data for the leeching tests of prepared materials. Data is given in “.raw” format and can be opened by the softwares MestreNova or OpenChrom, which are community standard softwares for this data type.
\nAbbreviations
\nPE: Polyethylene
IC: Irgacure 2959, a common Type I photoinitiator
BP: benzophenone, a common Type II photoinitiator
UnIC: 10-undecenoate -based comonomer used to introduce Irgacure 2959 moieties to the hyperbranched polyethylene macrophotoinitiators
UnBP: 10-undecenoate -based comonomer used to introduce benzophenone moieties to the hyperbranched polyethylene macrophotoinitiators
IBA: isobornyl acrylate (CAS: 5888-33-5)
TDD: tricyclodecanedimethanol diacrylate (CAS: 42594-17-2)
MIX: equimolar mixture of IBA and TDD
Formulations and Materials utilize the same abbreviations, according to their composition described next to the abbreviations.
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"description": "
These are the data for the paper:
\nV. Dziom, A. Shuvaev, J. Gospodari\u010d, E. G. Novik, A. A. Dobretsova, N. N. Mikhailov, Z. D. Kvon, Z. Alpichshev, and A. Pimenov
“Universal transparency and asymmetric spin splitting near the Dirac point in HgTe quantum wells”
Phys. Rev. B 106, 045302 (2022). https://doi.org/10.1103/PhysRevB.106.045302
arXiv:2504.15877
\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 \"Transmission_Field.oggu\" contains the rransmission spectra of circularly polarized radiation at 1.85 K and at 940 GHz. The dark sample demonstrates two resonances at positive magnetic field, corresponding to spin-resolved hole subbands.The illumination shifts the Fermi level to the conduction band with much smaller spin splitting, and electrons produce the single resonance in the negative field.
\nThe file \"Absorption_Frequency.oggu\" shows the THz absorption in zero magnetic field as extracted from field dependence of the transmission at fixed frequencies.
\nThe file \"Conductivity_Mass.oggu\" shows:
a) Optical conductivity for the hole resonences, demonstrating the universal value πα/2 upon passing through zero magnetic field.
b) Cyclotron masses determined from positions of the two hole and one electron resonances, respectively.
Spin-orbit coupling in thin HgTe quantum wells results in a relativistic-like electron band structure, making it a versatile solid state platform to observe and control nontrivial electrodynamic phenomena. Here we report an observation of universal terahertz (THz) transparency determined by fine-structure constant α ≈ 1/137 in 6.5-
nm-thick HgTe layer, close to the critical thickness separating phases with topologically different electronic band structure. Using THz spectroscopy in a magnetic field we obtain direct evidence of asymmetric spin splitting of the Dirac cone. This particle-hole asymmetry facilitates optical control of edge spin currents in the quantum wells.
Each file is named after the figure number indicated in the manuscript. The data sets are tabulated and, within each file, columns are labelled to describe the corresponding physical variables (please see the manuscript for exact definitions of the variables.)
\nFig.3 : Molecular potential as a function interatomic distance R together with the wavefunctions of the selected vibrational states.
\nFig.4 : (a) Photoassociation spectrum (b) expectation value of rotational quantum number and standard variation, (c-g) wavefunctions of selected molecular eigenstates (in the absence of magnetic field)
\nFig.5 : (a,b) Photoassociation spectra (c,d) expectation values of rotational quantum number and standard variations (in the limit of strong magnetic field, M=1,2)
\nFig.6 : Spherical harmonics for l=2 with variaous m
\nFig.7 : Wavefunctions of selected molecular eigenstates from Fig.5
\nFig.8 : (a,b) Photoassociation spectra (c) expectation values of rotational quantum number and standard variations (in the limit of strong magnetic field, M=0)
\nFig.9 : Wavefunctions of selected molecular eigenstates from Fig.8
\nFig.10 : Molecular eigenenergies as a function of magnetic field
\nFig.11 : Photoassociation spectra at B=0.2G
\nFig.12 : Photoassociation spectra at B=1G
\nFig.13 : Photoassociation spectra at B=2G
\nFig.14 : Photoassociation spectra at B=6G
\nFig.15 : Wavefunctions of selected molecular eigenstates from Fig.14
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"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 repository contains the R scripts used for the analyses presented in the master's thesis. A README file describing the project structure and software requirements is included.
\nEvent-based soil erosion in an erosion-prone agricultural catchment: Deviations between ABAG modelling and reference measurements.
\n(TU Wien, 2026)
\nThis repository contains the R scripts developed as part of the master's thesis on event-based soil erosion in an erosion-prone agricultural catchment. The scripts were used to preprocess geospatial and monitoring data, calculate event-based soil loss using the ABAG model, derive sediment delivery ratios (SDR), and compare modelled soil loss with event-based reference sediment loads. The analyses were carried out for 49 rainfall events in the Oberndorferbach catchment (Upper Austria).
\nData availability:
This repository contains the analysis code only. The input data consist of publicly available geospatial datasets (e.g. DEM, Sentinel-2 imagery, soil maps, INVEKOS field boundaries and precipitation data) and project-internal monitoring data. The event-based reference sediment load measurements used for model validation cannot be shared publicly because they originate from an internal research project. Publicly available input data can be obtained from their respective providers.
\n
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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 is a program that is useful for studying cardinal invariants in the set theory of real numbers.
\nThis program tells you whether consistency of x < y for cardinal invariants x and y can be deduced or not using transitivity from known theorems of inequality and known consistency results.
\nDirectly open index.html in your browser. (You don't need to create a web server.)
\nThis repository bundles the following third-party file:
\nA javascript program that displays diagrams of Cardinal Characteristics.
It is meant as supplemantary material to the book CCC Forcing Constructions for the Real Line by Diego A. Mejía, Martin Goldstern, Jakob Kellner, and Saharon Shelah.
The according characteristcs (nodes), and (in)equalities for various models (arrows betweem modes) for the models of the book are given as defaulta data, but the user can enter other partial orders to be displayed. The program creates an interactive diagram with movable nodes where equality clusters can be collapsed or highlighted by background blobs.
Downlaod CCViewer_v44.html (or CCViewer_v44_full.html, same program but with dependencies included), store it locally (or on any webserver) and open it in a webbrowser.
A live installation (possibly in an updated version) can be found at https://dmg.tuwien.ac.at/kellner/CCViewer.html
This notebook is complementary material to the book CCC Forcing Constructions for the Real Line by D. A. Mejía, M. Goldstern, J. Kellner, and S. Shelah. Its goal is to investigate open questions concerning the consistency with ZFC of inequalities of the form y<x, where x and y are classical cardinal characteristics of the continuum.\n\nThe notebook encodes the known provable inequalities between characteristics, as a directed graph, and stores information from classical forcing models (such as Cohen, Random, Hechler, Laver, Miller, and others) as assignments of values to the vertices of this graph. A collection of algorithms processes this data to identify potential open questions and unexplored consistency patterns.\n\nThe notebook was developed and tested using Wolfram Mathematica 15.0.0. An online version can be accessed from the Wolfram Cloud through the link below (this online version might be updated at some stage in the future). No additional packages are required.\n\nhttps://www.wolframcloud.com/obj/damejiag/Public/2charmodels.nb\n\nIntended use: the notebook serves as a research and exploration tool accompanying the book, allowing readers and researchers to experiment with the encoded data and generate potential open problems concerning cardinal characteristics of the continuum.\n
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[{"person_or_org": {"type": "personal", "name": "Davoudipahnekolayi, Mahboubeh", "given_name": "Mahboubeh", "family_name": "Davoudipahnekolayi"}, "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": "Modulation of de novo organogenesis in Euphorbia pulcherrima Willd: morphogenic and molecular insights", "publisher": "TU Wien", "publication_date": "2026-07-20", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?4.4", "subject": "Agricultural biotechnology", "scheme": "FOS"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?4", 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This dataset was generated as part of research in plant biotechnology, plant tissue culture, and molecular biology, investigating factors that influence in vitro direct adventitious shoot organogenesis in poinsettia (Euphorbia pulcherrima Willd.). The study evaluates the effects of the growth adjuvants silver nitrate (AgNO\u2083) and myo-inositol on regeneration efficiency and their relationship with the expression of genes involved in shoot regeneration and ethylene biosynthesis.
\nThe purpose of this dataset is to provide phenotypic and molecular data describing the effects of AgNO\u2083 and myo-inositol on in vitro shoot regeneration in poinsettia. The dataset supports the investigation of regeneration-promoting treatments and the identification of transcriptional changes associated with enhanced morphogenic competence and cellular reprogramming. It may be reused for comparative studies, optimization of tissue culture protocols, and research on regeneration mechanisms in ornamental plants.
\nThe dataset was generated from controlled in vitro tissue culture experiments using explants of Euphorbia pulcherrima cv. Christmas Eve. Explants were cultured on regeneration media supplemented with different concentrations of silver nitrate (AgNO\u2083) and myo-inositol. Regeneration responses, including morphogenic characteristics and shoot formation, were recorded under standardized culture conditions. Expression levels of shoot regeneration-related transcription factors and ethylene biosynthesis genes were determined using molecular analyses. Experimental observations, quantitative measurements, gene expression data, and associated metadata were compiled and organized for statistical analysis.
\nThe dataset is organized into folders according to data type and experimental workflow. It includes raw and processed regeneration data, gene expression data, statistical outputs, and metadata describing experimental conditions and treatments. Files are named systematically using experiment identifiers, treatment names, sample identifiers, and dates to facilitate traceability and reproducibility.
\nThe dataset can be opened using standard spreadsheet software such as Microsoft Excel. Statistical analyses can be reproduced using R, Python, SPSS, or similar statistical software. If quantitative PCR data are included, the associated qPCR analysis software may be used to reproduce normalization and expression calculations.
\nA README file accompanies the dataset and provides descriptions of the dataset structure, variable definitions, abbreviations, experimental design, and measurement units. Detailed experimental procedures are available in the associated publication.
\nThe dataset contains phenotypic and molecular measurements obtained under controlled in vitro culture conditions and is intended to support research on shoot regeneration and morphogenic competence in poinsettia. Users should consider explant type, culture medium composition, concentrations of plant growth regulators and growth adjuvants, sampling time points, and experimental conditions when interpreting the data. Gene expression measurements should be interpreted together with the accompanying regeneration data and experimental metadata. The dataset may be reused for studies on plant regeneration, tissue culture optimization, hormonal regulation, gene expression, and the development of improved propagation protocols for Euphorbia pulcherrima and related species.
\nFunding:
\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 101065228 (HORIZON-MSCA-2021-PF-01-COLORnamental). The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme. The authors would also like to thank Andreea Enculescu and Guido v. Tubeuf for generously supplying plant materials.
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\nThe dataset includes measured and fitted N\u2082 and CO\u2082 adsorption isotherms; pore size distribution (PSD) results for contaminated and uncontaminated biochars; X-ray diffraction (XRD) patterns of fresh, spent, and high-temperature-treated biochars; inorganic composition of contaminated and uncontaminated birch feedstocks; elemental analysis and ash content of pyrolytic and thermally activated biochars; a summary of the conducted toluene conversion experiments; concentrations of selected inorganic elements in high-temperature-treated and untreated chars; and surface area and pore volume characteristics of fresh, spent, and high-temperature-treated biochars.
\nThe biochars were derived from birch harvested from heavy-metal-contaminated and uncontaminated areas. Thermal activation was performed using a 50/50 vol% CO\u2082/H\u2082O mixture, and toluene was employed as a model tar compound to evaluate the catalytic performance and deactivation behaviour of the biochars. The supplementary data support the evaluation of biochar porosity, heavy-metal retention, structural evolution, coke-induced deactivation, and catalytic tar decomposition.
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