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832 results for “automatic”
Datasets used for Automatic Acquisition of Non-Saturated Hyperspectral Images
<p>data-sets acquired for studying correlation between automatic exposure times and hyper-spectral images, with the aim of devising procedures for automatic acquisition of non-saturated hyper-spectral images</p>
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Repository of speech features from speakers with and without Parkinson's Disease. Neurovoz - Rasta PLP - V2 - Scientific Reports Publication: Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease
<p>This repository contains the Rasta-PLP features of six different speech recordings (sentences) from Neurovoz corpus (47 parkinsonian and 32 control speakers whose mother tongue is Spanish Castillian.)<br> Number of PLP coefficients: [6, 8, 10, 12, 14, 16, 18, 20].<br> Delta coefficients: Yes<br> Delta Delta coefficients: Yes<br> Sampling rate: 16 kHz<br> Frame size: 15 ms<br> Frame overlapping: 50%</p> <p>This subset of the Neurovoz corpus was recorded between 2015 and 2017 by Universidad Politécncia de Madrid and Hospital General Universitario Gregorio Marañón.</p> <p>This version includes the same files as the previous version and information about UPDRS, H&Y, years since diagnosis and age of each participant.</p> <p>The sentences were:</p> <p>BARBAS: "Cuando las barbas de tu vecino veas pelar, pon las tuyas a remojar"</p> <p>CALLE: "De la calle vendrá quien de tu casa te echará"</p> <p>DIABLO: " Cuando el diablo no sabe qué hacer, con el rabo mata moscas "</p> <p>PETACA BLANCA: " La petaca blanca es mía"</p> <p>PIDIO: "No pidas a quien pidió ni sirvas a quien sirvió"</p> <p>SOMBRA: " El que a buen árbol se arrima, buena sombra le cobija "</p> <p> </p> <p>How to cite:<br> [1] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Grandas-Perez, F., Shattuck-Hufnagel, S. Yagüe-Jimenez, V., and Dehak, N. (2019). Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s disease.Scientific reports 9, 19066.</p> <p><br> [2] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Villalba, J., Rusz, J., Shattuck-Hufnagel, S. and Dehak, N. (2019). A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing. Biomedical Signal Processing and Control, 48, 205-220.</p> <p>BibTeX:</p> <pre><code>@article{moro2019phonetic, title={Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge A. and Godino-Llorente, Juan I. and Grandas-Perez, Francisco and Shattuck-Hufnagel, Stefanie and Yague-Jimenez, Virginia and Dehak, Najim}, journal={Scientific Reports}, volume={9}, pages={19066}, year={2019}, publisher={Nature Research Publishing} } @article{moro2019forced, title={A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge Andres and Godino-Llorente, Juan Ignacio and Dehak, Najim}, journal={Biomedical Signal Processing and Control}, pages={205--220}, volume={48}, year={2019}, publisher={Elsevier} } </code></pre> <p> </p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p> <div> </div>
Automatic System for Food Waste Assessment at Household
<p>ntroducing the Automatic System for Food Waste Assessment at Household developed under the WASTELESS project. This tool, which is tailored to everyday use, supports the measurement of food waste in households and is intended to improve and validate existing methods such as surveys. The tool includes a set of features to measure and estimate various types of food waste generated in households. The developed tool will be demonstrated on a representative sample of households to ensure its reliability and applicability. The core of this tool is a user-friendly scale complemented by a dedicated application for cell phones that enables seamless data collection and transfer to a centralized database. The data collected is then analysed and the results obtained are extrapolated to a wider population, allowing a more comprehensive evaluation of the parameters traditionally determined with questionnaires and interviews.</p>
PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
<p>Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing</p>
Automatic Complaints Classification in E-Commerce: A Case Study Using CRISP-DM
<p>The growth of e-commerce has been remarkable in recent years, driven by increasing consumer demand for attention and quick responses. Given the large volume of transactions and complaints accompanying this increase, automating the classification of these complaints can help quickly route them to the appropriate departments. This paper presents a computational approach using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to automate the complaints screening process. We categorized 600 real complaints from three e-commerce platforms in Brazil. The learning model was trained progressively, using an initial set of 25 complaints in each category. The classification model obtained an accuracy of 85\% and an average of over 80\% across all relevant metrics, including precision, recall, and F1-Score. The results confirmed the effectiveness of the developed model for automated complaint classification in e-commerce, providing a computational strategy that improves the customer service process and allows for quicker problem resolution.</p>
Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana (dataset)
<p>The dataset contains all the data required to reproduce the experiments done in the paper "Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana", published in the 25th International Conference on Theory and Practice of Digital Libraries (<a href="http://www.tpdl.eu/tpdl2021/">TPDL'21</a>). In that work we run an experiment using the Europeana CH digital library as a use case, and we evaluated the effectiveness of a multilingual information retrieval strategy using machine translations to English as pivot language. We used the CEF translation service (eTranslation) for the translation of queries and content to English (<a href="https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation">https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation</a>).</p> <p>The dataset is also available at <a href="https://rnd-2.eanadev.org/share/crosslingual-search/">https://rnd-2.eanadev.org/share/crosslingual-search/</a>, and it is organized in four main folders:</p> <ul> <li><strong>queries</strong>: sample of 68 queries and their translations to English. The queries were issued in languages other than English from the Europeana Portal, using the Europeana’s 1914-1918 thematic collection, between January and August 2019.</li> <li><strong>transcriptions</strong>: sample of 18,257 handwriting transcriptions and its translations to English. The transcriptions are taken from the Europeana 1914-1918 thematic collection, and obtained from the Transcribathon crowdsourcing platform (https://europeana.transcribathon.eu/).</li> <li><strong>solr_configuration</strong>: Apache Solr search engine configuration used in the experiments (which replicates the one used in Europeana).</li> <li><strong>results</strong>: manual evaluation of the query translations, and automatic evaluation of the multilingual retrieval.</li> </ul> <p> </p>
A case study on automatic summarization for gray literature
<p>Replication package for a case study on using PositionRank to automatic summarize blog posts based on the Spicy implementation of the algorithm</p>
Integrated automatic design process for robot swarms
<p>Demonstration of the Integrated automatic process for robot swarms in three missions: Aggregation, Foraging, and Migration.<br> The dataset contains the following:<br> 1. SML (Swarm Mission Language) related files: <br> - specification files used to create missions,<br> - generated files to be used by an optimization method <br> 2. AUTOMODE related files:<br> - the log files running AUTOMODE - an optimization method that generates control software for different missions<br> - generated control software <br> 3. Demonstration:<br> - snapshots and videos of running the missions on real robots</p>
Quantum calculation results for "Automatically Generated Model for Light Alkene Combustion"
<p>This repository contains the quantum calculation results associated with the paper "Automatically Generated Model for Light Alkene Combustion" by Gianmaria Pio, Xiaorui Dong, Ernesto Salzano, and William H. Green.</p> <p>In the alkene_qm_data.zip, files are organized according to species. For each species, CBS-QB3 calculation is stored in the 'composite' folder, frequency calculation is stored in the 'freq' folder, and scan jobs for torsional modes (if available) are stored in the 'scan_XXXX' folders.</p> <p> </p>
To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes: Replication Package
<p>This is the replication package for the JSS article To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes. It provides the source data files and the r-script to produce analysis and images.</p>
DeepDIVA automatic synthesizer programmer project
<p>DeepDIVA is a data science project in which we aim to create a deep-learning syntheziser programmer using the vst plugin DIVA. We are creating a tool that addresses the challenge of programming sound synthesizers. This requires a thorough technical understanding as software synthesizers often have many parameters. To tackle this, we built a deep learning model for automatic synthesizer programming that takes a target sound as input, predicts the parameter settings for the synthesizer that cause it to emit as close a sound as possible, and generate a template that you can load in your DIVA synthesizer. In future work, we aim to improve the sound matching performance of our deep neural network architecture. At this point in time the sound matching performance of our best performing architecture is too limited for an application that users can integrate into their workflow, at least not for a synthesizer as complex as the Diva.</p> <p>We generated and saved datasets for training and validating deep learning models. Here we share the dataset (extracted features and patch targets) we created on which our currently best performing model is trained. For this dataset, 11 parameters of the DIVA synthesizers were allowed to vary during data generation.</p>
Datasets of "An Automatically Generated Annotated Corpus for Albanian Named Entity Recognition"
<p>This is an Albanian named entities annotation corpus generated automatically (silver-standard) from Wikipedia and WikiData. It is offered in Apache OpenNLP annotation format.</p> <p>Details of the generation approach may be found in the respective published paper: https://doi.org/10.2478/cait-2018-0009</p> <p>Attached are also the files that were used for generating the Albanian named entities gazetteer and the gazetteer itself in JSON format.</p>
InCLosure Code for Interval Automatic Differentiation
<p>InCLosure Input and Output Files for Interval Automatic Differentiation. To run the code download version 3.0 or later of InCLosure. Download latest release of InCLosure via <a href="https://doi.org/10.5281/zenodo.2702404">https://doi.org/10.5281/zenodo.2702404</a></p>
Data Set "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis"
<p>This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Automatically Answering Developer Questions on Discord with Similar Conversations from the Past
<p>Replication Package for "Automatically Answering Developer Questions on Discord with Similar Conversations from the Past"</p>
Automatic 3D CAD models reconstruction from 2D orthographic drawings
<p>This dataset is built to reconstruct 3D CAD models from 2D drawings and is based on the public dataset <a href="https://github.com/AutodeskAILab/Fusion360GalleryDataset">Fusion 360 gallery</a>. The dataset includes two parts that are the original data and the reconstructed data (in folders '/original_data" and '/reconstructed'). The part of the original data contains the '.svg' files of 2D drawings and the '.step' files of CAD models. Our reconstruction results are shown in the second part (folder '/reconstructed'), which includes the reconstructed 3D wireframes, 3D shapes with faces (storage in FreeCAD files '.FCStd'), and the images of reconstructed models (screenshot). We also test some cases from the <a href="https://deep-geometry.github.io/abc-dataset/">ABC dataset</a>, shown in the 2_ABC folder.</p> <p>Please cite our paper if you use the dataset.</p> <pre>@article{zhang2023automatic, title={Automatic 3D CAD models reconstruction from 2D orthographic drawings}, author={Zhang, Chao and Pinqui{\'e}, Romain and Polette, Arnaud and Carasi, Gregorio and De Charnace, Henri and Pernot, Jean-Philippe}, journal={Computers \& Graphics}, year={2023}, publisher={Elsevier} }</pre>
A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing
<p>This repository holds experimental data and figures of our paper "A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing" in 10th International Conference on Dependable Systems and Their Applications (DSA) 2023.</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN automatic weather station data set from Nafingalm
<p><strong>ABSTRACT</strong></p> <p>This data set was collected with an automatic weather station (AWS) of <a href="http://acinn.uibk.ac.at/">ACINN</a> at the Nafingalm, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The AWS was located close to a small lake at the Nafinglam in the Weer Valley, Tyrol, Austria. The exact location is: 47.2151419°N / 11.712628°E / 1928 m MSL</p> <p><strong>2. Period</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the AWS data set provided here contains the period from 15 June to 12 September 2022. The time series has a measurement interval of 5 minutes and, depending on the parameter, contains both mean values and instantaneous values.</p> <p><strong>3. Instrument details</strong></p> <p>A detailed description of the AWS, its sensors, parameters, calibration and correction procedures is provided as part of the netCDF file metadata, as well as in a PDF file containing the netCDF header extracted with the Linux command ncdump -h.</p> <p><strong>4. Data file</strong></p> <p>The data are provided in a single netCDF file teamx_pc22_aws_nafingalm.nc together with a description of its content in the PDF file teamx_pc22_aws_nafingalm_ncdump_output.pdf.</p> <p><strong>5. Analysis</strong></p> <p>A first analysis of the data was performed in a Bachor thesis (Viebahn, 2023), which is available upon request from the first author of this data set.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, <strong>103</strong>, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment</em>. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Viebahn, T., 2023: <em>Windregime und Stabilität in einem alpinen Seitental im Sommer: Eine Standortcharakterisierung im Rahmen der TEAMx Vorkampagne 2022.</em> Bachlor thesis, University of Innsbruck, 75 pp.</p>
ScienceDex guides
Understand access before you commit
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.