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Dataset results
107 results for “automated analysis”
Computerized automated text and voice analysis - a single case study of seven chronically schizophrenia patients in art therapy (livopict)
<p>This is an explorative monocentric art therapy study in the form of a quantitative single-case study (n-of-one-trial without baseline) in seven patients with chronic schizophrenia. The interventions consist of therapist-guided interviews with patients about their images, which are digitally recorded and analyzed using both text and voice analysis software.</p> <p>This dual study approach of digitized interviews allows correlations between the two portions of the recorded voice samples and forms the internal control in the study approach.</p> <p>The sample of seven patients with chronic schizophrenia (n=7), defined under inclusion and exclusion criteria, engaged in pictorial activity as part of their weekly group art therapy (non- invasive portion of the studies intervention: image creation, IC). In a one-on-one interview after three or four days, study participants were invited to talk with the author about the paintings they had created. An interview developed especially for the study served as a guide and, above all, as a standardized stimulus for the patients' statements (therapist-guided picture discussion, TGPD).</p> <p> </p> <p> </p>
Digital-scRRBS: A cost-effective, highly sensitive and automated single-cell methylome analysis platform via digital microfluidics
GEO Series GSE214106. Homo sapiens. 45 samples. Type: Methylation profiling by high throughput sequencing.
Genomic and transcriptomic data integration and its automated analysis in clear-cell renal cell carcinoma suggests population heterogeneity of causes and mechanism of the disease
GEO Series GSE76351. Homo sapiens. 24 samples. Type: Expression profiling by array.
Optimizing phase variability threshold for automated synchrogram analysis of cardiorespiratory interactions in amateur cyclists
<p>Cairo B, de Abreu RM, Bari V, Gelpi F, De Maria B, Rehder-Santos P, Sakaguchi CA, da Silva CD, De Favari Signini É, Catai AM, Porta A. Optimizing phase variability threshold for automated synchrogram analysis of cardiorespiratory interactions in amateur cyclists. Philos Trans A Math Phys Eng Sci. 2021 Dec 13;379(2212):20200251. doi: 10.1098/rsta.2020.0251. Epub 2021 Oct 25. PMID: 34689616.</p> <p>Abstract</p> <p>We propose a procedure suitable for automated synchrogram analysis for setting the threshold below which phase variability between two marker event series is of such a negligible amount that the null hypothesis of phase desynchronization can be rejected. The procedure exploits the principle of maximizing the likelihood of detecting phase synchronization epochs and it is grounded on a surrogate data approach testing the null hypothesis of phase uncoupling. The approach was applied to assess cardiorespiratory phase interactions between heartbeat and inspiratory onset in amateur cyclists before and after 11-week inspiratory muscle training (IMT) at different intensities and compared to a more traditional approach to set phase variability threshold. The proposed procedure was able to detect the decrease in cardiorespiratory phase locking strength during vagal withdrawal induced by the modification of posture from supine to standing. IMT had very limited effects on cardiorespiratory phase synchronization strength and this result held regardless of the training intensity. In amateur athletes training, the inspiratory muscles did not limit the decrease in cardiorespiratory phase synchronization observed in the upright position as a likely consequence of the modest impact of this respiratory exercise, regardless of its intensity, on cardiac vagal control. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.</p>
A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography
<p>Full dataset of segmentations for both the thoracic artery and the proximal pulmonary arteries (format: standard NIfTI, nii) from Saitta S, Sturla F, Caimi A, Riva A, Palumbo MC, Nano G, Votta E, Corte AD, Glauber M, Chiappino D, Marrocco-Trischitta MM, Redaelli A. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. J Digit Imaging. 2022 Jan 26. doi: 10.1007/s10278-021-00535-1. Epub ahead of print. PMID: 35083618.</p> <p>Abstract</p> <p>Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.</p> <p> </p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
Open the record for dataset details and reuse information.
Towards the automation of high-throughput quantitative NMR analysis: NMR and Gaussian data
<p>The file contains: raw NMR data acquired throughout the project and Gaussian output (.log) files both, failed and complete calculations.</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.