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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"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", "subject": "Agricultural sciences", "scheme": "FOS"}], "contributors": [{"person_or_org": {"type": "personal", "name": "Halbwirth, Heidrun", "given_name": "Heidrun", "family_name": "Halbwirth", "identifiers": [{"identifier": "0000-0001-9059-5850", "scheme": "orcid"}]}, "role": {"id": "supervisor", "title": {"en": "Supervisor"}}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Gruber Schmidt, Viktoria", "given_name": "Viktoria", "family_name": "Gruber Schmidt"}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}}, {"person_or_org": {"type": "personal", "name": "Boehm, Robert", "given_name": "Robert", "family_name": "Boehm"}, "role": {"id": "supervisor", "title": {"en": "Supervisor"}}}, {"person_or_org": {"type": "personal", "name": "Sprink, Thorben", "given_name": "Thorben", "family_name": "Sprink"}, "role": {"id": "projectmember", "title": {"en": "Project member"}}}], "dates": [{"date": "2026", "type": {"id": "available", "title": {"en": "Available"}}}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "10.1016/j.scienta.2026.114776", "scheme": "doi", "relation_type": {"id": "isdocumentedby", "title": {"en": "Is documented by"}}, "resource_type": {"id": "publication", "title": {"de": "Publikation", "en": "Publication"}}}], "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": 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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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{"identifier": "grid.5329.d", "scheme": "grid"}, {"identifier": "0000 0004 1937 0669", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Szl\u0119k, Andrzej", "given_name": "Andrzej", "family_name": "Szl\u0119k", "identifiers": [{"identifier": "0000-0003-1026-5275", "scheme": "orcid"}]}, "affiliations": [{"id": "02dyjk442", "name": "Silesian University of Technology", "identifiers": [{"identifier": "02dyjk442", "scheme": "ror"}, {"identifier": "grid.6979.1", "scheme": "grid"}, {"identifier": "0000 0001 2335 3149", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Winter, Franz", "given_name": "Franz", "family_name": "Winter", "identifiers": [{"identifier": "0000-0001-9854-3836", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Korus, Agnieszka", "given_name": "Agnieszka", "family_name": "Korus", "identifiers": [{"identifier": "0000-0003-4493-6452", "scheme": "orcid"}]}, "affiliations": [{"id": "02dyjk442", "name": "Silesian University of Technology", "identifiers": [{"identifier": "02dyjk442", "scheme": "ror"}, {"identifier": "grid.6979.1", "scheme": "grid"}, {"identifier": "0000 0001 2335 3149", "scheme": "isni"}]}]}], "title": "Research Data and Supplementary Datasets for \"Kinetics of Model Tar Decomposition over Activated Biochar from a Heavy Metal-Contaminated Area\"", "publisher": "TU Wien", "publication_date": "2026-07-12", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?2.4", "subject": "Chemical engineering", "scheme": "FOS"}, {"subject": "biochar"}, {"subject": "catalysis"}], "dates": [{"date": "2026-07-12", "type": {"id": "available", "title": {"en": "Available"}}}], "related_identifiers": [{"identifier": "10.1016/j.renene.2025.123661", "scheme": "doi", "relation_type": {"id": "issupplementedby", "title": {"en": "Is supplemented by"}}, "resource_type": {"id": "publication", "title": {"de": "Publikation", "en": "Publication"}}}], "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/legalcode", "scheme": "spdx"}}], "description": "This dataset contains supplementary experimental data supporting the research article “Kinetics of model tar decomposition over activated biochar from a heavy metal-contaminated area,” published in Renewable Energy, Volume 253 (2025), Article 123661.
\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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{"type": "personal", "name": "Korus, Agnieszka", "given_name": "Agnieszka", "family_name": "Korus", "identifiers": [{"identifier": "0000-0003-4493-6452", "scheme": "orcid"}]}, "affiliations": [{"id": "02dyjk442", "name": "Silesian University of Technology", "identifiers": [{"identifier": "02dyjk442", "scheme": "ror"}, {"identifier": "grid.6979.1", "scheme": "grid"}, {"identifier": "0000 0001 2335 3149", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Winter, Franz", "given_name": "Franz", "family_name": "Winter", "identifiers": [{"identifier": "0000-0001-9854-3836", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}], "title": "Supplementary Research Data for \"Activated Biochars from Heavy Metal-Contaminated Biomass for CO2 Capture: Adsorption Performance and Dominant Mechanisms\"", "publisher": "TU Wien", "publication_date": "2026-07-12", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?2.4", "subject": "Chemical engineering", "scheme": "FOS"}, {"subject": "biochar"}, {"subject": "CO\u2082 adsorption"}, {"subject": "XPS"}], "dates": [{"date": "2026-07-12", "type": {"id": "accepted", "title": {"en": "Accepted"}}}], "related_identifiers": [{"identifier": "10.1016/j.jcou.2025.103217", "scheme": "doi", "relation_type": {"id": "issupplementedby", "title": {"en": "Is supplemented by"}}, "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/legalcode", "scheme": "spdx"}}], "description": "This dataset contains supplementary experimental and characterization data supporting the research article “Activated biochars from heavy metal-contaminated biomass for CO2 capture: Adsorption performance and dominant mechanisms,” published in the Journal of CO2 Utilization, Volume 101 (2025), Article 103217.
\nThe dataset includes Raman spectra and X-ray photoelectron spectroscopy (XPS) survey spectra of selected activated pine- and birch-derived biochars; pore size distributions of biochars activated under different conditions; measured N2 and CO2 adsorption isotherms and corresponding model fittings; inorganic composition of contaminated and uncontaminated birch and pine feedstocks; a summary of the biochar activation experiments; CO2 adsorption kinetic parameters obtained using pseudo-first-order, pseudo-second-order, and Avrami models; elemental composition of activated biochars; concentrations of selected inorganic elements in pyrolyzed and activated samples; biochar yields after pyrolysis and activation; XPS-based surface elemental composition; and total, ultramicropore, and supermicropore surface areas and pore volumes determined using the 2D-NLDFT method.
\nThe biochars were produced from contaminated and uncontaminated birch and pine biomass and activated under different conditions using CO2 or a 50/50 vol% CO2/H2O mixture at different temperatures and reaction times. Their CO2 adsorption performance was evaluated using thermogravimetric analysis, while complementary characterization techniques included ICP-OES, elemental analysis, SEM, FTIR, Raman spectroscopy, XPS, and gas adsorption measurements. The dataset supports the assessment of the effects of biomass type, heavy-metal contamination, activation conditions, pore structure, surface chemistry, and adsorption kinetics on the CO2 capture performance of activated biochars.
\n", "additional_descriptions": [{"description": "
The corresponding research article is available at DOI: 10.1016/j.jcou.2025.103217.
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measurement of the aerosol formation potential of volatile chemical products", "publisher": "TU Wien", "publication_date": "2026-06-08", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?2.4", "subject": "Chemical engineering", "scheme": "FOS"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?1.5", "subject": "Earth and related environmental sciences", "scheme": "FOS"}, {"subject": "Atmospheric chemistry"}, {"id": "http://www.oecd.org/science/inno/38235147.pdf?1.3", "subject": "Physical sciences", "scheme": "FOS"}], "contributors": [{"person_or_org": {"type": "personal", "name": "Stolzenburg, Dominik Marco", "given_name": "Dominik Marco", "family_name": "Stolzenburg", "identifiers": [{"identifier": "0000-0003-1014-1360", "scheme": "orcid"}]}, "role": {"id": "supervisor", "title": {"en": "Supervisor"}}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Tischberger, Markus", "given_name": "Markus", "family_name": "Tischberger", "identifiers": [{"identifier": "0009-0003-6423-608X", "scheme": "orcid"}]}, "role": {"id": "supervisor", "title": {"en": "Supervisor"}}, "affiliations": [{"name": "TU Wien"}]}], "languages": [{"id": "eng", "title": {"en": "English"}}], "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/legalcode", "scheme": "spdx"}}], "description": "This dataset was created as part of the Master's thesis:
\nCharacterization and calibration of an oxidation flow reactor and MION-Orbitrap mass spectrometer for the measurement of the aerosol formation potential of volatile chemical products conducted at TU Wien.
\nThe objective of the work was to establish and characterize an experimental platform for investigating atmospheric oxidation processes and secondary aerosol formation from volatile chemical products (VCPs). The experiments included transmission measurements of the oxidation flow reactor, OH exposure characterization, calibration of permeation sources, sulfuric acid calibration experiments, and measurements of bitumen emissions.
\nThe dataset contains the processed and extracted data used for the analysis presented in the thesis, together with the Python scripts used for data processing and figure generation.
\nThe dataset is organized into six folders corresponding to the main experimental sections of the thesis:
\n1.Transmission
2.OH_exposure
3.Permeation_oven
4.Evaporator_toluene
5.Calibration_box
6.Bitumen
In addition, the file
\nAll_measurements.xlsb
contains an overview of all measurements performed during this work, including experimental notes, measurement conditions, and the retention times used for Orbitrap peak extraction during data evaluation.
\nEach experimental folder may contain:
\nextracted CSV data files,
\nintermediate evaluation files,
\nPython scripts used for data processing and visualization,
\ngenerated figures and result tables.
\nThe extracted CSV files were generated from Orbitrap measurements using Thermo Freestyle. The retention times used for peak extraction are documented in the file All_measurements.xlsb.
\nThe data files can be opened using standard software:
\nMicrosoft Excel or compatible spreadsheet software (.xlsx, .xlsb, .csv)
\nText editors for script inspection (.py)
\nPython (recommended version 3.x) for executing the data evaluation scripts
\nThe Python environment used for the data analysis is documented in:
\naerosol_env_2026_06.yaml
\nThis file contains the Python environment and package versions used during the thesis work.
\nSome calculations included in this repository rely on external scientific software packages:
\nMARFORCE flowtube model:
https://github.com/momo-catcat/MARFORCE-flowtube
These packages are not distributed as part of this repository and can be obtained from their respective public repositories if reproduction of the calculations is desired.
\nA detailed description of the data processing workflow, including the purpose of individual scripts and the relationships between raw data, intermediate files, and final results, is provided in the thesis appendix:
\nData Repository and Processing Workflow
\nHow to Reproduce the Analysis
\nTo reproduce the figures presented in the thesis:
\nOpen the corresponding experimental folder.
\nExecute the data processing scripts in the order described in the thesis appendix.
\nThe generated output files and figures will be stored in the designated results folders.
\nThe folder structure reflects the workflow used during the thesis and should be preserved.
\nThe repository is intended to document the complete data processing workflow used during the thesis and to facilitate the reproducibility of the reported results. All scripts are provided in the form used during the project and therefore reflect the original research workflow.
\nThe full thesis provides additional scientific background, methodological details, and interpretation of the results. The thesis is available here: https://doi.org/10.34726/hss.2026.135132
\n\n
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{"resource_type": {"id": "dataset", "title": {"de": "Datensatz", "en": "Dataset"}}, "creators": [{"person_or_org": {"type": "personal", "name": "Abbaspour, Nastaran", "given_name": "Nastaran", "family_name": "Abbaspour", "identifiers": [{"identifier": "0000-0003-4332-4630", "scheme": "orcid"}]}, "role": {"id": "contactperson", "title": {"en": "Contact person"}}, "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": "Vali, Naeimeh", "given_name": "Naeimeh", "family_name": "Vali", "identifiers": [{"identifier": "0000-0001-9443-5547", "scheme": "orcid"}]}, "affiliations": [{"id": "01fdxwh83", "name": "University of Bor\u00e5s", "identifiers": [{"identifier": "01fdxwh83", "scheme": "ror"}, {"identifier": "grid.412442.5", "scheme": "grid"}, {"identifier": "0000 0000 9477 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Technology", "identifiers": [{"identifier": "02dyjk442", "scheme": "ror"}, {"identifier": "grid.6979.1", "scheme": "grid"}, {"identifier": "0000 0001 2335 3149", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Korus, Agnieszka", "given_name": "Agnieszka", "family_name": "Korus", "identifiers": [{"identifier": "0000-0003-4493-6452", "scheme": "orcid"}]}, "affiliations": [{"id": "02dyjk442", "name": "Silesian University of Technology", "identifiers": [{"identifier": "02dyjk442", "scheme": "ror"}, {"identifier": "grid.6979.1", "scheme": "grid"}, {"identifier": "0000 0001 2335 3149", "scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Winter, Franz", "given_name": "Franz", "family_name": "Winter", "identifiers": [{"identifier": "0000-0001-9854-3836", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}], "title": "Supplementary Research Data for \"Gasification of Heavy Metal\u2013Contaminated Biochar: Experimental Investigation and Thermodynamic Analysis\"", "publisher": "TU Wien", "publication_date": "2026-07-12", "subjects": [{"subject": "Chemical Engineering"}, {"subject": "biochar"}, {"subject": "gasification"}], "dates": [{"date": "2026-07-12", "type": {"id": "accepted", "title": {"en": "Accepted"}}}], "related_identifiers": [{"identifier": "10.1016/j.ecmx.2026.101557", "scheme": "doi", "relation_type": {"id": "issupplementto", "title": {"en": "Is supplement to"}}, "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/legalcode", "scheme": "spdx"}}], "description": "
This dataset contains supplementary experimental and characterization data supporting the research article “Gasification of heavy metal–contaminated biochar: Experimental investigation and thermodynamic analysis,” published in Energy Conversion and Management: X, Volume 29 (2026), Article 101557.
\nThe dataset includes gas evolution profiles for biochar gasification; dry-gas composition data showing the effects of gasification temperature and reaction time on the average gaseous product composition; Raman spectra of pyrolytic and gasified birch biochars; inorganic composition of contaminated and uncontaminated birch biomass; a summary of the gasification experiments conducted under pure CO2 and 50/50 vol% H2O/CO2 atmospheres at different temperatures and reaction times; elemental analysis of pyrolyzed and gasified biochars; moisture, ash, and ultimate analysis of contaminated birch biomass; information on the elements and solution compounds included in the thermodynamic databases used for equilibrium modelling; concentrations of selected inorganic elements in pyrolyzed and gasified biochars; and BET surface area, total pore volume, and average pore diameter of the resulting biochars.
\nHeavy metal-contaminated birch biomass collected from a phytoremediation site was carbonized, and the resulting biochar was gasified under pure CO2 or a 50/50 vol% H2O/CO2 atmosphere at temperatures between 700 and 900 °C. The experimental investigation evaluated gas composition, biochar conversion, structural and textural properties, and the retention of heavy metals, particularly Zn and Pb. Thermodynamic equilibrium calculations were performed using FactSage 8.4 and FactFlow to assess elemental speciation and equilibrium tendencies for heavy-metal volatilization and retention under different thermal conversion conditions.
\nThe dataset supports the evaluation of the effects of gasifying atmosphere, temperature, and residence time on syngas composition, biochar conversion and properties, and heavy-metal retention during the gasification of contaminated biomass.
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"scheme": "isni"}]}]}, {"person_or_org": {"type": "personal", "name": "Freinberger, Lukas", "given_name": "Lukas", "family_name": "Freinberger", "identifiers": [{"identifier": "0009-0000-3251-1153", "scheme": "orcid"}]}, "role": {"id": "datacurator", "title": {"en": "Data curator"}}, "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": "Results from \"Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization\"", "publisher": "TU Wien", "publication_date": "2026-07-10", "subjects": [{"subject": "Applied quantum computing"}, {"subject": "quantum annealing"}, {"subject": "adaptive encoding strategy"}, {"subject": "mixed discrete\u2013continuous optimization"}, {"subject": "structural design optimization"}, {"subject": "computational mechanics"}], "related_identifiers": 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\nThis dataset originates from computational engineering optimization, specifically mixed discrete–continuous structural design, using quantum annealing (QA). Its purpose is to quantify how the binary budget per continuous variable (N) in fixed encodings affects solution accuracy on hardware-embedded QA and to evaluate the proposed adaptive encoding that updates representable ranges during optimization.
\nIn the empirical error analysis, we consider an illustrative fluid-structure-interaction problem to measure the relative H^1 error (median and interquartile ranges (IQRs)) in the structure's displacement as a function of N and report chain-break statistics, showing that QA accuracy improves only up to moderate bit depths before plateauing. In contrast, the adaptive encoding exhibits a monotonic decrease in error across coupling iterations, achieving a much lower final error with the same binary budget.
\nFor a structural design optimization benchmark, we integrate the adaptive encoding into a quadratic penalty method, updating ranges of continuous force variables at each iteration; we compare solution accuracy for the adaptive encoding with reference results from the literature (fixed encoding) and perform parameter studies over the relaxation factor for range updates, the initial range scale, and the number of QA reads, reporting relative H^1 error trajectories (median and IQRs) across penalty iterations. Additionally, we perform a scalability analysis using a quantum-inspired solver.
\nThe dataset is organized according to the structure of the publication:
\nEach subsection (3.1 and 3.2) has its own top-level folder. Within the folder for subsection 3.2, there are subfolders for each subsubsection (3.2.1, 3.2.2, 3.2.3, and 3.2.4). We detail the different datasets below (see Further details).
\nAll numerical data are stored as CSV files. Unless noted otherwise, column names follow these conventions: N (number of binary variables per continuous variable), iteration (iteration index of the coupling or penalty method), error (relative H^1 error), error_ba (best-approximation/encoding\u2011optimal error), and chain_break (chain break fraction). Nodal solution values with their range limits are reported per node, using a suffix indicating the node index, for example, node_0_min or node_1_max. When results for both encodings appear in the same file, columns use the suffixes fixed and adaptive (e.g., error_fixed, error_adaptive). For aggregated statistics, additional suffixes median, min, max, q25, and q75 indicate the median, minimum, maximum, and the 25th/75th percentile (IQR bounds), respectively.
Top-level folder names mirror the corresponding subsection titles. Subfolders are named after the parameters varied in each study (e.g., relaxation_factor for the relaxation factor). Filenames describe their contents (e.g., error_over_iterations_....csv for errors over iterations) and include the suffix aggregated when they contain aggregated statistics across multiple runs.
No specialized software is required to open the files; any CSV-capable tool is sufficient.
\nExample usage with Python (pandas)
\nimport pandas as pd\ndf = pd.read_csv(\"empirical_error_analysis/num_binary_variables/error_over_num_binary_variables_aggregated.csv\")\nprint(df.head())Data is licensed under Creative Commons Attribution 4.0 International.
\n3.1 Empirical Error Analysis
\nIn the following, we consider the relative H^1 error in the structure's displacement field.
\nError for different numbers of binary variables in the fixed encoding
\nempirical_error_analysis/error_over_num_binary_variables_aggregated.csvError comparison of the fixed and the adaptive encoding during coupling iterations
\nempirical_error_analysis/error_over_iterations_aggregated.csv3.2 Structural Design Optimization with Adaptive Encoding Strategy
\nHistory of adaptive range limits
\nstructural_design_optimization/range_limits_over_iterations.csvIn the following, we consider the relative H^1 error in the structure's internal force field.
\nError over quadratic penalty iterations compared to the reference from the literature
\nstructural_design_optimization/error_over_iterations.csv3.2.1 Effect of the Range Relaxation Factor in the Adaptive Encoding
\nError over quadratic penalty iterations for different relaxation factors
\nstructural_design_optimization/relaxation_factor/error_over_iterations_aggregated_<relaxation factor>.csv with relaxation factor in {0_25,0_5,0_75}for 0.25, 0.5, or 0.75, respectively.3.2.2 Sensitivity to Initial Ranges
\nError over quadratic penalty iterations for different initial ranges
\nstructural_design_optimization/initial_range/error_over_iterations_aggregated_<initial range>.csv with initial range in {0_1,0_5,0_1}for [0,1], [0,5], or [0,10], respectively.3.2.3 Impact of Suboptimal Solutions
\nError over quadratic penalty iterations for different numbers of QA reads
\nstructural_design_optimization/num_reads/error_over_iterations_aggregated_<num reads>.csv with num reads in {400,800}for 400 or 800 reads, respectively.3.2.4 Scalability Analysis with a Quantum-Inspired Solver
\nstructural_design_optimization/scalability_analysis/error_over_iterations_aggregated.csvThis repository contains the data used in the master thesis “Ion Beam Shaping of Catalytic Nanoparticles”, which is part of the CatIons FFG project.
\n· The folder “AFM” contains atomic force microscopy images of various samples that consist of a glassy carbon substrate with Au, Cu, or AuCu nanoparticles drop cast onto the surface. The images are organized into folders by sample. Their status (sputter cleaned/freshly drop cast/irradiated) is indicated in the file names of the images. The AFM images are .ibw files, which can be opened and processed using the free software Gwyddion (Version 2.69 was used in the course of this thesis). Additionally, there are .jpg files with screenshots showing the positioning of the AFM cantilever on the sample surface.
\n· The folder \"SDTrimSP-3D\" contains various folders, which each contain a simulation. The files are compatible with version 2.50 (incompatible with earlier versions!) To run a simulation with SDTrimSP-3D, its folder needs to be added to the “case” folder in the SDTrimSP-3D directory. The necessary input files to start a simulation are always named “tri.inp” (general simulation parameters), “fig.inp” (target geometry and composition) and “stop_run.inp” (0 or 1, depending on if the simulation should be stopped during a run)
\n· The output files created during the simulations are in the same folders.
\n-time_run.dat tracks the progress of the simulations
\n-Target_cut_00_3D.dat and Target_cut_90_3D.dat provide information on the coordinates and composition of a cross section of the target at each fluence step
\n-surf_cut_00_3D.dat and surf_cut_90_3D.dat have a similar format, but only include the surface profile of the cross section
\n-an amira.iv file is created for every single fluence step (number of outputs defined in tri.inp). Each one contains a list of voxels representing the 3D shape of the target.
\n· The folder Python contains the scripts used to plot the 2D and 3D output files, as well as Text_to_FigInput.py, which generates a fig.inp file for a target consisting only of spheres from spheres.txt. The scripts are modified versions of scripts originally created by Paul Szabo
\n· The folder SW_sphere_Au1Cu1 also contains analysis on the shape of the particle at fluence 0.0, 0.5 and 1.0
\n· More information in SDTrimSP-3D Documentation
\n· The folder “Sputter Source” contains information on the profile of the ion beam of the sputter gun. The current for the 2D profiles was measured using an M3 screw and washer as a faraday cup. The 1D profiles use the straight bottom edge of a sample holder. The current was measured using a picoamperemeter. The sample holder was moved manually. The measured currents are recorded in .csv files and plotted with plot2D.py and plot3D.py
\n\n
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L.", "given_name": "M. L.", "family_name": "Savchenko", "identifiers": [{"identifier": "0000-0003-4722-8902", "scheme": "orcid"}]}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}}, {"person_or_org": {"type": "personal", "name": "Shuvaev, Alexey", "given_name": "Alexey", "family_name": "Shuvaev", "identifiers": [{"identifier": "0000-0002-7059-3740", "scheme": "orcid"}]}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Dmitriev, I. A.", "given_name": "I. A.", "family_name": "Dmitriev"}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}}, {"person_or_org": {"type": "personal", "name": "Ganichev, S. D.", "given_name": "S. D.", "family_name": "Ganichev"}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}}, {"person_or_org": {"type": "personal", "name": "Kvon, Z. D.", "given_name": "Z. D.", "family_name": "Kvon"}, "role": {"id": "datacollector", "title": {"en": "Data collector"}}}], "title": "Demonstration of high sensitivity of microwave-induced resistance oscillations to circular polarization", "publisher": "TU Wien", "publication_date": "2026-07-03", "subjects": [{"subject": "Magnetoresistance"}, {"subject": "Terahertz"}, {"subject": "MIRO"}, {"subject": "Quantum Wells"}], "related_identifiers": [{"identifier": "10.1103/PhysRevB.106.L161408", "scheme": "doi", "relation_type": {"id": "issupplementto", "title": {"en": "Is supplement to"}}, "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/legalcode", "scheme": "spdx"}}], "description": "
These are the data for the paper:
\nM. L. Savchenko, A. Shuvaev, I. A. Dmitriev, S. D. Ganichev, Z. D. Kvon, and A. Pimenov
„Demonstration of high sensitivity of microwave-induced resistance oscillations to circular polarization“
Phys. Rev. B 106, L161408 (2022). https://doi.org/10.1103/PhysRevB.106.L161408
arXiv:2206.07600
\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 \"Transmittance_circular.oggu\" contains the magnetic field dependencies of the transmittance as a function of the magnetic field, normalized to its maximum value Tmax (b), for two circular polarisations of incident light.
\nThe file \"Resistivity_ratio.oggu\" contains the resistivity ratios at the extrema of the MIRO signals, illustrating the asymmetry of MIRO between the CR-active and CR-passive polarities for η = 1 and η = −1 . The solid curve shows the ratio of the Drude absorption calculated for η = 1 using parameters extracted from the dark transport measurements.
\nThe file \"Decomposition_circular.oggu\" shows the circular decomposition of the electric field in the waveguide: the incident E+ and distorted E− helicity components of the near field reaching the 2DES. Also shown is the size dependence of the MIRO asymmetry in experiments with varying diameters of the diaphragm. Curves are calculated using Eqs. (1), (3), and (5) of the main manuscript.
", "additional_descriptions": [{"description": "We demonstrate that the long-debated immunity of the microwave-induced resistance oscillations (MIRO) to the sense of circular polarization is in fact not an inherent property of this phenomenon in solid-state two-dimensional electron systems (2DES): We detect an up to 30 times larger MIRO signal for the cyclotron resonance (CR) active helicity, fully consistent with the concurrently measured transmission and the deduced
CR shape of the Drude absorption. Using a 2DES as a sensitive sensor of the actual polarization state, we further study extrinsic near-field effects capable of producing an apparent immunity of the photoresponse.
This repository contains the data used in the study for developing an ATR-FTIR baseline correction script:
\nAll files are stored inside zipped folders for each measurement technique. They were converted from original device environment file formats (Opus, WVASE, etc) to .tiff, .dat and .csv to increase accessibility of the data.
\nTable 1: The samples used in this study, indicating their grade, modification, ageing protocols and presence of mineral filler
\n| \n SAMPLE \n | \n\n Modification \n | \n\n Grade \n | \n\n Ageing \n | \n
| \n Bit-A \n | \n\n Unmodified \n | \n\n 70/100 \n | \n\n RTFOT, 20h/40h/60h/ | \n
| \n Bit-A1 \n | \n\n Unmodified \n | \n\n 50/70 \n | \n\n / \n | \n
| \n Bit-A2 \n | \n\n Unmodified \n | \n\n 160/220 \n | \n\n / \n | \n
| \n Bit-B \n | \n\n Unmodified \n | \n\n 70/100 \n | \n\n RTFOT, 20hPAV \n | \n
| \n Bit-C \n | \n\n Unmodified \n | \n\n 70/100 \n | \n\n RTFOT, 20hPAV \n | \n
| \n Bit-A-SBS \n | \n\n ~3.5% SBS \n | \n\n / \n | \n\n RTFOT, 20hPAV \n | \n
| \n BIT-A-6%SBS1 \n | \n\n 6% diblock SBS with high vinyl content \n | \n\n / \n | \n\n / \n | \n
| \n BIT-A-6%SBS2 \n | \n\n 6% triblock SBS with low vinyl content \n | \n\n / \n | \n\n / \n | \n
| \n Bit-F \n | \n\n Mineral Filler \n | \n\n 70/100 \n | \n\n RTFOT, 20hPAV \n | \n
| \n Bit-F-SBS \n | \n\n ~3.5% SBS & Mineral Filler \n | \n\n / \n | \n\n RTFOT, 20hPAV \n | \n
The Fourier Transform Infrared Spectrometer with UltraScan™ Interferometer, specifically a Bruker FTIR Vertex 80v + Pike GladiATR, is operated under vacuum conditions to eliminate atmospheric absorptions. The Attenuated Total Reflectance (ATR) unit is placed in an evacuated side chamber and equipped with a diamond crystal exposed to the environment, allowing direct analysis of solid and liquid samples without extensive preparation. All spectra are recorded with a liquid nitrogen cooled MCT (mercury-cadmium-telluride) detector in a spectral range from 4000 cm-1 to 600 cm-1, at a spectral resolution of 2 cm-1. Four replicate samples were prepared per binder, and each sample was measured four times resulting in a total of 16 measurements per binder. When a spectrum was deemed insufficient, a new measurement was performed. Pressure was only applied for strongly aged samples, when otherwise no proper contact with the ATR crystal could have been achieved.
\nTransmittance FTIR spectra were recorded with a Bruker FTIR Vertex 80v; a similar set-up as described above, but with a two 2-mm BaF2 windows in the transmission cell and dissolving the bitumen in dichloromethane (DCM) at a concentration of 10mg/mL. A comparable spectral range was applied; however, the lower limit was restricted to ~800 cm-1 due to the transmission cutoff of the BaF2 windows. The resolution was set to 2 cm-1, and each sample was measured using four repetitions of 24 scans. The cell spacing was determined from interference fringes, yielding an approximate pathlength of 0.14 mm.
\nAn IR-VASE by J.A. Woollam infrared Ellipsometer was used to characterize the optical properties of bitumen thin films in the mid-infrared spectral region. The instrument measures changes in polarization state – specifically the amplitude ratio (Ψ) and phase difference (Δ) – of IR radiation reflected off the sample surface. Measurements were performed over a wavenumber range of 4000 cm\u207b¹ to 400 cm\u207b¹. The collected Ψ and Δ data were converted using Fresnel equations and ellipsometry theories to extract the complex refractive index. Samples were ensured to have smooth surfaces; by spin coating a drop of bitumen-n-heptane solution (0.025 mg/mL). The measurements were taken at different angles of incidence (45, 55 and 65o), at a resolution of 32 cm-1 over a spectral range of 8000-500 cm-1; collecting 15 spectra created by 20 scans each; to reproduce the conditions within the ATR crystal.
\nA Nikon Eclipse Ci optical microscope was employed to examine the surface morphology and microstructural features of bitumen samples. Bitumen samples, roughly 5-10 g, were heated and homogenised at 150° C for not more than 2 minutes on a metal spoon, poured on pre-heated (at 150°C) microscopy slides with an indent to promote a flat surface. These were then cooled to room temperature for at least 15 minutes and then observed under brightfield illumination at a magnification of 100x. Images were captured with a digital camera integrated into the microscope system to document morphological features corresponding to spectroscopic measurements.
\nA POLOS SPIN150i spin coater was used to prepare uniform thin films of both bitumen and its SARA fractions for infrared ellipsometry measurements. The sample procedure was based on work from Hung and Fini [32]. Bitumen was first dissolved in n-heptane to obtain a homogeneous solution (0.025 g/mL), after which a small volume was dispensed onto clean glass substrates (40 µL). The samples were then spin-coated at controlled rotation speeds, an acceleration of 500 rpm/s and a speed of 1000 rpm for 30 seconds, to ensure uniform film thickness and smooth surface morphology. After spin coating, the films were allowed to dry to ensure complete solvent evaporation prior to analysis (~24 hours). This preparation method enabled reproducible thin-film formation suitable for optical characterization.
\nSARA fractionation was performed after the recommendations made by the simplified technique by Sakib & Bhasin in 2018 [33]. For each SARA separation, 400\u202f±\u202f40\u202fmg of bitumen was placed in a 250\u202fmL jar with 40 mL of HPLC-grade n-heptane. The mixture was magnetically stirred for 24 ± 2 hours at 200 rpm. Asphaltenes (n-heptane insoluble) were separated using 0.2\u202fµm PTFE syringe filters (25 mm, Thermo Scientific™ Titan3™). The resulting filtrate (maltenes) was divided into four vials; two were dried at 120\u202f°C under a nitrogen stream and the remaining mass was used to determine the maltene to asphaltene ratio. The remaining maltenes were diluted to 3.33\u202fmg/mL for solid phase extraction (SPE) with Thermo Scientific™ HyperSep™ Silica Cartridges (25\u202fmL, 40–60\u202fµm). After pre-washing the cartridges with 10 mL each, they received 15 mL of the maltene solution, followed by 10 mL n-heptane. Next, 25\u202fmL of toluene/n-heptane (80:20\u202fv/v) and 40 mL of DCM/methanol (90:10\u202fv/v) were added sequentially at separate manifold positions. The resulting SAR fractions were dried under nitrogen on a heating plate until constant mass. Asphaltene fractions were dissolved in toluene, transferred to glass vials, and evaporated similarly.
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"TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Hofko, Bernhard", "given_name": "Bernhard", "family_name": "Hofko", "identifiers": [{"identifier": "0000-0002-8329-8687", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Grothe, Hinrich", "given_name": "Hinrich", "family_name": "Grothe", "identifiers": [{"identifier": "0000-0002-2715-1429", "scheme": "orcid"}]}, "affiliations": [{"name": "TU Wien"}]}], "title": "Bitumen Refractive Index and ATR-FTIR baseline correction (script)", "publisher": "TU Wien", "publication_date": "2026-06-30", "subjects": [{"subject": "bitumen ageing"}, {"subject": "ATR-FTIR spectroscopy"}, {"subject": "Refractive Index"}, {"subject": "Baseline Correction"}, {"subject": "Bituminous Binders"}, {"subject": "Ellipsometry"}, {"subject": "SARA fractions"}], "languages": [{"id": "eng", "title": {"en": "English"}}], "related_identifiers": [{"identifier": "10.48436/10avt-mr027", "scheme": "doi", 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Copyright and license notices must be preserved. Contributors provide an express grant of patent rights."}, "props": {"url": "https://www.gnu.org/licenses/gpl-3.0-standalone.html", "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/legalcode", "scheme": "spdx"}}], "description": "This repository contains a complete processing pipeline for ATR-FTIR spectra of bituminous materials, with a focus on:
\nThe correction approach is based on refractive index–dependent ATR penetration depth and characteristic aliphatic band vibrations, allowing improved comparison between spectra and more physically meaningful interpretation of band areas.
\nThe pipeline is designed to process raw FTIR spectra in a predefined CSV format and automatically generate corrected datasets and publication-ready plots.
\nATR-FTIR measurements of bituminous materials are strongly influenced by:
\nThis script applies:
\nSpecial attention is given to aliphatic CH stretching and rocking vibrations, which are used as internal references in the correction workflow.
\nThe main pipeline performs the following steps:
\nIntegrates characteristic peak regions:
\n| Functional Group | \nWavenumber Range (cm\u207b¹) | \n
| Aliphatic CH stretching | \n2800–3000 | \n
| Carbonyl (C=O) | \n1650–1750 | \n
| Aromatic C=C | \n1520–1650 | \n
| Sulfoxides (S=O) | \n980–1070 | \n
| Aromatic bending | \n735–910 | \n
| Aliphatic CH rocking | \n710–735 | \n
Areas are extracted for:
\nThe script automatically generates:
\nAll figures are saved to disk for further analysis.
\nThe script expects raw spectra as CSV files with:
\nImproper formatting will prevent correct parsing.
\nIt is recommended that:
\nThe pipeline generates:
\nThe purpose of this project is to provide a reproducible and physically informed correction workflow for ATR-FTIR spectra of bituminous materials, enabling:
\nThe pipeline is intended for research applications in:
\npython main.py --raw data/raw --extracted data/extracted --corrected data/corrected --plots data/spectra_plotsDefault directories are provided but can be modified via CLI arguments.
\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": "Alexandra", "family_name": "Linster", "identifiers": [{"identifier": "0009-0009-6022-8086", "scheme": "orcid"}]}, "role": {"id": "contactperson", "title": {"en": "Contact person"}}, "affiliations": [{"name": "TU Wien"}]}, {"person_or_org": {"type": "personal", "name": "Neff-Kolassa, Sabine", "given_name": "Sabine", "family_name": "Neff-Kolassa", "identifiers": [{"identifier": "0000-0001-9708-085X", "scheme": "orcid"}]}, "role": {"id": "projectleader", "title": {"en": "Project leader"}}, "affiliations": [{"name": "TU Wien"}]}], "title": "ARI&Snet Projektabschluss", "publisher": "TU Wien", "publication_date": "2026-06-22", "subjects": [{"subject": "ARI&Snet"}, {"subject": "Cluster Forschung+Daten"}], "related_identifiers": [{"identifier": "10.48436/xevm6-bbk04", "scheme": "doi", "relation_type": {"id": "ispartof", "title": {"en": "Is part of"}}, "resource_type": {"id": "presentation", "title": {"de": "Pr\u00e4sentation", "en": "Presentation"}}}, {"identifier": "10.48436/3a8ng-9qf07", "scheme": "doi", "relation_type": {"id": "ispartof", "title": {"en": "Is part of"}}, "resource_type": {"id": "presentation", "title": {"de": "Pr\u00e4sentation", "en": "Presentation"}}}], "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/legalcode", "scheme": "spdx"}}], "description": "Slides for the closing meeting of the project ARI&Snet, which took place online on 22.06.2026. The meeting was held in German.
\nAGENDA
\nTechnische Infrastruktur
• Tools & Services
• Software Compliance
• Rechtliches
Öffentlichkeitsarbeit
\nAusblick ab Juli 2026
\nPräsentationen Use Cases:
• DAMAP –Call forProjects
• daspario
• NAVIA
• FUNDify
• Importtool
• PHAIDRA –PHAIDRA Depot
• UHSTAT2
• PRANK
• CAT
• ARIVO
Research domain: Geophysics
\nPurpose of the dataset: The dataset presents the apparent electrical conductivity of a 100 m long profile of the landfill in Breitenau (Lower Austria), up to a depth of approximately 5 meters.
\nMeasurement instrument: The dataset was created with an experimental multi-coil multi-frequency electromagnetic induction (EMI) system.
\nLow-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. The EMI method does not require direct contact with the ground and is therefore well suited for surveying larger areas. EMI surveys have been applied, for instance, to detect buried anthropogenic artefacts and utilities, to investigate landfills, to monitor microbial activity in hydrocarbon contaminated sites, to locate archeologic remains, and to resolve shallow soil properties such as variations in soil water content, clay content, organic matter content, or salinity. EMI instruments often permit to measure the electrical conductivity from multiple depths simultaneously by implementing either multiple receiver coils at varying distances to the transmitter, or implementing multiple simultaneous frequencies for a single transmitter-receiver pair. In the dataset presented here, four receiving coils arranged in distances of 50 cm, 100 cm, 170 cm, and 370 cm from the transmitter, and three simultaneous operating frequencies (3.2 kHz, 9.6b kHz, and 41.6 kHz) have been used.
\nStructure of the dataset: The dataset is a .csv file. Column headers are described in a .pdf file that accompanies the dataset.
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Evaluation Tests", "publisher": "TU Wien", "publication_date": "2026-06-29", "related_identifiers": [{"identifier": "10.1016/j.eswa.2026.133468", "scheme": "doi", "relation_type": {"id": "issupplementto", "title": {"en": "Is supplement to"}}, "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/legalcode", "scheme": "spdx"}}, {"id": "gpl-3.0-or-later", "title": {"en": "GNU General Public License v3.0 or later"}, "description": {"en": "Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights."}, "props": {"url": "https://www.gnu.org/licenses/gpl-3.0-standalone.html", "scheme": "spdx"}}], "description": "
This repository accompanies our study on heterogeneous ensembles for anomaly detection. Anomaly detection algorithms vary in their implicit assumptions and definitions of anomalies. When the distribution and characteristics of anomalies are largely unknown, combining diverse methods can improve robustness and overall detection performance. Motivated by this, we focus on ensembles that combine heterogeneous approaches, addressing three key aspects: the impact of method combination, the effect of score normalization and aggregation, and the influence of evaluation metrics on ensemble design.
\nWe evaluate 14 established anomaly detection algorithms, including ABOD, K-NN, SDO, LODA, HBOS, OCSVM, PCA, IForest, LOF, GLOSH, AE, DeepSVDD and two feature bagging ensembles nesting K-NN (FBkNN) and LOF (FBLOF). on two collections of structured tabular datasets: GLCA (100 synthetic dataset) and ADBench-Classic (47 real datasets). We additionally carry out validation tests with the CIC2017IDS dataset.
\nExperiments test different ensemble configurations, including varying ensemble sizes, homogeneous versus heterogeneous ensembles, and multiple score normalization and aggregation strategies. This repository provides the datasets, algorithm implementations in Python, and experiment configurations used in our study. It enables replication of our experiments, further evaluation, and comparison of anomaly detection methods and ensemble strategies. It is framed within the broader research domains of anomaly detection, ensemble learning, unsupervised learning, machine learning, and data mining.
\nDocker
\nThis repository includes required Docker files to build a containerized environment for testing as well as the already pre-compiled docker-image ready to be used.
\nExperiments are conducted in Python v3.11. The file and folder structure is as follows:
\n\"AD_ensembles_Apr2026.tar\" contains the pre-compiled docker-container-image to run experiments in a secure, tested environment
\n\"docker.zip\" contains docker configuration files, including Python dependencies, to build the docker environment of the pre-compiled image.
\n“HetEns_Apr2026.zip” contains the source files with datasets (ADBench and GLCA collections) and Python scripts.
\n“results_Apr2026.zip” contains results, performance files, log files, tables, figures and all extracted statistics and material of experiments as published in the paper.
\nMost algorithms used are implementations from the PyOD library/repository (https://pyod.readthedocs.io/en/latest/index.html), presented in:
\n\n\nHan, S., Hu, X., Huang, H., Jiang, M., & Zhao, Y. (2022). Adbench: Anomaly detection benchmark. Advances in neural information processing systems, 35, 32142-32159.\n
\n
The CC-BY license applies to all data generated with the GLCA tool.
\nAll distributed code is under the GPLv3+ license.
\nADBench datasets were originally published in the repository: ADBench (https://github.com/Minqi824/ADBench/tree/main), In particular, the Classical collection (https://github.com/Minqi824/ADBench/tree/main/adbench/datasets/Classical)
\nThese datasets are © the original authors and are licensed under the BSD 2-Clause \"Simplified\" License. No endorsement by the original authors is implied.
\n\n
The CIC-IDS2017 dataset (Kaggle version by Laurens D'hooge, https://www.kaggle.com/datasets/dhoogla/cicids2017), originally by the Canadian Institute for Cybersecurity (CIC), University of New Brunswick. This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International (CC BY-NC-SA 4.0) License.
\n
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"given_name": "Magdalena", "family_name": "Solitro"}, "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": "Maffei, Matteo", "given_name": "Matteo", "family_name": "Maffei", "identifiers": [{"identifier": "0000-0001-8061-1685", "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": "Wappler: Sound Reachability Analysis for WebAssembly (artifacts)", "publisher": "TU Wien", "publication_date": "2024-10-08", "subjects": [{"id": "http://www.oecd.org/science/inno/38235147.pdf?1.2", "subject": "Computer and information sciences", "scheme": "FOS"}], "languages": [{"id": 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This dataset contains the software and benchmarks accompanying the paper \"Wappler: Sound Reachability Analysis for WebAssembly\" (published at the 2024 IEEE 37th Computer Security Foundations Symposium (CSF)).
\nwappler-src.zip contains the sources for our prototype reachability analyzer Wappler (./wappler), and its dependencies (./horst, ./wparser). Additionally, it contains the test specifications for the code examples mentioned in the paper (./files) and a script to convert existing tests from the official WebAssembly specification [1] to Wappler's format (./scripts). The flake files (flake.nix, flake.lock) contain code to build the different components separately or bundle them in a Docker container. README.md contains exact build/reproduction instructions.
docker-image-wappler.tar.gz is a Docker container image that realizes the sources in wappler-src.zip.
\n
[1] https://github.com/WebAssembly/spec
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It is the extra delay introduced by deformation of the antenna given as a function of elevation.
\nThese are data to the paper:
\nA. Shuvaev, L. Pan, L. Tai, P. Zhang, K. L. Wang, and A. Pimenov
“Universal rotation gauge via quantum anomalous Hall effect”
(featured article) Appl. Phys. Lett. 121, 193101 (2022). https://doi.org/10.1063/5.0105159
\n
File format: \"OriginPro\" (OriginLab Corporation)
\nFor the free Viewer see the link: https://www.originlab.com/viewer/ . All the necessary information is in Q&A section.
\n\n
The file \"step_in_transmission.oggu\" contains
\na) Transmission of the (Cr0.12Bi0.26Sb0.62)2Te3 film on the substrate in zero magnetic field showing the Fabry–Perot resonances. Symbols cottespond to the experiment, solid lines - to theoretical transmission of a film on a substrate.
\nb) Off-diagonal (Hall) transmission and a phase shift as function of external magnetic field.
\nc) Hall resistivity.
\n\n
The file \"Faraday_rotation_step.oggu\" contains the data to the step in Faraday rotation that are equal to the fine-structure constant.
\nAlso given are:
\na) Frequency dependece of the step amplitude.
\nb) Temperature dependence of the step amplitude for two samples.
\nc) Temperature dependence of the step in Hall resistivity
", "additional_descriptions": [{"description": "These are data to the paper:
\nA. Shuvaev, L. Pan, L. Tai, P. Zhang, K. L. Wang, and A. Pimenov
“Universal rotation gauge via quantum anomalous Hall effect”
(featured article) Appl. Phys. Lett. 121, 193101 (2022). https://doi.org/10.1063/5.0105159
Abstract. Integer quantum Hall effect allows to gauge the resistance standard up to more than one part in a billion. Combining it with the speed of light, one obtains the fine-structure constant a 1/137, a dimensionless reference number that can be extracted from a physical experiment. Most exact notion of this value and especially its possible variation on the cosmological time scales is of enormous relevance for fundamental science. In an optical experiment, the fine-structure constant can be directly obtained as purely geometrical angle by measuring the quantized rotation of light polarization in two-dimensional quantum wells. In realistic conditions, high external magnetic fields have to be applied, which strongly affects possible attainable accuracy. An elegant solution of this problem is provided by quantum anomalous Hall effect where a universal quantized value can be obtained in zero magnetic field. Here, we measure the fine-structure constant in a direct optical experiment that requires no material adjustments or technical calibrations. By investigating the Faraday rotation at the interference maxima of the dielectric substrate, the angle close to one a is obtained at liquid helium temperatures without using a dilution refrigerator. Such calibration
and parameter-free experiment provides a system-of-unit-independent access to universal quantum of rotation.
We demonstrate that arynes can be generated and trapped efficiently through mechanically assisted microscale mixing on a standard vortex mixer. Brief vortex agitation enables ultra-fast generation and interception of these highly reactive intermediates with N-, O-, S-, and C-nucleophiles within 1 minute, therefore providing a low-energy-solvent minimized approach for aryl functionalization without the need for inert atmosphere, heating, or bulk solvent.Uploaded files:
\nThis dataset provides simulation results investigating the combined impacts of climate change and air conditioning (AC) adoption on low-voltage distribution grids in the diploma thesis: Buchmeier, J. (2025). Impact of Climate Scenarios and Air Conditioning Penetration on Low-Voltage Grid Stability. Diploma Thesis, TU Wien. https://doi.org/10.34726/hss.2025.133331
\nThe dataset includes:
\nAll data is provided at 15-minute resolution for the cooling season (May 1 – September 30). The dataset was created as part of the INFRADAPT project, funded by the Climate and Energy Fund under the program Energieforschung 2022.
The dataset uses the SECURES-MET Climate Data for temperature and irradiation data in RCP4.5 and RCP8.5 scenarios for Vienna(AT130).
And it uses SimBench Grid Models, namely LV Rural 3 for the base grid, PV adoption and baseload profiles.
Detailed description is given in the README and the Diploma Thesis.
The dataset contains reproduction scripts to reproduce most of the Diploma Thesis plots.
Keywords: low-voltage grids, climate change, air conditioning, power system simulation, RCP scenarios, grid stability
License: all data is licensed by CC-BY-4.0 and all software by MIT
DOI: 10.48436/krdb5-dn585
Sources:
\nSECURES-MET Climate Data
Formayer, H., Maier, P., et al. (2023). SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7907883
Used for: Temperature and irradiance time series for RCP4.5, RCP8.5, and Heatwave scenarios.
\nSimBench Grid Models
University of Kassel, Fraunhofer IEE, RWTH Aachen University, TU Dortmund (2019). SimBench - Power System Benchmark Models. https://simbench.de
License: Open Database License (ODbL)
Used for: Low-voltage grid topology, cable and transformer parameters, node structure.
\nSciencePlots Visualization Styles
Garrett, J. SciencePlots: Matplotlib styles for scientific figures. GitHub: https://github.com/garrettj403/SciencePlots
License: MIT
Used for: Matplotlib style configuration in reproduction scripts.
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"This dataset contains the source images, intermediate image stacks, numerical data, calculation workbooks, and plotting scripts that support the manuscript figures and table. 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\nFigure01/Figure02/Fig.02(i-j)/plot_Drift_New.py regenerates the height-offset and drift-coordinate plots from Height.dat and Drift.dat.Figure03/Figure04/Table01/Figure05/Figure06/Fig.06(d-e)/plot_Fit_New.py regenerates the directional FFT-profile plots; Fig.06(f-g)/Plot_final_New.py regenerates the frequency-summary plots.Figure07/plot_Hysteresis.py regenerates the hysteresis plots, and plot_Amplitude_Response.py regenerates the two summary plots.Figure08/*.png: figure-ready rendered panels, plots, masks, or color-map keys.*.tif: source, intermediate, or reconstructed image data. A TIFF may be a single image or a multi-page stack.*.dat and *.txt: numerical data used for plotting, fitting, or quantitative analysis.*.xlsx: calculation workbooks and summaries.*.py: plotting scripts and their associated input data are stored in the same figure folder.Center_Ref.tif has two frames.Refer to the local README in each folder for the exact stack page order and panel-to-file correspondence.
\nThe Python code included in this dataset is licensed under the MIT License.
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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/licenses/MIT", "scheme": "spdx"}}], "description": "This record contains a frozen snapshot of the solver source code, constraint models, and experiment configurations accompanying the paper \"GPMS: A Generalized Parallel Machine Scheduling Framework with Rich Temporal and Resource Constraints\".
\nLink to the paper: 10.1609/icaps.v36i1.42866
\nReference: Frühwirth, L., Einspieler, C., Musliu, N., & Winter, F. (2026). GPMS: A Generalized Parallel Machine Scheduling Framework with Rich Temporal and Resource Constraints. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 36, No. 1, pp. 490-499).
\nPurpose: An open-source solver for the generalized parallel machine scheduling problem, which combines, among others, time-varying machine availability, precedence constraints with time lags, secondary resources with time-varying capacity, and sequence-dependent setups. The snapshot reproduces the exact and heuristic results reported in the paper.
\nContents: construction heuristics, a CP-SAT (OR-Tools) model, equivalent MiniZinc constraint models, a command-line runner, a parameterized instance generator, and the experiment configurations used in the paper.
\nThe code is a .NET 8 solution organized by component:
\nThe solver CLI is driven by JSON configuration files passed via --config. The configurations are grouped by instance set (generated, real_type1, real_type2, real_type3) and by solver: construction heuristics only (*_CH), CP-SAT cold start (*_CP_cold), CP-SAT warm-started from the construction heuristics (*_CP_warm), and the MiniZinc model solved with the OR-Tools CP-SAT backend (*_Mzn_CP). Build and run instructions are provided in the README inside the archive.
\nRequired software: Building and running require the .NET 8 SDK. The MiniZinc-based exact models additionally require a MiniZinc installation; the CP-SAT and heuristic solvers do not. All other dependencies are public NuGet packages restored automatically from nuget.org.
\nAll files in the archive are plain text and are best opened in a code editor or IDE (such as Visual Studio, Visual Studio Code, or JetBrains Rider) rather than a plain text editor. The archive contains the following file types:
\n .cs C# source code, the solver implementation. Compiled by the .NET 8 SDK
.sln the Visual Studio solution file, the build entry point (dotnet build ParallelMachineScheduling.sln)
.csproj MSBuild project files declaring target framework and dependencies, read automatically by the build
.config nuget.config, pointing dependency restore at nuget.org
.mzn MiniZinc constraint models, needed only to run the MiniZinc models; opened and run with a MiniZinc installation/IDE
.json experiment configurations under Configs/ (solver settings and paths), needed to reproduce the runs
.md README.md with build and run instructions
LICENSE the MIT license text (no file extension)
The code is released under the MIT License.
\nAdditional resources: The benchmark instances used in the experiments are archived in a companion record at 10.48436/g0cyt-4h155
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