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470 results for “Evaluation dataset”
Sea Level Datasets for HIDRA2 Training and Evaluation
<p>HIDRA2 is a state-of-the-art deep neural model for sea-level prediction based on past sea level observations and future tidal and atmospheric forecasts. Training and evaluation was performed on the published sea level datasets.</p>
Child 3D anthropometry evaluation datasets
<p>Background: An efficacy evaluation of the AutoAnthro system to measure child (0–59 months) anthropometry in USA found three-dimensional imaging performed as well as gold-standard manual measurements for biological plausibility and precision.</p> <p>Objectives: Conduct an effectiveness evaluation of the accuracy of the AutoAnthro system to measure 0–59 months child anthropometry in population-based surveys and surveillance systems in households in Guatemala and Kenya, and in hospitals in China.</p> <p>Methods: The evaluation was done using health or nutrition surveillance system platforms among 600 children 0–59 months (Guatemala, Kenya) and 300 children 0-23 months (China). Field team anthropometrists and their assistants collected from each child manual and scan anthropometric measurements including length/height, mid-upper arm circumference (MUAC), and head circumference (HC, China only). An anthropometry expert and assistant later collected both manual and scan anthropometric measurements on the same child. The expert manual measurements were considered the standard compared to field team scans.</p> <p>Results: Overall, in Guatemala, Kenya and China, respectively, for inter-rater accuracy, average bias for length/height was -0.3 cm, -1.9 cm, -6.2 cm; for MUAC was 0.9 cm, 1.2 cm, -0.8 cm; for HC was 2.4 cm; the inter-technical error of measurement (TEM) for length/height was 2.8 cm, 3.4 cm, 5.5 cm; for MUAC was 1.1 cm, 1.5 cm, 1.0 cm; for HC was 2.8 cm. For intra-rater precision, absolute mean difference and intra-TEM were 0.1 cm for all countries for all manual measurements. For scan, overall, absolute mean difference ranged for length/height 0.4-0.6 cm; MUAC 0.1-0.1 cm; HC was 0.4 cm. For intra-TEM, length/height was 0.5 cm in Guatemala and China, 0.7 cm in Kenya, and other measurements were <0.3 cm.</p> <p>Conclusions: Understanding the factors that cause the many poor scan results and how to correct them will be needed prior to using this instrument in routine population-based survey and surveillance systems.</p>
Dataset II related to the publication: In silico Evaluation of the Thr58-associated Conserved Water with KRAS Switch-II Pocket Binders
<p>Desmond trajectories of simulations conducted with TIP3P water model related to the publication:</p> <p>Leini R, Pantsar T: In Silico Evaluation of the Thr58-Associated Conserved Water with 2 KRAS Switch-II Pocket Binders. J. Chem. Inf. Model. [accepted] https://doi.org/10.1021/acs.jcim.2c01479</p> <ul> <li>Individual .zip files contain the Desmond trajectories and -out.cms -files.</li> </ul> <p>Related datasets: 10.5281/zenodo.7656467 and 10.5281/zenodo.7342311</p>
Dataset III related to the publication: In silico Evaluation of the Thr58-associated Conserved Water with KRAS Switch-II Pocket Binders
<p>Desmond trajectories of simulations conducted with TIP4P water model related to the publication:</p> <p>Leini R, Pantsar T: In Silico Evaluation of the Thr58-Associated Conserved Water with 2 KRAS Switch-II Pocket Binders. J. Chem. Inf. Model. [accepted] https://doi.org/10.1021/acs.jcim.2c01479</p> <ul> <li>Individual .zip files contain the Desmond trajectories and -out.cms -files.</li> </ul> <p>Related datasets: 10.5281/zenodo.7656467 and 10.5281/zenodo.7341954</p>
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
<p>This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.<br><br><strong>Description</strong><br><br>The SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.<br>Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.</p> <p>To listen to audio samples of the dataset, please see <a href="https://innoetics.github.io/publications/somos-dataset/index.html">our Github page</a>.</p> <p>The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.</p> <p><em>This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.</em></p> <p><strong>Terms of use</strong></p> <ul> <li>The dataset may be used for <strong>research</strong> purposes only, for <strong>non-commercial</strong> purposes only, and may be distributed with the same terms.</li> <li>Every time you produce research that has used this dataset, please <strong>cite </strong>the dataset appropriately.</li> </ul> <p>Cite as:</p> <pre><code>@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} } </code></pre> <p><br><strong>References of resources & models used</strong></p> <p>Voice & synthesized texts:<br>K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.</p> <p>Vocoder:<br>J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.<br>R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.</p> <p>Acoustic models:<br>N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.<br>Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.<br>J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.<br>J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.<br>M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.<br>M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.<br>Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.<br>K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.<br>T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.</p> <p>Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:<br>M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.<br>V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.<br>A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.</p> <p><strong>Contact</strong></p> <p>Alexandra Vioni - a.vioni@samsung.com</p> <ul> <li>If you have any questions or comments about the dataset, please feel free to write to us.</li> <li>We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.</li> </ul>
Supplementary material for a usability evaluation of a semantic search for biological datasets
<p>We conducted a usability evaluation for a semantic dataset search with 20 biodiversity scholars in June and July 2022 in Germany.</p> <p>Following the TREC guidelines (https://www-nlpir.nist.gov/projects/t9i/spec.html), we setup eight user tasks and surveys with questionnaires to guide users through the evaluation. The zip file provides questionnaires, survey templates and the original results.</p> <p>A Jupyter notebook for data analysis is provided in our GitHub repository: <a href="https://github.com/fusion-jena/semantic-search-usability-analysis">https://github.com/fusion-jena/semantic-search-usability-analysis</a></p>
Dataset: Evaluation of post-hoc interpretability methods in time-series classification
<p>This repository contains the dataset, trained models as well as results for the article <em>Evaluation of post-hoc interpretability methods in time-series classification.</em></p> <p>The code to reproduce the results presented in the article is available on <a href="https://github.com/hturbe/InterpretTime">GitHub</a>. More details on the data and results can be found in the article.</p> <p><strong>Files:</strong></p> <ul> <li><strong>datasets.zip: </strong>Include the three datasets used in the article: <ul> <li><strong>ECG: </strong>Processed version of the CPSC dataset from <em>Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</em></li> <li><strong>fordA: </strong>Dataset from the <a href="https://www.cs.ucr.edu/~eamonn/time_series_data_2018/">UCR Time Series Classification Archive</a></li> <li><strong>synthetic: </strong>Synthetic dataset developed specifically for the purpose of the article</li> </ul> </li> <li><strong>trained_models.zip: </strong>Include CNN, transformer and bi-lstm trained on the three datasets</li> <li><strong>results_paper.zip: </strong>Computed relevance and evaluation metrics for the trained models <ul> <li><strong>model_interpretability: </strong>Include the relevance computed using the different interpretability methods as well as the computed metrics for each method </li> <li><strong>summary_results: </strong>Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.</li> </ul> </li> </ul>
DEepfake CROss-lingual evaluation dataset (DECRO)
<p>Deepfake cross-lingual evaluation dataset (DECRO) is constructed to evaluate the influence of language differences on deepfake detection. </p> <p><strong>If you use DECRO dataset for deepfake detection, please cite the paper "<em>Transferring Audio Deepfake Detection Capability across Languages</em>" published in <em>www'23</em>.</strong></p>
Dataset I related to the publication: In silico Evaluation of the Thr58-associated Conserved Water with KRAS Switch-II Pocket Binders
<p>WaterMaps results related to the publication:</p> <p>Leini R, Pantsar T: In Silico Evaluation of the Thr58-Associated Conserved Water with 2 KRAS Switch-II Pocket Binders. J. Chem. Inf. Model. [accepted] https://doi.org/10.1021/acs.jcim.2c01479</p> <ul> <li>Individual .zip files contain the WaterMap results for each structure.</li> </ul> <p><em>Additional notes: there is a typo in the 5v90 file name (it is the 5v9o structure).</em></p> <p>Related datasets: 10.5281/zenodo.7341954 and 10.5281/zenodo.7342311</p>
Audio files and dataset: Guess who? Evaluating individual acoustic monitoring for males and females of the Tawny Pipit, a migratory passerine bird with a simple song
<p>We have uploaded the audio files (song files + recording files) and the dataset of the analysis conducted in our manuscript entitled ''Guess who? Evaluating individual acoustic monitoring for males and females of the<br> Tawny Pipit, a migratory passerine bird with a simple song''. This study is published in the scientific journal ''Journal of Ornithology''. We have also provided a txt. file, called 'Read me' explaining the uploaded data in more detail.</p>
Genomically predicted theoretical protein mass database for mass spectrometry (GPMsDB) evaluation datasets
<p>These are datasets obtained for the evaluation of GPMsDB (genomically predicted protein mass database) and its toolkits (GPMsDB-tk/GPMsDB-dbtk). The following datasets are deposited.</p> <ul> <li>The genome sequences of the strains newly sequenced and added using GPMsDB-dbtk (genomes_added.zip)</li> <li>MALDI-TOF-MS peak lists obtained from reference bacterial and archaeal strains (MALDI_peaklists.zip)</li> <li>16S rRNA gene sequences of the faecal isolates (mice_isolates_16S_nanopore.zip)</li> <li>Metagenome-assembled genomes from mouse faeces (mice_MAGs.zip)</li> </ul>
Korean Morpheme Disambiguation Evaluation Dataset
<p>바른팀(바이칼AI & 한국언론진흥재단)에서 구축한 한국어 모호성 평가 데이터세트</p>
Freebase Datasets for Robust Evaluation of Knowledge Graph Link Prediction Models
<p><strong>Freebase</strong> is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong <strong>type system</strong>; its properties are purposefully represented in <strong>reverse pairs</strong>; and it uses <strong>mediator objects</strong> to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available <strong>full-scale</strong> Freebase dataset that has gone through <strong>proper preparation</strong>. </p><p> </p><p>Dataset Details</p><p>The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:</p><ul><li>Subject matter triples file<ul><li><i>fb+/-CVT+/-REV</i> One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.<ul><li>Example of a row in train.txt, valid.txt, and test.txt: <ul><li>2, 192, 0</li></ul></li><li>Example of a row in entity2id.txt:<ul><li>/g/112yfy2xr, 2</li></ul></li><li>Example of a row in relation2id.txt:<ul><li>/music/album/release_type, 192</li></ul></li><li>Explaination<ul><li>"/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.</li></ul></li></ul></li></ul></li><li>Type system file<ul><li><i>freebase_endtypes</i>: Each row maps an edge type to its required subject type and object type.<ul><li>Example<ul><li>92, 47178872, 90</li></ul></li><li>Explanation<ul><li>"92" and "90" are the type id of the subject and object which has the relationship id "47178872".</li></ul></li></ul></li></ul></li><li>Metadata files<ul><li><i>object_types</i>: Each row maps the MID of a Freebase object to a type it belongs to.<ul><li>Example<ul><li>/g/11b41c22g, /type/object/type, /people/person</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b41c22g" has a type "/people/person"</li></ul></li></ul></li><li><i>object_names</i>: Each row maps the MID of a Freebase object to its textual label.<ul><li>Example<ul><li>/g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.</li></ul></li></ul></li><li><i>object_ids</i>: Each row maps the MID of a Freebase object to its user-friendly identifier.<ul><li>Example<ul><li>/m/05v3y9r, /type/object/id, "/music/live_album/concert"</li></ul></li><li>Explanation<ul><li>The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.</li></ul></li></ul></li><li><i>domains_id_label</i>: Each row maps the MID of a Freebase domain to its label.<ul><li>Example<ul><li>/m/05v4pmy, geology, 77</li></ul></li><li>Explanation<ul><li>The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.</li></ul></li></ul></li><li><i>types_id_label</i>: Each row maps the MID of a Freebase type to its label.<ul><li>Example<ul><li>/m/01xljxh, /government/political_party, 147</li></ul></li><li>Explanation<ul><li>The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.</li></ul></li></ul></li><li><i>entities_id_label</i>: Each row maps the MID of a Freebase entity to its label.<ul><li>Example<ul><li>/g/11b78qtr5m, Viroliano Tries Jazz, 2234</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.</li></ul></li><li><i>properties_id_label</i>: Each row maps the MID of a Freebase property to its label.<ul><li>Example<ul><li>/m/010h8tp2, /comedy/comedy_group/members, 47178867</li></ul></li><li>Explanation<ul><li>The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.</li></ul></li></ul></li><li><i>uri_original2simplified</i> and <i>uri_simplified2original</i>: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.<ul><li>Example<ul><li><i>uri_original2simplified</i><ul><li>"<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>": "/type/property/unique"</li></ul></li><li><i>uri_simplified2original</i><ul><li>"/type/property/unique": "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>"</li></ul></li></ul></li><li>Explanation<ul><li>The URI "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.</li><li>The identifier "/type/property/unique" in our dataset has URI <a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a> in the original Freebase RDF dataset.</li></ul></li></ul></li></ul></li></ul></li></ul>
Dataset of the Article Evaluating a Framework of Conceptual Modelling Research through a Family of Experiments
<p>Data set of the article "Evaluating a Framework of Conceptual Modelling Research through a Family of Experiments". Contains the results of:</p> <p>-Demographic questionnaire</p> <p>- Agreement and correctness of Paper A</p> <p>- Agreement and correctness of Paper B</p> <p>- Agreement and correctness of Paper C</p> <p>-Satisfaction questionnaire</p> <p>-Procedure and Pros and Cons of the framework</p>
Dataset of paper "Evaluation of the uniformity of UVA LED illumination on flat surfaces: Discrete Ordinate Method, Single Axis, and Surface Scanning Radiometry"
<p>Dataset of paper "Evaluation of the uniformity of UVA LED illumination on flat surfaces: Discrete Ordinate Method, Single Axis, and Surface Scanning Radiometry"</p> <ul> <li>Maximum Horizontal Intensity plots from discrete ordinates model simulations</li> <li>Maximum Horizontal Intensity plots from radiometry scans</li> <li>Peak intensity measurements for a UV LED lamp using Radiometry and DOM simulations</li> <li>Power measurements from DOM and radiometry measurements across a flat surface for a UV LED lamp</li> <li>Homogeneity data of irradiance on a flat surface</li> </ul>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Datasets used for the performance evaluation of kataegis detection tools
<p>These two datasets (.csv files) were used for the performance evaluation of katdetectr and other publicly available kataegis detection tools (link to paper, not yet published).</p> <p>The synthetic data was generated using the generateSyntheticData function from the katdetectr package (https://github.com/ErasmusMC-CCBC/katdetectr).</p> <p>Additionally we provide all files and RData objects needed for running the scripts for the performance evaluation which can be found on GitHub at: https://github.com/ErasmusMC-CCBC/evaluation_katdetectr.</p> <p>We present this dataset under the Public Domain Dedication (CC0) waiver.</p>
Dataset related to paper "Preliminary Analysis of Non-destructive Test Methods to Evaluate the Self-healing Efficiency on the Construction Site"
<p>Dataset of the study reported in the conference paper ""Preliminary Analysis of Non-destructive Test Methods to Evaluate the Self-healing Efficiency on the Construction Site" presented at the SynerCrete'23 Conference (14-16 June 2023, Milos Island, Greece).</p>
Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation"
<p>Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation":</p> <ul> <li>Results for the CFD model of the residence time distribution in the tubular reactor and E(t) curve for an ideal laminar.</li> <li>UV dosage histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Microbial inactivation histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Results for the CFD model of the residence time distribution in the annular reactor for different flow rates.</li> <li>UV dosage histograms at the outlet surface for the annular reactor for different flow rates.</li> <li>Microbial inactivation histograms at the outlet surface in the annular reactor for different flow rates.</li> </ul>
Dataset for Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire
<p>These are the datasets that we used for evaluating the construct validity of the Charité Alarm Fatigue Questionnaire (CAFQa) in a forthcoming publication. All items were answered on a 5-point Likert scale and were scored by us as follows: -2/“I do not agree at all”, -1/“I do not agree”, 0/“I agree in part”, 1/“I agree”, 2/"I very much agree".</p> <p>A previous version of this upload included only the data of Study 1. A new version provides the data of Study 2. Please refer to the methods section of the forthcoming publication for more details.</p> Variable names and their corresponding item. Items marked with <table><tbody><tr> <th>Variable Name</th> <th>CAFQa Item</th> </tr> </tbody><tbody> <tr> <td>procedural_instruction</td> <td>In my ward, procedural instruction on how to deal with alarms is regularly updated and shared with all staff.<sup>a</sup></td> </tr> <tr> <td>respond_quickly</td> <td>Responsible personnel respond quickly and appropriately to alarms.<sup>a</sup></td> </tr> <tr> <td>motivation_decrease</td> <td>With too many alarms on my ward, my work performance, and motivation decrease.</td> </tr> <tr> <td>physical_symptoms</td> <td>Too many alarms trigger physical symptoms for me, e.g., nervousness, headaches, and sleep disturbances.</td> </tr> <tr> <td>ward_floor</td> <td>The acoustic and visual monitor alarms used on my ward floor and in my nurse station allow me to assign the patient, the device, and the situation clearly.<sup>a</sup></td> </tr> <tr> <td>reduce_concentration</td> <td>Alarms reduce my concentration and attention.</td> </tr> <tr> <td>alarm_limits</td> <td>Alarm limits are regularly adjusted based on patients' clinical pictures (e.g., blood pressure limits for conditions after bypass surgery).<sup>a</sup></td> </tr> <tr> <td>interrupt_workflow</td> <td>My or neighboring patients' alarms or crisis alarms frequently interrupt my workflow.</td> </tr> <tr> <td>alarms_confuse</td> <td>There are situations when alarms confuse me.</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> Other variables in the data set. <table><tbody><tr> <th>Variable Name</th> <th>Explanation</th> </tr> </tbody><tbody> <tr> <td>self_reported_AF</td> <td>self-estimated alarm fatigue in percent</td> </tr> <tr> <td>estimated_false_alarms</td> <td>perceived rate of false alarms in the participant's ICU</td> </tr> <tr> <td>monthly_time_on_ICU</td> <td>the average number of workdays per month in an intensive care or monitoring area</td> </tr> <tr> <td>ICU_experience</td> <td>number of years/months of ICU experience</td> </tr> <tr> <td>profession</td> <td>physician, nurse, or supporting nurse</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Members of the Study Group</strong> <strong>in alphabetical order</strong>: <em>Dr. med. Mirza Aghamov</em><sup><em>1</em></sup><em>, Prof. Dr. med. Manfred Blobner<sup>2</sup>, Prof. Dr. med. Ulrich Frey<sup>3</sup></em><em>, Prof. Dr. Christian von Heymann<sup>4</sup></em><em>, Prof. Dr. med. Bettina Jungwirth</em><sup><em>1</em></sup><em>, Dr. med. Dragutin Popovic<sup>4</sup></em><em>, Prof. Dr. med. Michael Sander<sup>5</sup></em><em>. </em></p> <p>1: <em>Department of Anesthesiology and Intensive Care Medicine, University Hospital Ulm, Ulm University, Ulm, Germany</em></p> <p>2: <em>Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany</em></p> <p>3: <em>Department for Anesthesiology, Surgical Intensive Care, Pain and Palliative Medicine, Marien Hospital Herne – Universitätsklinikum der Ruhr-Universität Bochum, Herne, Germany</em></p> <p>4: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Vivantes Klinikum im Friedrichshain, Berlin, Germany</em></p> <p>5: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Justus Liebig University, Giessen, Germany</em></p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.