Universität Innsbruck - Data Repository
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Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions
No full text<h2>Materials of the "<em>Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions</em>"</h2>
<h3>Content</h3>
<p>This dataset contains the following data from the "Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions":</p>
<ul>
<li>Combat_description_final.pdf (description of the provided materials, the implementation and competition rules, and the performance evaluation and ranking for the combat)</li>
<li>Case_study.inp (calibrated SWMM file of the case study)</li>
<li>rain2018.dat (rain data over 1 year in 1min resolution)</li>
<li>temp2018.dat (temperature data over 1 year in 10min resolution)</li>
<li>implementation_details.xlsx (details to the connectable imperviousness area and installable NBS area per NBS type and subcatchment)</li>
<li>solutions.xlsx (empty spreadsheet to fill out the connected imperviousness area and implemented NBS area per NBS type and subcatchment)</li>
<li>PerformanceIndicator_Teams.xlsx (anonymised performance indicators of the participating teams)</li>
</ul>
<h3>Reference</h3>
<p>If you use any material of the combat, please cite it as:</p>
<p>Reference</p>
Data for figures Publication "Regeneration of Ni-Zr dry reforming catalysts in CO2: reduction of coking and Ni re-dispersion"
No full text<h2>A primer on your dataset's description (to be edited)</h2><p>The influence of proper documentation on the reusability for research data should not be underestimated!<br>In order to help others understand how to interpret and reuse your data, we provide you with a few questions to help you structure your dataset's description (though please don't feel obligated to stick to them):</p><h3>Context and methodology</h3><ul><li>What is the research domain or project in which this dataset was created?</li><li>Which purpose does this dataset serve?</li><li>How was this dataset created?</li></ul><h3>Technical details</h3><ul><li>What is the structure of this dataset? Do the folders and files follow a certain naming convention?</li><li>Is any specific software required to open and work with this dataset?</li><li>Are there any additional resources available regarding the dataset, e.g. documentation, source code, etc.?</li></ul><h3>Further details</h3><ul><li>Is there anything else that other people may need to know when they want to reuse the dataset?</li></ul>
Data for Figures in Publication "Lanthanum nickel titanate perovskites as model systems for Ni-perovskite interfacial engineering in methane dry reforming"
No full text<h2>A primer on your dataset's description (to be edited)</h2><p>The influence of proper documentation on the reusability for research data should not be underestimated!<br>In order to help others understand how to interpret and reuse your data, we provide you with a few questions to help you structure your dataset's description (though please don't feel obligated to stick to them):</p><h3>Context and methodology</h3><ul><li>What is the research domain or project in which this dataset was created?</li><li>Which purpose does this dataset serve?</li><li>How was this dataset created?</li></ul><h3>Technical details</h3><ul><li>What is the structure of this dataset? Do the folders and files follow a certain naming convention?</li><li>Is any specific software required to open and work with this dataset?</li><li>Are there any additional resources available regarding the dataset, e.g. documentation, source code, etc.?</li></ul><h3>Further details</h3><ul><li>Is there anything else that other people may need to know when they want to reuse the dataset?</li></ul>
Novel, selective k-opioid receptor agonists determined by virtual screening and their pharmacology
No full text<p><span>Modulating the GPCR k-opioid receptor is a promising strategy for treating various human diseases, with agonists as potentially safer pain medications than conventional µ-opioid analgesics, as k-opioid receptor activation does not produce respiratory depression or risk of overdose.</span></p>
<p><span>applying structure-based virtual screening using 3D pharmacophore models based on the binding mode of the natural k-opioid agonist Salvinorin A, two compounds, SalA-VS-07 and SalA-VS-08 were discovered as novel, nonbasic, potent, and selective k-opioid agonists with a G protein-biased agonist profile. Two 3D pharmacophore-based virtual screening methods were performed in parallel and searched both natural product libraries and synthetic compound libraries. Compounds selected from the virtual screening campaign were experimentally evaluated in vitro for binding at the k-opioid receptor. Radioligand competitive binding and [35S]GTP</span><span>γ</span><span>S binding assays established SalA-VS-07 and SalA-VS-08 having affinity and potency at the k-opioid receptor in the nanomolar range, with SalA-VS-07 as a partial agonist and SalA-VS-08 as a full agonist at the k-opioid receptor, as well as their selectivity for the k-opioid receptor. In vitro studies on functional properties of Sal-VS-07 and Sal-VS-08 to recruit </span><span>β</span><span>-arrestin2 at the k-opioid receptor in the PathHunter </span><span>β</span><span>-arrestin2 recruitment assay demonstrated the lack of Sal-VS-07 and Sal-VS-08 to induced </span><span>β</span><span>-arrestin2 recruitment after receptor activation, in contrary to the high potency and efficacy of prototypical k-opioid agonists Salvinorin A and U69,593, profiling <a name="_Hlk204520480"></a>Sal-VS-07 and Sal-VS-08 as G protein-biased k-opioid agonists. </span></p>
<p><span>The dataset includes experimental in vitro pharmacological data on Sal-VS-07 and Sal-VS-08 as novel, selective k-opioid receptor agonists. </span></p>
Pharmacology of a k-opioid receptor ligand with a new chemotype
No full text<p><span>Modulating the GPCR k-opioid receptor is a promising strategy for treating various human diseases, where receptor activation shows potential for treating pain without causing respiratory depression or risk of overdose of conventional µ-opioid analgesics, and receptor antagonism is associated with beneficial effects for the treatment of mood and psychiatric diseases, and drug addictive disorders.</span></p>
<p><span><span>Combining experimental pharmacology (binding and functional in vitro assays, and behavioral nociceptive models) and computational chemistry (molecular docking, molecular dynamics simulations and dynamic pharmacophore (dynophore) generation, Compound A (4-[(2,3-dichlorophenyl)methylamino]-2-methylquinoline-8-carboxamide)) was identified as a novel k-opioid receptor antagonist, with a structurally distinct scaffold compared to the so far known k-opioid receptor ligands. Compound A selectively binds at the human k-opioid receptor and it shows k-opioid receptor antagonism in vitro and in vivo. The k-opioid receptor in vitro antagonism of Compound A was demonstrate based on the lack in inducing G protein activation upon ligand binding to the receptor expressed in CHO cells in the [35S]GTP</span><span>γ</span><span>S binding assay, in contrast to the high potency and stimulatory effect shown by the prototypical k-opioid agonist U69,593. Behavioral investigations in mice established the in vivo k-opioid receptor antagonist properties of Compound A after subcutaneous administration, based on its capability to effectively reverse the antinociceptive effects of the prototypical k-opioid agonist, U50,488, in two pain models, the writhing assay and the formalin test. Furthermore, Compound A shows favorable physicochemical features and a better capability to enter the CNS. </span></span></p>
<p><span>Compound A represents a valuable starting point for chemical optimization toward the development of innovative drugs targeting the k-opioid receptor as potential therapeutics for human conditions where the k-opioid system has a key function including mood, psychiatric and addictive disorders (antagonists), or pain conditions (agonists).</span></p>
<p><span>The dataset includes experimental in vitro and in vivo pharmacological data on Compound A, as a k-opioid receptor ligand with a new chemotype. </span></p>
NMR data - Methylation of Cytidine 1407 Increases the Lifetimes of the A-Site Ground and Excited States of E. coli 16S Ribosomal RNA
No full text<p>NMR data of publication:</p>
<h4>Methylation of Cytidine 1407 Increases the Lifetimes of the A-Site Ground and Excited States of <em>E. coli</em> 16S Ribosomal RNA</h4>
<div>Stefan Hilber, Alessandro Marotto, Christoph Mitteregger, Martin Tollinger, and Christoph Kreutz</div>
<div>Journal of the American Chemical Society <strong>2025</strong> <em>147</em> (30), 26097-26101</div>
<p>DOI: 10.1021/jacs.5c06523</p>
Glass transition in colloidal monolayers controlled by light-induced caging
No full text<h2>Glass transition in colloidal monolayers controlled by light-induced caging</h2>
<p>We theoretically investigate the glass-transition problem for a quasi-two-dimensional colloidal dense suspension modulated by a one-dimensional periodic external potential as imposed by interfering laser beams. Relying on a mode-coupling approach, we examine the nonequilibrium state diagram for hard disks as a function of the density and the period of the modulation for various potential strengths. The competition between the local packing and the distortion of the cages induced by the potential leads to a striking nonmonotonic behavior of the glass-transition line which allows melting of a glass state merely by changing the external fields. <br>In particular, we find regions in the non-equilibrium state diagram where a moderate periodic modulation stabilizes the liquid state. </p>
<h3>Context and Methodology</h3>
<ul>
<li>
<p><strong>Monte Carlo Simulation:</strong><br>The <code>SSF_MC.zip</code> archive contains code used to generate static structure factors (SSF) for various control parameters in modulated colloidal liquids.</p>
</li>
<li>
<p><strong>Mode-Coupling Theory (MCT) Calculation:</strong><br>The <code>MCT.zip</code> archive includes C code that solves the mode-coupling theory equations for the glass transition, using the SSF as input.</p>
</li>
<li>
<p><strong>Plotting Scripts:</strong><br>Python Jupyter notebooks are provided for generating the figures.</p>
</li>
</ul>
<p><strong>For theoretical details, please refer to the published papers:</strong></p>
<p><strong><a title="Mode-coupling theory of the glass transition for a liquid in a periodic potential" href="https://journals.aps.org/pre/abstract/10.1103/ks5t-xtvd" target="_blank" rel="noopener">Mode-coupling theory of the glass transition for a liquid in a periodic potential</a></strong></p>
<p><strong><a title="Glass transition in colloidal monolayers controlled by light-induced caging" href="https://journals.aps.org/pre/abstract/10.1103/3bmx-ldr8" target="_blank" rel="noopener">Glass transition in colloidal monolayers controlled by light-induced caging</a></strong></p>
<h3>Steps to Generate the Data</h3>
<ol>
<li>
<p><strong>Run the Monte Carlo simulations</strong> to produce static structure factors (SSF) for multiple random seeds.<br>After running several simulations, compute the average SSF over all valid results.</p>
</li>
<li>
<p><strong>Run the MCT code</strong> using the averaged SSF along with the specified control parameters.<br>Instructions for compiling and running the MCT code are included in the <code>MCT</code> directory.</p>
</li>
<li>
<p><strong>Repeat Steps 1 and 2</strong> for all desired sets of control parameters.</p>
</li>
</ol>
<h3>How to Generate the Plots</h3>
<ul>
<li>
<p>Jupyter notebooks corresponding to each figure are provided.</p>
</li>
<li>
<p>The datasets required to reproduce the figures are contained in <code>Figures_Dataset.zip</code>.</p>
</li>
</ul>
Mode-coupling theory of the glass transition for a liquid in a periodic potential
No full text<h2>Mode-coupling theory of the glass transition for a liquid in a periodic potential</h2>
<p>We derive a microscopic theory for the structural dynamics in the vicinity of the glass transition for a liquid exposed to a one-dimensional periodic potential. The periodic potential breaks translational invariance, in particular, the density exhibits a periodic modulation. Using techniques familiar from solid-state theory, we define generalized intermediate scattering functions from fluctuating densities in wave-vector space. Exact equations of motion are derived within the Mori-Zwanzig projection operator formalism reflecting the residual lattice symmetries. Due to the lack of rotational symmetry it is necessary to split the currents into components parallel and perpendicular to the modulation. We provide a closure of the equations in terms of a mode-coupling approximation for the force kernel. The theory reflects the usual analytic properties of correlation functions and encodes all phenomena known for mode-coupling theories. We prove that the theory reduces to the conventional mode-coupling theory in the case of vanishing amplitude of the modulation.</p>
<p>The paper is published at Physical Review E and, it has open access here: <a title="To the journal page" href="https://journals.aps.org/pre/abstract/10.1103/ks5t-xtvd" target="_blank" rel="noopener">https://journals.aps.org/pre/abstract/10.1103/ks5t-xtvd</a></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>
Run-and-Tumble Particles Learning Chemotaxis
No full text<pre><span>Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment.</span></pre>
<pre><span>By adopting a machine learning approach, we aim to understand how this behavior arises.</span></pre>
<pre><span>We consider run-and-tumble agents able to tune the instantaneous probability of switching between the run and the tumble phase.</span></pre>
<pre><span>When such agents are navigating in an environment characterized by a concentration field pointing towards a circular target, we investigate how a chemotactic strategy may be learned starting from unbiased run-and-tumble dynamics.</span></pre>
<pre><span>Target detection is allowed only during the tumble phase, which qualifies our agents as truly intermittent searchers.</span></pre>
<pre><span>We compare the learning performances of agents that sense only the instantaneous concentration with those of two types of agents both having a short-term memory that allows them to perform temporal comparisons.</span></pre>
<pre><span>While all types of learning agents develop successful target-search policies, we demonstrate that those achieved by agents endowed with temporal comparison abilities are significantly more efficient, particularly when the initial distance from the target is large.</span></pre>
<pre><span>Finally, we also show that when an additional length scale is imposed, for example by fixing the initial distance to the target, the learning agents can leverage this information to further improve their efficiency in locating the target.</span></pre>
<p>Codes and scripts necessary to reproduce data contained in the manuscript</p>