Universität Innsbruck - Data Repository
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Enzyme-responsive polymer formulations: a turning point in the fight against implant-related biofilm
No full text<p>Oral presentation at CRS Local Chapter Meeting Germany/Switzerland/Austria (DeChAt) on February 13 and 14, 2025 in Bern, Switzerland. </p>
NanoBioRS
No full text<p>Poster and one minute oral presentation. Zukunft Pharmazie 2025. 16.06.2025. Innsbruck, Austria</p>
<p> </p>
DIGIdat Metadata and Measurements
No full text<h2>DIGIdat Metadata and Measurements Database</h2>
<p>This sqlite database includes all public data of the DIGIdat project. The database can be accessed via an Sqlite Viewer Software or the python library "digidat_db_interface", which was developed for the data analysis of the project. Supplementary material can be found in the folders "images", which contains pictures of the measured sites, and "documentation", which includes a database overview and a column wise explanation for the given dataset.</p>
<h3>Acknowledgement</h3>
<p>Project DIGIdat was funded by the Sparkling Science Programme of the Austrian Agency for Education and Internationalisation (OEAD) and the Federal Ministry of Education, Science and Research Austria (BMBWF). We cordially thank all the students, teachers, school principles and other school personell for the strong commitment and contributions to the success of the project.</p>
An electrophilic uridine building block for post synthetic RNA modification as exemplified for spin labeling
No full text<h2 class="Title1">An electrophilic uridine building block for post synthetic RNA modification as exemplified for spin labeling</h2>
<p>- Zip files include data on manuscript, data fitting python scripts, NMR data of building block <strong>10</strong> and RNAs used in this study.</p>
<p> </p>
Metapictor | Embedded Self-Portraits in Fifteenth-Century Painting – A Systematic Assessment
No full text<p><em>Metapictor</em> is part of the project <em>Embedded Self-Portraits in Fifteenth-Century Painting. A Systematic Assessment (2020–2025)</em> led by Lukas Madersbacher (LFU Innsbruck) and funded by the FWF (Principal Investigator Project P 33552). This upload contains the dataset collected as part of the <em>Metapictor</em> database curated by Elisabeth Krabichler, senior staff member.</p>
<h3>Context and methodology</h3>
<p>The aim of the project was to record, analyse and evaluate potential embedded self-portraits in <em>Fifteenth-Century</em> wall and panel paintings (in Italian, Dutch and German-speaking regions) and to make the collected data available digitally (open access). In addition the data will be published via a publicly accessible web application based on a relational database.</p>
<p>The data collected in the digital archive includes research data and further information on the project.</p>
<p>The research data is divided into catalogue entries in which objects (with object data) are combined with the data collected in the categories Artist, Portraits and Contexts.</p>
<ul>
<li>Artist: biographical data, overarching contexts, possible self-portraits</li>
<li>Portraits: theses on possible self-portraits, state of research and plausibility</li>
<li>Contexts: considerations on thematic references, multi-part art objects, groups of works and overarching aspects</li>
</ul>
<p>A detailed explanation of the data, their function and structure is provided in the readme.pdf (German) attached to the data set.</p>
<h3>Technical details</h3>
<ul>
<li>Database provided in the following formats: .sqlite, .json and, .xml</li>
<li>Documentation of the database structure in .pdf format (German)</li>
</ul>
<h3>Further details</h3>
<ul>
<li>
<p>Web platform: <a href="https://explore-research.uibk.ac.at/arts/metapictor/">https://explore-research.uibk.ac.at/arts/metapictor/</a> (with further information on methodology).</p>
</li>
</ul>
Mutual correspondence and translation bias of German sollen and Spanish deber
No full text<p>Data related to a study of mutual correspondence of German sollen and Spanish deber extracted from the PaGeS nuclear corpus, entries between 1999 and 2019 (https://www.corpuspages.eu/corpus/about/about?lang=de)</p>
<p>Collection of translation options of the German modal verb sollen into Spanish as presented by Schmidhofer&MIllán Vidal (2025) and Soler Bonafont (2023).</p>
<p>Dataset of 100 originally German entries containing sollen and their translations into Spanish</p>
<p>Dataset of 100 originally Spanish entries containing deber and their translation into German</p>
Dataset for Interlaboratory Evaluation of Experimental Setups for Textile Reinforcements
No full text<p><strong>Interlaboratory Evaluation of Experimental Setups for Textile Reinforcements</strong></p>
<p><strong>Overview</strong></p>
<p>This repository provides the raw experimental data underlying the results presented in the associated publication on an interlaboratory investigation of uniaxial tensile tests and bond tests for textile-reinforced concrete (TRC). The data are made available to support transparency, traceability, and reproducibility of the reported findings.</p>
<p>The repository is limited to raw measurement data and does not include analysis or evaluation scripts.</p>
<p><strong>Scope and Description of the Data</strong></p>
<p>The experimental program comprised a series of uniaxial tensile tests and bond tests performed on TRC specimens with different textile configurations. The tests were conducted by multiple independent laboratories following predefined and harmonized testing procedures, while accounting for variations in laboratory environments and equipment.</p>
<p>In accordance with the methodology described in the publication, the raw data were subjected to a manual pre-selection process prior to evaluation. During this process, datasets affected by incomplete recordings, evident measurement errors, or insufficient data quality were identified and excluded from further analysis.</p>
<p>Only datasets that were complete, internally consistent, and suitable for quantitative and qualitative evaluation were retained. Consequently, this repository contains exclusively those datasets that formed the basis of the statistical analyses and results reported in the publication. Datasets excluded during the pre-selection process are not listed or archived in this repository. Therefore, general information files are supplied, with the necessary additional data provided by the authors.</p>
<p>As such, the data provided here correspond directly and unambiguously to the results discussed in the associated work.</p>
<p><strong>Abstract</strong></p>
<p>This dataset accompanies an interlaboratory study investigating the comparability, robustness, and precision of uniaxial tensile tests and bond tests for textile-reinforced concrete (TRC) considering different textile configurations. The experimental campaign was carried out by multiple laboratories using harmonized testing protocols, while allowing for differences in test equipment and boundary conditions. To ensure objectivity in the subsequent evaluation, all laboratory identifiers were anonymized. The aim of the round-robin test (RRT) was not to assess the performance or accuracy of individual laboratories, but to evaluate the repeatability, reproducibility, and interlaboratory scatter of the applied test methods. The statistical evaluation followed the framework of ISO 5725 Parts 1–6, enabling a systematic assessment of within-laboratory and between-laboratory variability. The results show a good agreement between laboratories for uniaxial tensile tests, whereas bond tests exhibit a significantly higher scatter, particularly in the range of the maximum bond shear stress. This increased variability is primarily attributed to differences in test setup and load introduction, rather than to fundamental differences in material behavior. Despite the higher numerical scatter observed in bond test results, an analysis of the qualitative bond flow–crack opening curves reveals a consistent characteristic shape across all laboratories. This indicates a generally successful and comparable execution of the bond tests and highlights the importance of considering both quantitative results and qualitative response characteristics when assessing bond test data. The raw data provided in this repository enable independent verification of the reported findings and provide a basis for further refinement of bond test setups, evaluation procedures, and standardization efforts.</p>
<p><strong>Reproducibility</strong></p>
<p>The data supplied in this repository allow for the reproduction of the reported results within the methodological framework described in the publication. All datasets used for the analyses are provided in full and correspond directly to the evaluated results.</p>
<p><strong>Contact</strong></p>
<p>For questions regarding the experimental program, data structure, or evaluation methodology, please contact:</p>
<p>Jonas Wachter<br>[email protected]</p>
When velocity autocorrelations mirror force autocorrelations: Exact noise-cancellation in interacting Brownian systems (data)
No full text<p>Raw data for "When velocity autocorrelations mirror force autocorrelations: Exact noise-cancellation in interacting Brownian systems" by Anton Lüders, Suvendu Mandal, and Thomas Franosch.</p>
<p> </p>
<p>See Phys. Rev. E <strong>113</strong>, 035305 (2026) for the full paper.</p>
<p>DOI: https://doi.org/10.1103/8hrb-bkv9</p>
Synthetic Photogrammetric Dataset for Two-Media 3D Reconstruction: Shipwreck & Terrain
No full text<p><span>The data in this repository serves as a benchmark for the development of new image-based processing pipelines that consider two separate optical media, i.e., water and air, including refraction effects. Ground truth datasets representing the true geometry form the basis of the simulation.</span></p>
<p><span>The synthetic scene showcases an environment constructed from both real-world laser scanning data (Jamtal glacial valley - WGS84: 46.90° N, 10.17° E) and a synthetic CAD model of a sailing ship. Although the Jamtal scan depicts a riverbed, the original data were not acquired underwater and is only synthetically flooded. The ship was chosen because of the fine details of the ropes, which are well-suited for determining the quality of the reconstruction, and larger structured areas on the hull, where classic photogrammetric methods should perform well. In the simulation dataset the entire ship is submerged and large parts of the scene are submerged (up to 8 m depth), therefore the influence of refraction must be taken into account for an accurate reconstruction of these areas. The planar water surface serves as an idealized baseline to evaluate geometric refraction correction without the interference of dynamic wave patterns.</span></p>
<p>Further technical details can be found in <em>description_dataset.pdf</em>.</p>
Free chiral Hexbugs compared to active Brownian circle swimmers (data)
No full text<div>
<div>
<div>
<div># Hexbugs_as_ABC_Project</div>
<br>
<div>Reproducible analysis and figure generation for HexBug experiments/simulations:</div>
<div>- ISF 3‑panel plots per dataset</div>
<div>- Dual MSD plot (FreeHexbug + MDF2)</div>
<div>- Propagator 2×2 panel (short/long lags)</div>
<div>- Optional: run native C++ simulator via GNU screen</div>
<div>- Precomputed standard deviations for MSD/ISF are included to save time</div>
<br>
<div>Quickstart</div>
<div>----------</div>
<div>```bash</div>
<div># 1) Environment</div>
<div>conda create -n hexbugs python=3.10 -y</div>
<div>conda activate hexbugs</div>
<div>pip install numpy scipy matplotlib lmfit</div>
<br>
<div># 2) System deps (Ubuntu)</div>
<div>sudo apt-get install -y g++ screen texlive-latex-base dvipng</div>
<br>
<div># 3) Set project root (recommended)</div>
<div>export HEXBUGS_PROJECT_DIR=/absolute/path/to/Hexbugs_as_ABC_Project</div>
<br>
<div># 4) Put raw .mat files into:</div>
<div># data/mdf2/trajectories/RawData/</div>
<div># data/FreeHexbug/trajectories/RawData/</div>
<br>
<div># 5) Run the pipeline</div>
<div>python PythonScripts/1_io.py</div>
<div>python PythonScripts/2_process_trajectories.py</div>
<div>python PythonScripts/3_MSD.py</div>
<div>python PythonScripts/4_ISF.py</div>
<div>python PythonScripts/5_plots.py</div>
<div>```</div>
<br>
<div>Project layout</div>
<div>--------------</div>
<div>```</div>
<div>Hexbugs_as_ABC_Project/</div>
<div>├─ C++/</div>
<div>│ ├─ build/ # compile.sh (build + run via screen)</div>
<div>│ ├─ hexbugs</div>
<div>│ ├─ include</div>
<div>│ └─ src</div>
<div>├─ data/</div>
<div>│ ├─ FreeHexbug/</div>
<div>│ │ └─ trajectories/RawData/ # put nocubes14.mat here</div>
<div>│ ├─ inertia/</div>
<div>│ └─ mdf2/</div>
<div>│ └─ trajectories/RawData/ # put tr_cm_*.mat here</div>
<div>├─ imgs/</div>
<div>│ ├─ FreeHexbug/</div>
<div>│ ├─ inertia/</div>
<div>│ └─ mdf2/</div>
<div>└─ PythonScripts/</div>
<div>├─ 1_io.py</div>
<div>├─ 2_process_trajectories.py # can build & launch C++ via screen</div>
<div>├─ 3_MSD.py</div>
<div>├─ 4_ISF.py</div>
<div>└─ 5_plots.py # main paper figures</div>
<div>```</div>
<br>
<div>Requirements</div>
<div>------------</div>
<div>- Python ≥ 3.9 (tested with 3.10)</div>
<div>- Python packages:</div>
<div>- numpy, scipy, matplotlib, lmfit</div>
<div>- Optional (for paper-quality fonts in figures):</div>
<div>- LaTeX: texlive-latex-base, dvipng (or set text.usetex=False in the scripts)</div>
<div>- Optional (for C++ simulator):</div>
<div>- g++ (C++17), GNU screen</div>
<br>
<div>Configuration</div>
<div>-------------</div>
<div>- Path resolution:</div>
<div>- If HEXBUGS_PROJECT_DIR is set, scripts use it.</div>
<div>- Otherwise they resolve the project folder relative to their file location.</div>
<div>- Set it explicitly (recommended):</div>
<div>```bash</div>
<div>export HEXBUGS_PROJECT_DIR=/absolute/path/to/Hexbugs_as_ABC_Project</div>
<div>```</div>
<br>
<div>Data input</div>
<div>----------</div>
<div>- MDF2 raw .mat files → data/mdf2/trajectories/RawData/</div>
<div>- FreeHexbug raw .mat → data/FreeHexbug/trajectories/RawData/</div>
<div>- 1_io.py creates missing folders and converts .mat → .dat trajectories.</div>
<br>
<div>Expected formats used later</div>
<div>- msd_isf_*.dat (tab-delimited with header): lag col 0, MSD col 1, ISF cols 5..19, counter in last col</div>
<div>- STD_deviation_python*.dat (CSV header): lag, msd, msd_std, count</div>
<div>- STD_deviation_ISF_python*.dat (CSV header): q, lag, ISF, ISF_std, count</div>
<br>
<div>Precomputed standard deviations (included)</div>
<div>------------------------------------------</div>
<div>- To save time, precomputed standard deviation files are shipped with the repository:</div>
<div>- MSD std: data/*/STD_deviation_python*.dat</div>
<div>- ISF std: data/*/STD_deviation_ISF_python*.dat</div>
<div>- You can delete these files to force recomputation (3_MSD.py for MSD std, 4_ISF.py for ISF std). Be aware: recomputation can take a long time.</div>
<br>
<div>How to run (VS Code and terminal)</div>
<div>---------------------------------</div>
<div>VS Code:</div>
<div>- Open the repo, select conda env (Python 3.10), click Run (“Play”) in order:</div>
<div>1) 1_io.py</div>
<div>2) 2_process_trajectories.py</div>
<div>3) 3_MSD.py</div>
<div>4) 4_ISF.py</div>
<div>5) 5_plots.py</div>
<br>
<div>Terminal (same order):</div>
<div>```bash</div>
<div>python PythonScripts/1_io.py</div>
<div>python PythonScripts/2_process_trajectories.py</div>
<div>python PythonScripts/3_MSD.py</div>
<div>python PythonScripts/4_ISF.py</div>
<div>python PythonScripts/5_plots.py</div>
<div>```</div>
<br>
<div>What each script does</div>
<div>---------------------</div>
<div>- 1_io.py</div>
<div>- Ensures folder structure under data/*/trajectories[/RawData]</div>
<div>- Converts MDF2 and FreeHexbug .mat → per‑trajectory .dat files</div>
<div>- 2_process_trajectories.py</div>
<div>- Loads .dat trajectories, crops/filters, combines</div>
<div>- Produces:</div>
<div>- data/FreeHexbug/FreeHexbug_cropped_combined_filtered.dat</div>
<div>- data/FreeHexbug/FreeHexbug_cropped_distinct.dat</div>
<div>- data/mdf2/MDF2_cropped_combined_filtered.dat</div>
<div>- data/mdf2/MDF2_cropped_distinct.dat</div>
<div>- Optionally compiles and launches the C++ simulator via C++/build/compile.sh</div>
<div>- 3_MSD.py</div>
<div>- Computes MSD std if missing from trajectories (skips if STD_deviation_python*.dat exists)</div>
<div>- Fits the MSD model; writes fit reports to:</div>
<div>- data/FreeHexbug/msd_fit_report_FreeHexbug.txt</div>
<div>- data/mdf2/msd_fit_report_mdf2.txt</div>
<div>- 4_ISF.py</div>
<div>- Computes ISF std if missing (skips if STD_deviation_ISF_python*.dat exists); fits ISF across k-values</div>
<div>- Saves per‑k diagnostics to imgs/isf_fitting_helper/</div>
<div>- Writes fit reports:</div>
<div>- data/FreeHexbug/isf_fit_report_FreeHexbug.txt</div>
<div>- data/mdf2/isf_fit_report_mdf2.txt</div>
<div>- 5_plots.py</div>
<div>- Builds final figures:</div>
<div>- imgs/FreeHexbug/ISF/ISF_isffit_msdfit_exp.pdf</div>
<div>- imgs/mdf2/ISF/ISF_isffit_msdfit_exp.pdf</div>
<div>- imgs/MSD_dual_plot.pdf</div>
<div>- imgs/propagator_2x2.pdf</div>
<div>- imgs/inertia/propagator_inertia.pdf</div>
<div>- Caches analytic ISF curves under data/<dataset>/isf_data/ on first run</div>
<br>
<div>C++ simulator details (optional)</div>
<div>--------------------------------</div>
<div>Build and run via script (recommended):</div>
<div>```bash</div>
<div>cd C++/build</div>
<div>chmod +x compile.sh</div>
<div>./compile.sh</div>
<div>```</div>
<div>- Starts a screen session hexbugs_session with multiple parallel runs (num_screens in compile.sh).</div>
<div>- Each tab runs: ./hexbugs <RANK> <SIZE></div>
<br>
<div>Screen basics:</div>
<div>- attach: screen -r hexbugs_session</div>
<div>- next tab: Ctrl+a, n | previous: Ctrl+a, p | detach: Ctrl+a, d | quit: screen -S hexbugs_session -X quit</div>
<br>
<div>Manual build/run:</div>
<div>```bash</div>
<div>g++ -I C++/include C++/src/hexbugs.cpp C++/src/nrutil.cpp C++/src/random_mars.cpp -o C++/build/hexbugs -O2 -w</div>
<div>C++/build/hexbugs 0 9 # RANK SIZE</div>
<div>```</div>
<br>
<div>Inputs (hexbugs.cpp → filepaths)</div>
<div>- data/FreeHexbug/FreeHexbug_cropped_distinct.dat</div>
<div>- data/mdf2/MDF2_cropped_distinct.dat</div>
<div>- data/FreeHexbug/FreeHexbug_cropped_combined_filtered.dat</div>
<div>- data/mdf2/MDF2_cropped_combined_filtered.dat</div>
<div>- data/Simulation_data/Noise/combined/{1..4}.dat, Data.dat</div>
<br>
<div>Input formats</div>
<div>- RANK 0/1 (distinct): “x y t trajnum”</div>
<div>- RANK 2/3 (combined filtered): “x y t”</div>
<div>- RANK 4–8 (sim): “t x y”</div>
<div>- Missing frames handled by ceil(t) + NaN gaps to keep integer time steps.</div>
<br>
<div>Outputs</div>
<div>- msd_isf_<basename>.dat with header:</div>
<div>- lag, msd, mqd, ngp, skewness,</div>
<div>- isfx[<L1..L15>], isfy[<L1..L15>], isfr[<L1..L15>],</div>
<div>- isfx_im[<L1..L15>], isfy_im[<L1..L15>], isfr_im[<L1..L15>],</div>
<div>- counter</div>
<br>
<div>Notes</div>
<div>- In modify_filepath (RANK 4–8) some absolute paths are hardcoded (e.g., /home/tom/...). Adjust if needed.</div>
<div>- <bits/stdc++.h> is GCC‑specific; on non‑GCC compilers include headers individually.</div>
<div>- N = 10,000,000 is predefined → high RAM use; reduce if needed.</div>
<br>
<div>Troubleshooting</div>
<div>---------------</div>
<div>- Matplotlib LaTeX errors: install texlive-latex-base + dvipng, or in the scripts:</div>
<div>```python</div>
<div>import matplotlib as mpl</div>
<div>mpl.rcParams.update({'text.usetex': False})</div>
<div>```</div>
<div>- Path issues: verify HEXBUGS_PROJECT_DIR and the folder structure.</div>
<div>- First run is slow: analytic ISF curves are generated and cached; re‑runs are faster.</div>
<div>- Recompute stds: delete data/*/STD_deviation_python*.dat and/or data/*/STD_deviation_ISF_python*.dat to recompute (time‑consuming).</div>
<div>- Array alignment: ensure masks are created on the correct lag axis (handled in the scripts).</div>
<br>
<div>Reproducibility</div>
<div>---------------</div>
<div>- Freeze current environment:</div>
<div>```bash</div>
<div>pip freeze > requirements.txt</div>
<div>```</div>
<div>- Reproduce:</div>
<div>```bash</div>
<div>pip install -r requirements.txt</div>
<div>```</div>
<div>- Or with conda:</div>
<div>```bash</div>
<div>conda env export > environment.yml</div>
<div>conda env create -f environment.yml</div>
<div>```</div>
<br>
<div>Citation</div>
<div>--------</div>
<div>Code for the theoretical intermediate scattering function is taken from:</div>
<div>- Rusch, R. (2024). Intermediate scattering function of a gravitactic circle swimmer. Universität Innsbruck. https://doi.org/10.48323/pykgf-vhf05</div>
<br>
<div>If you use this repository, please also cite the associated manuscript.</div>
</div>
</div>
</div>