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831
datasets available to search
ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Automatic Segmentation of Prostate Cancer using Deep Learning
<p>Demo of Prostate Cancer Segmentation on 3D Magnetic Resonance Imaging using deep learning</p>
Action Planning Makes Physical Activity More Automatic, Only If it Is Autonomously Regulated: A Moderated Mediation Analysis
<p>Dataset used for analysis. </p>
Fast likelihood calculations for automatic identification of macroevolutionary rate heterogeneity in continuous and discrete traits
<p>Understanding phenotypic disparity across the tree of life requires identifying where and when evolutionary rates change on phylogeny. A primary methodological challenge in macroevolution is therefore to develop methods for accurate inference of among-lineage variation in rates of phenotypic evolution. Here, we describe a method for inferring among-lineage evolutionary rate heterogeneity in both continuous and discrete traits. The method assumes that the present-day distribution of a trait is shaped by a variable-rate process arising from a mixture of constant-rate processes and uses a single-pass tree traversal algorithm to estimate branch-specific evolutionary rates. By employing dynamic programming optimization techniques and approximate maximum likelihood estimators where appropriate, our method permits rapid exploration of the tempo and mode of phenotypic evolution. Simulations indicate that the method reconstructs rates of trait evolution with high accuracy. Application of the method to datasets on squamate reptile reproduction and turtle body size recovers patterns of rate heterogeneity identified by previous studies but with computational costs reduced by many orders of magnitude. Our results expand the set of tools available for detecting macroevolutionary rate heterogeneity and point to the utility of fast, approximate methods for studying large scale biodiversity dynamics.</p>
Open Science materials of the paper "Automatically Recognizing the Semantic Elements from UML Class Diagram Images"
<p>This submission contains such files:</p> <p>1. "questionnaire.docx": The questionnaire for the survey. The file contains all the questions and answers.<br> 2. "raw data collected from participants.xlsx": The raw data collected from participants. Each row in the file represents a participant's answers to all questions, including the date, source, IP, and answers.<br> 3. "raw data collected from open-source community.xlsx": The raw data collected from open-source community (the UML diagram usage). It contains the repositories and GitHub URLs, the UML diagrams and the corresponding links, and some statistics about the UML diagram usage.<br> 4. "an implementation of ReSECDI.zip", "utility source code.zip", "utility compiled JAR.zip", and ".m2.zip": An implementation of ReSECDI, and its dependencies. The implementation is in Java, and it requires JDK11 or higher. It depends on a project named "utility", in addition to other dependencies. The source code of "utility" is provided in "utility source code.zip", the compiled JAR file is in "utility compiled JAR.zip", and the maven dependency files are provided in ".m2.zip". The ways to add the "utility" to the implementation's dependencies are explained in the "readme.txt".<br> 5. "instructions for how to use the artifacts.docx": The instructions for how to use the implementation of ReSECDI. It mainly explains the key components of the implementation, and how to set the parameters.<br> 6. "diagrams used for its evaluation.zip": The diagrams used for the evaluation. There are 50 diagrams collected from the open-source community. Each diagram's name represents its belonging repository.<br> 7. "raw data collected during the evaluation.xlsx": The raw data collected during the evaluation. It contains the statistics of the classes and relationships for each diagram, and the recognition results.<br> 8. "Manuscript.pdf": The manuscript explaining our approach.<br> 9. "readme.txt": The readme file explaining details about each file.</p>
Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery
<p>Cochlear implants (CIs) provide an opportunity for the hearing impaired to perceive sound through electrical stimulation of the hearing (cochlear) nerve. However, there is a high risk of losing a patient's natural hearing during CI surgery, which has been shown to reduce speech perception in noisy environments as well as music appreciation. This is a major barrier to the adoption of CIs by the hearing impaired. Electrocochleography (ECochG) has been used to detect intra-operative trauma that may lead to loss of natural hearing. There is early evidence that ECochG can enable early intervention to save natural hearing of the patient. However, detection of trauma by observing changes in the ECochG response is typically carried out by a human expert. Here, we discuss a method of automating the analysis of cochlear responses during CI surgery. We establish, using historical patient data, that the proposed method is highly accurate (~94% and ~95% for sensitivity and specificity respectively) when compared to a human expert. The automation of real-time cochlear response analysis is expected to improve the scalability of ECochG and improve patient safety.</p>
Deep Learning-based Method for Automatic Resolution of GC-MS Data from Complex Samples
<p>气相色谱-质谱;多元曲线分辨率</p>
Use of a pressure wire for automatically correcting artifacts in phasic pressure tracings from a fluid-filled catheter
<div> <div> <div>Background/purpose: Matching phasic pressure tracings between a fluid-filled catheter and high-fidelity pressure wire has received limited attention, although each part contributes half of the information to clinical decisions. We aimed to study the impact of a novel and automated method for improving the phasic calibration of a fluid-filled catheter by accounting for its oscillatory behavior.</div> <div> </div> <div>Methods/materials: Retrospective analysis of drift check tracings was performed using our algorithm that corrects for mean difference (offset), temporal delays (timing), differential sensitivity of the manifold transducer and pressure wire sensor (gain), and the oscillatory behavior of the fluid-filled catheter described by its resonant frequency and damping factor (how quickly oscillations disappear after a change in pressure).</div> <div> </div> <div>Results: Among 2886 cases, correcting for oscillations showed a large improvement in 28% and a medium improvement in 41% (decrease in root mean square error >0.5 mmHg to <1 or 1–2 mmHg, respectively). 96 % of oscillators were underdamped with median damping factor of 0.27 and frequency of 10.6 Hz. Fractional flow reserve or baseline Pd/Pa demonstrated no clinically important bias when ignoring oscillations. However, uncorrected subcycle non-hyperemic pressure ratios (NHPR) displayed both bias and scatter.</div> <div> </div> <div>Conclusions: By automatically accounting for the oscillatory behavior of a fluid-filled catheter system, phasic matching against a high-fidelity pressure wire can be improved compared to standard equalization methods. The majority of tracings contain artifacts, mainly due to underdamped oscillations, and neglecting them leads to biased estimates of equalization parameters. No clinically important bias exists for whole-cycle metrics, in contrast to significant effects on subcycle NHPR.</div> </div> </div>
Supplementary material 1 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
A template for an occurrence or specimen record to be imported as a material citation.
Figure 5 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 5 - Selection of the journal and "Data Paper (Biosciences)" template in the ARPHA Writing Tool.
Figure 2 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 2 - This fictionalized workflow presents the flow of information content of biodiversity specimens or biodiversity occurrences from the data portals GBIF, BOLD Systems, iDigBio, and PlutoF, through user-interface elements in AWT to textualized content in a Taxonomic Paper manuscript template intended for publication in the Biodiversity Data Journal.
Figure 3 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 3 - User interface of the ARPHA Writing Tool controlling the import of specimen records from external databases.
Figure 7 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 7. – Example of detection and classification obtained with an image including Crinoïds, a Gastropod and pieces of seaweed with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 5 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 5. – Otolith image process of plaice (Pleuronectes platessa; Linnaeus, 1758) standardization with illustration of three main steps presented in figure 3.
Figure 3 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 3. – Proposed processing pipe- line for converting otolith images from a conventional ageing database to a standardized shape database format in the R environment (R scripts are presented in the Supplementary data).
Phonetic Richness for Improved Automatic Speaker Verification: Aplawd-Based Speaker Verification Protocols
<p>This data was created as part of the work entitled "Phonetic Richness for Improved Automatic Speaker Verification", published in EUSIPCO 2024.</p> <p>In doing our experimental evaluation, Pindrop used FLAC files from the APLAWD Markings Dataset. You can obtain copies of these same FLAC files directly from the developers here: <a href="https://github.com/serwy/aplawdw" target="_blank" rel="noopener">https://github.com/serwy/aplawdw</a></p> <p>This data contains two automatic speaker verification protocols: "Aplawd" and "Aplawd-Repetitive". See below for details regarding the structure of the data for each of these protocols.</p> <p><strong>Files defining the Aplawd protocol:</strong><br>> <code>Pindrop_aplawd_protocol/models.csv</code></p> <ul> <li>This file defines what files should be used to create each enrollment model used in this protocol.</li> <li>Columns: <ul> <li><code>enroll_id</code>: unique string identifying the enrollment model that this row corresponds to</li> <li><code>filename</code>: filename specifying the file from the APLAWD Markings Dataset to be used in creating the enrollment model specified for this row by enroll_id.</li> </ul> </li> <li>Note: a single model is created for each enroll_id, using all of the files specified for the given enroll_id</li> </ul> <p>> <code>Pindrop_aplawd_protocol/trials_same_gender.csv</code></p> <ul> <li>This file defines the trials that make up this protocol, specifying pairs of enrollment model and probe audios to test against eachother.</li> <li>Columns: <ul> <li><code>enroll_id</code>: unique string identifying the enrollment model whose identity will be compared against in the trial defined by this row.</li> <li><code>probe_filename</code>: filename specifying the file from the APLAWD Markings Dataset for which the identity of the speaker should be compared against the identity of the corresponding enrollment model for this row.</li> <li><code>probe_identity</code>: unique string identifying the true speaker identity for this row's probe.</li> </ul> </li> </ul> <p> </p> <p><strong>Files defining the Aplawd-Repetitive protocol:</strong></p> <p>(<strong>NOTE</strong>: the key difference from the above Aplawd protocol is in the probe_filename column of the trials_same_gender.csv file)</p> <p>> <code>Pindrop_aplawd_repetitive_protocol/models.csv</code></p> <ul> <li>This file defines what files should be used to create each enrollment model used in this protocol.</li> <li>Columns: <ul> <li><code>enroll_id</code>: unique string identifying the enrollment model that this row corresponds to</li> <li><code>filename</code>: filename specifying the file from the APLAWD Markings Dataset to be used in creating the enrollment model specified for this row by enroll_id.</li> </ul> </li> <li> Note: a single model is created for each enroll_id, using all of the files specified for the given enroll_id</li> </ul> <p>> <code>Pindrop_aplawd_repetitive_protocol/trials_same_gender.csv</code></p> <ul> <li>This file defines the trials that make up this protocol, specifying pairs of enrollment model and probe audios to test against eachother.</li> <li>Columns: <ul> <li><code>enroll_id</code>: unique string identifying the enrollment model whose identity will be compared against in the trial defined by this row.</li> <li><code>probe_filename</code>: an underscore ("_") delimited string of filename(s) specifying the file(s) from the APLAWD Markings Dataset that should be concatenated to form the probe audio for this row's trial. The resulting probe's speaker identity should be compared against the identity of the corresponding enrollment model for this row.</li> <li><code>probe_identity</code>: unique string identifying the true speaker identity for this row's probe.</li> </ul> </li> </ul>
Database for Automatic Spatial Audio Scene Classification in Binaural Recordings of Music
<p>This repository contains supplementary material for the paper titled ‘<em>Automatic Spatial Audio Scene Classification in Binaural Recordings of Music</em>.’</p> <p>The database consists of the five following folders:</p> <ol> <li>Binaural recordings used for training</li> <li>Binaural recordings used for testing</li> <li>Extracted features</li> <li>Classification algorithm</li> <li>Music credits</li> </ol>
scPanel: A tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets
<p>Dataset used to reproduce severe COVID-19 prediction results in scPanel manuscript.</p>
LCMDC: Large-scale Chinese Medical Dialogue Corpora for Automatic Triage and Medical Consultation
Open the record for dataset details and reuse information.
On Automatically Generating Assertions for Validating Reported Failures in Android Apps
<p>Dataset and reproduction package.</p>
On Automatically Generating Assertions for Validating Reported Failures in Android Apps
<p>dataset and reproduction package</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.