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831
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ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Data from: ASHS-OAP atlas for automatic entorhinal cortex segmentation
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Hybrid ATPG (Automatic Test Pattern Generation) algorithm
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Data from: Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy
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Data from: Automatic segmentation of early Triassic vertebrate fossil CT scans: Reducing human annotation time through deep learning
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Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems
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Data from: Automatic Laplacian-based shape optimization for patient-specific vascular grafts
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Automatic titration detection method of organic matter content based on machine vision
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Mere presence of co-eater automatically shifts foraging tactics toward “fast and easy” food in humans
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Astrea: automatic generation of SHACL shapes from ontologies
<p> This resource is Astrea-KG, a Knowledge Graph that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraints patterns.</p>
OPTIM-TUNE: In-flight automatic optimal tuning of UAV controllers for robust operation
<p>Tuning results, interplay between gains and allowable radius of a cylindric force constraint. Use PS-Tricks in LaTeX to obtain the final graphical results. </p>
Stimuli and Results for "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription"
<p>This contains the stimuli and the participants data for the listening tests presented in the paper:</p> <p>Adrien Ycart, Lele Liu, Emmanouil Benetos, Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription". <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1):68-81, 2020 .</p> <p>More precisely, it contains:</p> <ul> <li>MAPS_midi_cut.zip: The MIDI files used to create the stimuli </li> <li>cut_points_seconds.zip: The points in seconds at which the MAPS music pieces were cut to make the stimuli. These correspond to manually-selected 5 to 10 seconds chunks, roughly corresponding to musical phrases.</li> <li>listening_test_results.zip: The data gathered during the listening test: <ul> <li>user_data.csv contains data about participants</li> <li>answers_data.csv contains the answers given by all participants</li> <li>comments.txt contains the comments left by the participants.</li> </ul> </li> </ul> <p>For any enquiries, please contact Adrien Ycart (a.ycart@qmul.ac.uk) or Emmanouil Benetos (emmanouil.benetos@qmul.ac.uk).</p> <p> </p>
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
<p><b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p> <p><b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p> <p><b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p> <p><b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p> <p><b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>
Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence
<p>This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. dataset folder</strong></p> <ul> <li>dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews.</li> <li>readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.</li> </ul>
CGRE Framework Dataset - A Dataset automatically generated to evaluate OCR Software on Webdocuments
<p><strong>Description</strong><br> The provided dataset was generated by the <a href="https://github.com/Drizzy3D/CGRE">CGRE Framework.</a><br> It was generated as a part of a bachelor thesis and used to evaluate the Tesseract OCR Software on webdocuments.</p> <p><strong>CGRE_dataset.zip:</strong><br> <em>1. crawl.json</em><br> This file contains crawling results from the alexa.com Top 50 most used webpages in the US from the 7th June 2020.<br> The crawling was done specifically for styling information only.</p> <p><em>2. html</em><br> The generated webdocuments can be found in this directory.<br> They are based on the crawled styling information.<br> The levels of the directory are used to store the different styling attributes.<br> Every directory is named by the used value for a specific styling attribute.<br> Every word is placed in a span html element.</p> <p><em>3. dataset</em><br> The rendered webdocuments can be found in this directory as png files.<br> They were rendered using the Chromium Embedded Framework (CEF) and contain corresponding labels.<br> The labels are in the same directory with the same name as the corresponding rendered webdocument, just as txt files.<br> The labels contain "word\t(left,top,width,height)\n" lines.<br> "(left,top,width,height)" is the bounding box of a span element containing a word.<br> "word" is the word in the bounding box.</p> <p><em>4. dataset_tesseract_complete</em><br> This directory contains the Tesseract results on the dataset as txt files.<br> The structure is analogue to the dataset.<br> The txt files contain analogue to the dataset "word\t(left,top,width,height)\n" lines.</p> <p><em>5. evaluation</em><br> The results of the evaluation of Tesseract on the dataset.<br> To evaluate the localisation of words by Tesseract, the Intersection Over Union metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> To evaluate the determination of words by Tesseract, a normalized Levenshtein distance metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> The times were measured by using this system:<br> Ubuntu 20.04, AMD Ryzen 5 1600 CPU, AMD Radeon RX Vega 56 GPU, 16 GB DDR4 RAM with 2400 MHz<br> The different threshold values are stored in the filenames.<br> You can find the results in the csv files.<br> Every line contains the results for a specific webdocument.<br> The txt files contain calculated precision and recall values.</p>
An Ensemble Approach to Automatic Structuring of Radiology Reports -- Dataset
<p>This dataset contains the annotations for 100 radiology reports from MIMIC-III dataset. The annotations use the BRAT format and this dataset only provides the annotations (not the original radiology reports). In order to view them on BRAT, you need to request and download the original text from MIMIC-III website. Please note that the file names are the IDs for each radiology report in the MIMIC-III dataset.</p>
[CTD-ES] Automatically Fixing Static Analysis Tools Violations
<p>Video <strong>in Portuguese</strong> with a short presentation summarizing some of the main points of the research conducted during my (Diego) masters' under the supervision of Prof. Dr. Rodrigo Bonifácio.</p>
A Tool for Product Derivation and Semi-Automatic Deployment of Web-Based Systems
<p>This paper presents a tool for product derivation and semi-automatic deployment of web-based systems. The tool focuses on cloned webbased systems implemented using the PHP language, which are refactored to become software product lines. The paper also reports the results of a case study of using the tool in the context of a large scale web-based system. Our findings show some preliminary evidence of the benefits of the tool to reduce: (i) the time of product derivation and deployment; and (ii) the manual effort to customize existing cloned web-based systems.<br> </p>
Implantation of a dual-chamber automatic cardioverter defibrillator in a patient with persistent left superior vena cava: case report and brief literature review
<p>Video 1. Transthoracic echocardiography, parasternal long axis – dilated left ventricle with hypokinesia of ventricular walls and atrial enlargement; Video 2. Transthoracic echocardiography – apical view revealing the moderate functional mitral regurgitation; Video 3. Intraprocedural standard anteroposterior fluoroscopic view with the atrial and ventricular lead in place; Video 4. Intraprocedural fluoroscopic view (right anterior oblique projection at 30 degrees): atrial lead in the anterior RAA, ventricular lead at the apex of the right ventricle; Video 5. Venography performed by standard fluoroscopy through a catheter placed at the level of the left median cubital vein: left SVC draining in the right atrium via the coronary sinus; obstruction of the innominate vein, with no contrast flow; residual stenosis/kinking at the border between left SVC and CS; Video 6. Venography performed by standard fluoroscopy through a catheter placed at the level of the right median cubital vein: right SVC draining in the right atrium; obstruction of the innominate vein with no contrast flow.</p>
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 in A tale of two genera: the revival of Hoplodoris (Nudibranchia: Discodorididae) with the description of new species of Hoplodoris and Asteronotus
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 and Hoplodoris Bergh, 1880 COI sequences using three genetic distance calculations: (A) Jukes-Cantor (JC69); (B) Kimura (K80); (C) Simple Distance. Presumed intraspecific variation is shown in white, interspecific variation in grey.
Using Code Reviews to Automatically Configure Static Analysis Tools
<p>Working dataset (check traceability oracle) used in the paper</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.