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Data supporting: Experimental study and analytical method of reinforced UHPFRC-NC composite beams considering shrinkage
No full text<h2>Dataset for paper: Experimental study and analytical method of reinforced UHPFRC-NC composite beams considering shrinkage</h2>
<p>This dataset originates from an experimental study investigating the effect of shrinkage strain on the structural behavior of composite beams made of a reinforced ultra-high performance fiber-reinforced concrete (UHPFRC) bottom layer with normal strength concrete (NC) on top. Five composite beams were tested under four-point bending, varying the layer heights and reinforcement areas to examine the impact of the UHPFRC/NC ratio and reinforcement ratio on flexural performance.</p>
<p>Distributed fibre optical sensors (DFOS) were installed on the top side of the tensile reinforcement before casting to measure steel rebar deformations due to shrinkage restrained by the reinforcement and differing shrinkage behaviors of the two concrete layers. During loading, data from four load cells, five displacement transducers, and five strain gauges installed at the bottom of each tensile reinforcement were recorded, alongside continuous DFOS measurements.</p>
<p>This data collection is provided to support reuse in future research. For detailed information on the data files and variables, please refer to the included ReadMe.txt file.<br><br>The paper is expected to be published by the end of September at the latest.</p>
FORTE2020 - ConvoyFence - Field Test Demonstration Video
No full text<h2>Video description: </h2>
<p>This video demonstrates the performance and capabilities of a developed vibration isolation platform developed to enable the mobile operation of optical drone detection and tracking systems. It shows the automated, vibration-isolated tracking of a small drone using a telescope system mounted to a vehicle driving on a dirt road at 40 km/h. The video includes the view through the optical system (top right), a view of the road ahead (bottom right), a close-up perspective of the stabilized platform with reference to the top of the vehicle (bottom left) and a view of the optical system including its motorized mount (top left).</p>
<h3>Funding:</h3>
<p><span lang="EN-US">This project has been funded by the Austrian defense research programme FORTE of the Federal Ministry of Finance (BMF).</span></p>
pschissen perg vnd leckh die tall [Zwischen berg und tiefen thal] (A-Wn_Mus.Hs._18688_n51) Audio recording
No full text<h1>Audio recording of a lute piece from the E-LAUTE project</h1><h2>Overview</h2><p>This dataset contains an audio recording of the piece "pschissen perg vnd leckh die tall [Zwischen berg und tiefen thal]", a 16th century lute music piece originally notated in lute tablature, created as part of the E-LAUTE project (<a href="https://e-laute.info/">https://e-laute.info/</a>). The recording preserves and makes historical lute music from the German-speaking regions during 1450-1550 accessible.</p><p>The recording is based on the work with the title "pschissen perg vnd leckh die tall [Zwischen berg und tiefen thal]" and the id "A-Wn_Mus.Hs._18688_n51" in the e-lautedb. It is found on the page(s) or folio(s) 29r in the source "[Lautentabulatur des Stephan Craus]" with the source-id "A-Wn_Mus.Hs._18688".</p><p>The original source and multiple transcriptions of the work can be found on the E-LAUTE platform: <a href="https://edition.onb.ac.at/fedora/objects/o:lau.A-Wn_Mus.Hs._18688/methods/sdef:TEI/get?mode=n51" target="_blank">https://edition.onb.ac.at/fedora/objects/o:lau.A-Wn_Mus.Hs._18688/methods/sdef:TEI/get?mode=n51</a>.</p><p>Links to the source: <a href="http://data.onb.ac.at/rec/AC14316391" target="_blank">http://data.onb.ac.at/rec/AC14316391</a>, <a href="https://rism.online/sources/600141880" target="_blank">https://rism.online/sources/600141880</a>, .</p><h2>Dataset Contents</h2><p>This dataset includes:</p><ul><li><strong>Audio file</strong>: An audio recording of the lute piece in .wav format</li> <li><strong>Metadata file</strong>: A metadata file with detailed information about the recording in .json format</li></ul><h2>About the E-LAUTE Project</h2><p><strong>E-LAUTE: Electronic Linked Annotated Unified Tablature Edition - The Lute in the German-Speaking Area 1450-1550</strong></p><p>The E-LAUTE project creates innovative digital editions of lute tablatures from the German-speaking area between 1450 and 1550. This interdisciplinary "open knowledge platform" combines musicology, music practice, music informatics, and literary studies to transform traditional editions into collaborative research spaces.</p><p>For more information, visit the project website: <a href="https://e-laute.info/">https://e-laute.info/</a></p>
Ich bin ir langezeyt hold gewesen (Jud_1523-2_n12) Audio recording
No full text<h1>Audio recording of a lute piece from the E-LAUTE project</h1><h2>Overview</h2><p>This dataset contains an audio recording of the piece "Ich bin ir langezeyt hold gewesen", a 16th century lute music piece originally notated in lute tablature, created as part of the E-LAUTE project (<a href="https://e-laute.info/">https://e-laute.info/</a>). The recording preserves and makes historical lute music from the German-speaking regions during 1450-1550 accessible.</p><p>The recording is based on the work with the title "Ich bin ir langezeyt hold gewesen" and the id "Jud_1523-2_n12" in the e-lautedb. It is found on the page(s) or folio(s) 18v-19r in the source "1.5.2.3. Ain schone kunstliche vnderweisung" with the source-id "Jud_1523-2".</p><p>The original source and multiple transcriptions of the work can be found on the E-LAUTE platform: <a href="https://edition.onb.ac.at/fedora/objects/o:lau.Jud_1523-2/methods/sdef:TEI/get?mode=n12" target="_blank">https://edition.onb.ac.at/fedora/objects/o:lau.Jud_1523-2/methods/sdef:TEI/get?mode=n12</a>.</p><p>Links to the source: <a href="http://data.onb.ac.at/rec/AC09185338" target="_blank">http://data.onb.ac.at/rec/AC09185338</a>, <a href="https://opac.rism.info/rism/Record/rism990032736" target="_blank">https://opac.rism.info/rism/Record/rism990032736</a>, <a href="https://gateway-bayern.de/VD16+J+1031" target="_blank">https://gateway-bayern.de/VD16+J+1031</a>, .</p><h2>Dataset Contents</h2><p>This dataset includes:</p><ul><li><strong>Audio file</strong>: An audio recording of the lute piece in .wav format</li> <li><strong>Metadata file</strong>: A metadata file with detailed information about the recording in .json format</li></ul><h2>About the E-LAUTE Project</h2><p><strong>E-LAUTE: Electronic Linked Annotated Unified Tablature Edition - The Lute in the German-Speaking Area 1450-1550</strong></p><p>The E-LAUTE project creates innovative digital editions of lute tablatures from the German-speaking area between 1450 and 1550. This interdisciplinary "open knowledge platform" combines musicology, music practice, music informatics, and literary studies to transform traditional editions into collaborative research spaces.</p><p>For more information, visit the project website: <a href="https://e-laute.info/">https://e-laute.info/</a></p>
[Nach Willen Dein], postlúdiol[o] (A-Wn_Mus.Hs._18688_n16, A-Wn_Mus.Hs._18688_n17) Audio recording
No full text<h1>Audio recording of a lute piece from the E-LAUTE project</h1><h2>Overview</h2><p>This dataset contains an audio recording of the piece "[Nach Willen Dein], postlúdiol[o]", a 16th century lute music piece originally notated in lute tablature, created as part of the E-LAUTE project (<a href="https://e-laute.info/">https://e-laute.info/</a>). The recording preserves and makes historical lute music from the German-speaking regions during 1450-1550 accessible.</p><p>The recording is based on the work with the title "[Nach Willen Dein], postlúdiol[o]" and the id "A-Wn_Mus.Hs._18688_n16, A-Wn_Mus.Hs._18688_n17" in the e-lautedb. It is found on the page(s) or folio(s) 11v-12r, 12r_7 in the source "[Lautentabulatur des Stephan Craus]" with the source-id "A-Wn_Mus.Hs._18688".</p><p>The original source and multiple transcriptions of the work can be found on the E-LAUTE platform: <a href="https://edition.onb.ac.at/fedora/objects/o:lau.A-Wn_Mus.Hs._18688/methods/sdef:TEI/get?mode=n17" target="_blank">https://edition.onb.ac.at/fedora/objects/o:lau.A-Wn_Mus.Hs._18688/methods/sdef:TEI/get?mode=n17</a>.</p><p>Links to the source: <a href="http://data.onb.ac.at/rec/AC14316391" target="_blank">http://data.onb.ac.at/rec/AC14316391</a>, <a href="https://rism.online/sources/600141880" target="_blank">https://rism.online/sources/600141880</a>, .</p><h2>Dataset Contents</h2><p>This dataset includes:</p><ul><li><strong>Audio file</strong>: An audio recording of the lute piece in .wav format</li> <li><strong>Metadata file</strong>: A metadata file with detailed information about the recording in .json format</li></ul><h2>About the E-LAUTE Project</h2><p><strong>E-LAUTE: Electronic Linked Annotated Unified Tablature Edition - The Lute in the German-Speaking Area 1450-1550</strong></p><p>The E-LAUTE project creates innovative digital editions of lute tablatures from the German-speaking area between 1450 and 1550. This interdisciplinary "open knowledge platform" combines musicology, music practice, music informatics, and literary studies to transform traditional editions into collaborative research spaces.</p><p>For more information, visit the project website: <a href="https://e-laute.info/">https://e-laute.info/</a></p>
Data from PhD thesis by Anna Schmidbauer: "Bio-interactive hydrogels for regenerative medicine"
No full text<h3>Context and methodology</h3>
<p>This data was collected within the framework of the PhD thesis by Anna Schmidbauer. The thesis has been published in TU Wien's reposiTUm as referenced below. The data is currently restricted as it also contains data which might be subject to further exploitation; however, it will be published in separate, public entries as soon as possible. The thesis aimed for the development of biointeractive hydrogels, which potentially improve the integration of artificial 3D-printed bone scaffolds. The central concept involved<br>incorporating growth factors or bioactive molecules into these scaffolds to potentially boost bone regeneration and enhance osseointegration. Therefore, bio-based materials were chosen as starting materials due for their potential to closely mimic natural bone characteristics. The first part of this work focused on hydrogels derived from decellularized bone lysate (BL), leveraging the effectiveness of homologous tissues over heterologous scaffolds. The second part aimed for hydrogels based on human platelet lysate (PL) as the main building block, known for its remarkable regenerative potential regarding biointegration and wound healing. </p>
<h3>Technical details</h3>
<p>The data set is organized in several files/folders as described below.</p>
<p><strong>PDF file "Laborjournal_Schmidbauer"</strong> contains a full scan of the lab notebook.</p>
<p><strong>XLSX file "substances_thesis_Anna Schmidbauer"</strong> contains the substances used and synthesized during the thesis.</p>
<p><strong>XLSX file "raw_data_figures"</strong> contains data sets of all figures from the thesis.</p>
<p><strong>PPTX and OPJU files "Figures_thesis_Anna Schmidbauer"</strong> contain processed/visualized data.</p>
<p><strong>Folder "Raw Data"</strong> contains the following subfolders (as ZIP) organized by the methodologies used for acquisition. (in alphabetical order)</p>
<ul>
<li>"<em>DLS data</em>" contains CSV data from dynamic light scattering measurements of the used platelet lysate (PL) sample</li>
<li>"<em>In vitro data</em>" contains CSV data from measurements of "<em>BSA assay</em>", "<em>degradation</em>", "<em>rheology</em>", and CZI files (Zeiss microscopy image raw files) and TIF renders of LSM (laser scanning microscopy) of cells encapsulated in hydrogels after 1, 3 and 9 days in "<em>LSM images</em>" with measured fluorescence intensity in the XLSX file "Fluorescence".</li>
<li>"<em>In vivo data</em>" contains JPG renders (with added scale bars), raw JPG and VSI files (Olympus microscopy image raw files) of histological examinations in "<em>Histo</em>", macroscopic images of test subjects at different time points as JPG in "<em>subject images</em>", and compilations of the two different hydrogel types (PLAGE and PLGMA) as PPTX files</li>
<li>"<em>MALDI data</em>" contains Matrix Assisted Laser Desorption Ionisation mass spec data as CSV and compiled as OPJU file</li>
<li>"<em>NMR data</em>" contains raw NMR data of substances referenced in the PDF file "Laborjournal_Schmidbauer".</li>
<li>"<em>Photorheology data</em>" contains photorheological measurements of hydrogel formulations, based on precursors as indicated in the folder name as CSV and XLSX, respectively.</li>
<li>"<em>SDS PAGE</em>" contains scans of electrophoresis experiments as SCN files (BioRad scan raw file) and PNG/JPG/TIF render files, Bradford assay evaluations as XLSX files, and a results compilation as PPTX file.</li>
</ul>
<p><strong>File types:</strong></p>
<ul>
<li>
<p><strong><code>opju</code></strong> (OriginLab Origin project): created with OriginPro 2023b (64-bit) SR1 10.0.5.157 (Lehre) </p>
</li>
<li>
<p><strong><code>.RhPrj</code></strong> (Anton Paar RheoCompass, shown as ZIP internally): processable with Anton Paar RheoCompass Software</p>
</li>
<li>
<p><strong><code>.czi</code></strong> (Zeiss microscopy images): processable with ZEISS ZEN lite software</p>
</li>
<li>
<p><strong><code>.vsi</code></strong> (Olympus microscopy images): processable with Olympus Stream Software</p>
</li>
<li>
<p><strong><code>.scn</code></strong> (BioRad electrophoresis scans): processable with BioRad Image Lab Software </p>
</li>
<li>
<p><strong>Bruker NMR raw data directories</strong> (<code>pdata</code>, <code>acqu</code>, <code>shimvalues</code>, etc.): processable with MestreNova or Bruker TopSpin</p>
</li>
</ul>
Dataset supporting publication: "Remote-sensing based control of 3D magnetic fields using machine learning for in-operando applications".
No full text<h2>About the dataset</h2>
<p>This dataset supports a study where precise 3D magnetic field control is achieved using a hexapole electromagnet system combined with a multi-layer perceptron neural network. The work demonstrates how the neural network enables to calibrate non-linear field responses when direct measurements at the position of interest are not feasible.</p>
<p>The dataset includes code and processed data for reproducing the figures from the associated paper, and is intended to support further research in in-operando experiments that require high-precision field control. For more information about the code and data, please refer to the <code>readme.txt</code> file.</p>
<p>The published paper can be found here: <a href="https://doi.org/10.48550/arXiv.2411.10374">https://doi.org/10.48550/arXiv.2411.10374</a></p>
<h2>Requirements</h2>
<p>The code was executed with Python 3.12, the dependencies are listed in <code>requirements.txt</code>.</p>
<h2>Licenses</h2>
<p>The data is licensed under CC-BY, the code is licensed under MIT.</p>
Privacy-Sensitive Conversations between Care Workers and Care Home Residents in a Residential Care Home
No full text<div>
<h1>Dataset Card for "privacy-care-interactions"</h1>
</div>
<div>
<h2>Table of Contents</h2>
</div>
<div>
<ul>
<li><a target="_blank" rel="noopener nofollow">Dataset Description</a>
<ul>
<li><a target="_blank" rel="noopener nofollow">Purpose and Features</a></li>
<li><a target="_blank" rel="noopener nofollow">Dataset Overview</a></li>
<li><a target="_blank" rel="noopener nofollow">Language Distribution</a></li>
<li><a target="_blank" rel="noopener nofollow">Locale Distribution</a></li>
<li><a target="_blank" rel="noopener nofollow">Key Facts</a></li>
</ul>
</li>
<li><a target="_blank" rel="noopener nofollow">Dataset Structure</a>
<ul>
<li><a target="_blank" rel="noopener nofollow">Data Instances</a></li>
<li><a target="_blank" rel="noopener nofollow">Data Fields</a></li>
<li><a target="_blank" rel="noopener nofollow">Data Splits</a></li>
</ul>
</li>
<li><a target="_blank" rel="noopener nofollow">Dataset Creation</a>
<ul>
<li><a target="_blank" rel="noopener nofollow">Curation Rationale</a></li>
<li><a target="_blank" rel="noopener nofollow">Source Data</a></li>
<li><a target="_blank" rel="noopener nofollow">Annotations</a></li>
<li><a target="_blank" rel="noopener nofollow">Personal and Sensitive Information</a></li>
</ul>
</li>
<li><a target="_blank" rel="noopener nofollow">Considerations for Using the Data</a>
<ul>
<li><a target="_blank" rel="noopener nofollow">Social Impact of Dataset</a></li>
<li><a target="_blank" rel="noopener nofollow">Discussion of Biases</a></li>
<li><a target="_blank" rel="noopener nofollow">Other Known Limitations</a></li>
</ul>
</li>
<li><a target="_blank" rel="noopener nofollow">Additional Information</a>
<ul>
<li><a target="_blank" rel="noopener nofollow">Dataset Curators</a></li>
<li><a target="_blank" rel="noopener nofollow">Licensing Information</a></li>
<li><a target="_blank" rel="noopener nofollow">Citation Information</a></li>
<li><a target="_blank" rel="noopener nofollow">Contributions</a></li>
</ul>
</li>
</ul>
</div>
<div>
<h2>Dataset Description</h2>
</div>
<div>
<h3>Purpose and Features</h3>
</div>
<div>
<p> Collection of Privacy-Sensitive Conversations between Care Workers and Care Home Residents in an Residential Care Home </p>
</div>
<div>
<p>The dataset is useful to train and evaluate models to identify and classify privacy-sensitive parts of conversations from text, especially in the context of AI assistants and LLMs.</p>
</div>
<div>
<h3>Dataset Overview</h3>
</div>
<div>
<ul>
<li><strong>Total entries:</strong> 95</li>
<li><strong>Number of distinct taxonomy categories in the public dataset:</strong> 4</li>
<li><strong>Number of distinct conversational categories in public dataset:</strong> 7</li>
<li><strong>Papers:</strong>
<ul>
<li><strong>Continues the work of:</strong> <a href="https://doi.org/10.34726/5960" target="_blank" rel="noopener nofollow">Privacy Agents: Utilizing Large Language Models to Safeguard Contextual Integrity in Elderly Care</a></li>
<li><strong>Continues the work of:</strong> <a href="https://doi.org/10.34726/6399" target="_blank" rel="noopener nofollow">Prototype of a care documentation support system using audio recordings of care actions and large language models</a></li>
</ul>
</li>
</ul>
</div>
<div>
<h3>Language Distribution </h3>
</div>
<div>
<ul>
<li>English (en): 95</li>
</ul>
</div>
<div>
<h3>Locale Distribution </h3>
</div>
<div>
<ul>
<li>United States (US) : 95</li>
</ul>
</div>
<div>
<h3>Key Facts </h3>
</div>
<div>
<ul>
<li>This is synthetic data! Generated using proprietary algorithms - no privacy violations!</li>
<li>Conversations are classified following the taxonomy for privacy-sensitive robotics by <a href="https://doi.org/10.48550/arXiv.1701.00841" target="_blank" rel="noopener nofollow">Rueben et al. (2017)</a>.</li>
<li>The data was manually labeled by an expert.</li>
</ul>
</div>
<div>
<h2>Dataset Structure</h2>
</div>
<div>
<h3>Data Instances</h3>
</div>
<div>
<p>The provided data format is <code>.jsonl</code>, the JSON Lines text format, also called newline-delimited JSON. An example entry looks as follows.</p>
</div>
<div>
<pre><code>{ "text": "CW: Have you ever been to Italy? CR: Oh, yes... many years ago.", "taxonomy": 0, "category": 0, "affected_speaker": 1, "language": "en", "locale": "US", "data_type": 1, "uid": 16, "split": "train" }</code></pre>
</div>
<div>
<h3>Data Fields</h3>
</div>
<div>
<p>The data fields are:</p>
</div>
<div>
<ul>
<li><code>text</code>: a <code>string</code> feature. The abbreviaton of the speakers refer to the care worker (CW) and the care recipient (CR).</li>
<li><code>taxonomy</code>: a classification label, with possible values including <code>informational</code> (0), <code>invasion</code> (1), <code>collection</code> (2), <code>processing</code> (3), <code>dissemination</code> (4), <code>physical</code> (5), <code>personal-space</code> (6), <code>territoriality</code> (7), <code>intrusion</code> (8), <code>obtrusion</code> (9), <code>contamination</code> (10), <code>modesty</code> (11), <code>psychological</code> (12), <code>interrogation</code> (13), <code>psychological-distance</code> (14), <code>social</code> (15), <code>association</code> (16), <code>crowding-isolation</code> (17), <code>public-gaze</code> (18), <code>solitude</code> (19), <code>intimacy</code> (20), <code>anonymity</code> (21), <code>reserve</code> (22). The taxonomy is derived from <a href="https://doi.org/10.48550/arXiv.1701.00841" target="_blank" rel="noopener nofollow">Rueben et al. (2017)</a>. The classifications were manually labeled by an expert.</li>
<li><code>category</code>: a classification label, with possible values including <code>personal-information</code> (0), <code>family</code> (1), <code>health</code> (2), <code>thoughts</code> (3), <code>values</code> (4), <code>acquaintance</code> (5), <code>appointment</code> (6). The privacy category affected in the conversation. The classifications were manually labeled by an expert.</li>
<li><code>affected_speaker</code>: a classification label, with possible values including <code>care-worker</code> (0), <code>care-recipient</code> (1), <code>other</code> (2), <code>both</code> (3). The speaker whose privacy is impacted during the conversation. The classifications were manually labeled by an expert.</li>
<li><code>language</code>: a <code>string</code> feature. Language code as defined by ISO 639.</li>
<li><code>locale</code>: a <code>string</code> feature. Regional code as defined by ISO 3166-1 alpha-2.</li>
<li><code>data_type</code>: a <code>string</code> a classification label, with possible values including <code>real</code> (0), <code>synthetic</code> (1).</li>
<li><code>uid</code>: a <code>int64</code> feature. A unique identifier within the dataset.</li>
<li><code>split</code>: a <code>string</code> feature. Either <code>train</code>, <code>validation</code> or <code>test</code>.</li>
</ul>
</div>
<div>
<h3>Dataset Splits</h3>
</div>
<div>
<p>The dataset has 2 subsets:</p>
</div>
<div>
<ul>
<li><code>split</code>: with a total of 95 examples split into <code>train</code>, <code>validation</code> and <code>test</code> (70%-15%-15%)</li>
<li><code>unsplit</code>: with a total of 95 examples in a single train split</li>
</ul>
</div>
<div>
<table>
<tbody>
<tr>
<th>name</th>
<th>train</th>
<th>validation</th>
<th>test</th>
</tr>
</tbody>
<tbody>
<tr>
<td>split</td>
<td>66</td>
<td>14</td>
<td>15</td>
</tr>
<tr>
<td>unsplit</td>
<td>95</td>
<td>n/a</td>
<td>n/a</td>
</tr>
</tbody>
</table>
</div>
<div>
<p>The files follow the naming convention <code>subset-split-language.jsonl</code>. The following files are contained in the dataset:</p>
</div>
<div>
<ul>
<li><code>split-train-en.jsonl</code></li>
<li><code>split-validation-en.jsonl</code></li>
<li><code>split-test-en.jsonl</code></li>
<li><code>unsplit-train-en.jsonl</code></li>
</ul>
</div>
<div>
<h2>Dataset Creation</h2>
</div>
<div>
<h3>Curation Rationale</h3>
</div>
<div>
<p>Recording audio of care workers and residents during care interactions, which includes partial and full body washing, giving of medication, as well as wound care, is a highly privacy-sensitive use case. Therefore, a dataset is created, which includes privacy-sensitive parts of conversations, synthesized from real-world data. This dataset serves as a basis for fine-tuning a local LLM to highlight and classify privacy-sensitive sections of transcripts created in care interactions, to further mask them to protect privacy.</p>
</div>
<div>
<h3>Source Data</h3>
</div>
<div>
<h4>Initial Data Collection</h4>
</div>
<div>
<p>The intial data was collected in the project Caring Robots of TU Wien in cooperation with Caritas Wien. One project track aims to facilitate Large Languge Models (LLM) to support documentation of care workers, with LLM-generated summaries of audio recordings of interactions between care workers and care home residents. The initial data are the transcriptions of those care interactions.</p>
</div>
<div>
<h4>Data Processing</h4>
</div>
<div>
<p>The transcriptions were thoroughly reviewed, and sections containing privacy-sensitive information were identified and marked using qualitative data analysis software by two experts. Subsequently, the sections were translated from German to U.S. English using the locally executed LLM <a href="https://ollama.com/icky/translate" target="_blank" rel="noopener nofollow">icky/translate</a>. In the next step, another <a href="https://huggingface.co/meta-llama/Llama-3.1-70B" target="_blank" rel="noopener nofollow">llama3.1:70b</a> was used locally to synthesize the conversation segments. This process involved generating similar, yet distinct and new, conversations that are not linked to the original data. The dataset was split using the <code>train_test_split</code> function from the <a href="https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.train_test_split.html" target="_blank" rel="noopener nofollow">Scikit-learn</a> library.</p>
</div>
<div>
<h4>Who are the source language producers?</h4>
</div>
<div>
<p>Care workers and care recipients in a residential care home in a German-speaking country.</p>
</div>
<div>
<h3>Annotations</h3>
</div>
<div>
<h4>Annotation process</h4>
</div>
<div>
<p>Each entry in the synthetic data was read by an expert and labeled according to the various classifications.</p>
</div>
<div>
<h4>Who are the annotators?</h4>
</div>
<div>
<p><a href="https://orcid.org/0000-0002-0227-7906" target="_blank" rel="noopener nofollow">Reinhard Grabler</a> and <a href="https://orcid.org/0009-0008-6522-8344">Michael Starzinger</a></p>
</div>
<div>
<h3>Personal and Sensitive Information</h3>
</div>
<div>
<p>As this is completely synthetic data, there is no personal or sensitive information in this data set.</p>
</div>
<div>
<h2>Considerations for Using the Data</h2>
</div>
<div>
<h3>Social Impact of Dataset</h3>
</div>
<div>
<p>The ability to automatically detect privacy-sensitive parts of a conversation is a versatile use case that extends beyond care interactions. This technology has broad applications, such as in human-robot interactions and other scenarios involving active speech processing. Its implementation can significantly enhance privacy across these diverse applications.</p>
</div>
<div>
<h3>Discussion of Biases</h3>
</div>
<div>
<p>The source data comes from a residential care home in a German-speaking country. The classification was also carried out by an expert in a German-speaking country. Therefore, no cultural differences in the perception of privacy are reflected in this data set.</p>
</div>
<div>
<h3>Other Known Limitations</h3>
</div>
<div>
<p>The collection of original data for synthesis is a highly time-consuming process. Recordings are conducted by care workers amidst their already demanding day-to-day care responsibilities. Additionally, complete transcripts must be manually reviewed to identify and redact privacy-sensitive information. Consequently, the current volume of data is limited. Ongoing efforts aim to expand the master dataset as additional phases of the study are conducted. Furthermore, research is underway to explore and develop improved methods for generating high-quality synthetic data tailored to this use case.</p>
</div>
<div>
<h2>Additional Information</h2>
</div>
<div>
<h3>Dataset Curators</h3>
</div>
<div>
<p><a href="https://orcid.org/0000-0002-0227-7906" target="_blank" rel="noopener nofollow">Reinhard Grabler</a></p>
</div>
<div>
<h3>Licensing Information</h3>
</div>
<div>
<p><strong>Creative Commons Attribution 4.0 International</strong> (CC BY 4.0)</p>
</div>
<div>
<ul>
<li>The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank" rel="noopener nofollow">Read more</a></li>
</ul>
</div>
<div>
<h3>Citation Information</h3>
</div>
<div>
<p>If you use this dataset, please cite it as indicated.</p>
</div>
<div>
<h3>Contributions</h3>
</div>
<div>
<p>Special thanks to Muhammad Saleemi, who assisted with the editing of the initial transcripts, by adding speaker diarization and performing corrections. </p>
</div>
Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport [Data and Code]
No full text<h2>How to cite?</h2>
<p><a href="https://doi.org/10.5194/agile-giss-6-2-2025">Canestrini, M., & Giannopoulos, I. (2025). Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport. AGILE: GIScience Series 6.</a></p>
<h2>Abstract</h2>
<p>The concept of the "15-minute city" has recently attracted notable attention and is being widely discussed in urban planning and policymaking. The original idea focuses solely on active modes, thus walking and biking, without considering the role of public transport, which is, in fact, essential for accessing amenities of daily needs in urban areas. Additionally, most studies exploring this concept model walking and biking with constant average speeds. While this simplification is considered reasonable in flat urban environments, it may result in inaccurate estimations for cities on more hilly terrain. This study aims to address these two drawbacks by integrating public transport into the 15-minute concept and incorporating speed as a function of street inclination. The results for the case study of Vienna indicate only small differences in average accessibility when modelling walking speed in a slope-dependent manner. In contrast, for biking the difference is notable. Secondly, incorporating public transport as a valid mode option decreases the average duration to access all daily needs from 23.30 minutes (walking only) to 16.88 minutes and the median duration from 15.07 minutes to 13.28 minutes. The main finding of this work is that adding public transport extends the 15-minute city area rather than optimizing travel times within the existing walkable area. Furthermore, the presented analyses provide the means to uncover categories that limit the area of the 15-minute city.</p>
<h2>How to use?</h2>
<p>The provided material includes data and scripts which were used for the analysis in the paper entitled "<strong>Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport</strong>", accepted for AGILE Conference 2025 (Association of Geographic Information Laboratories in Europe).</p>
<p>It comprises three folders within the zip file:</p>
<ol>
<li><strong>code</strong>: Includes script files essential for conducting the analysis. The scripts are written in Python.</li>
<li><strong>data</strong>: Contains the datasets for the analysis.</li>
<li><strong>results</strong>: Includes the outcomes showcased in the associated paper.</li>
</ol>
<h2>Further information</h2>
<p>Programming Language: Python (3.10 tested)</p>
<p>For reproducibility read the <code>README.txt</code>; for necessary libraries refer to the <code>requirements.txt</code>. Both files are included in the zip folder.</p>
<p>All data files are licensed under CC BY 4.0, all software files are licensed under MIT License.</p>
Synergistic Effect of Ligand-Cluster Structure and Support in Gold Nanocluster Catalysts for Selective Hydrogenation of Alkynes
No full text<h2>Data for the research article titled: "Synergistic Effect of Ligand-Cluster Structure and Support in Gold Nanocluster Catalysts for Selective Hydrogenation of Alkynes"</h2>
<p>Data used in the <a href="https://doi.org/10.1039/D4NR03865G">research article</a> and its supporting information. The paper was published in the Nanoscale journal by RSC.</p>
<h3>Authors</h3>
<p>Rares, Banu, Adea Loxha, Nicole Müller, Stylianos Spyroglou, Egon Erwin Rosenberg, A.<br>Eduardo Palomares, Fernando Rey, Carlo Marini, Noelia Barrabés∗</p>
<h3>Funding</h3>
<p>FWF via grant Elise Richter (V831-N); by the Spanish National Research Council for the project i-LINK 2023(ILINK23067) and the COST Action CA21101 COSY. We thank ALBA synchrotron for the beamtime. </p>
<h3>Context and methodology</h3>
<ul>
<li>In this article we report on the use of supported gold nanoclusters in the selective hydrogenation of alkynes to alkenes and investigate the support and pretreatment effect.</li>
<li>The dataset serves as a public resource and contains the data used in the manuscript and its supporting information.</li>
<li>The data was acquired by using different analytical methods, including various spectroscopies, and chromatographies.</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>The files uploaded contain the measured XAFS, gas-chromatography, XRD, UV-Vis and MALDI-MS.</li>
<li>The OPJU files are graphs and can be opened with the <a href="https://en.wikipedia.org/wiki/Origin_(data_analysis_software)">Origin</a> software</li>
</ul>