Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
285
datasets available to search
ShareScore release 0.9.0
Dataset results
285 results for “Computational analysis”
Target-agnostic discovery of Rett Syndrome therapeutics by coupling computational network analysis and CRISPR-enabled in vivo disease modeling
GEO Series GSE199049. Xenopus laevis. 13 samples. Type: Expression profiling by array.
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>
Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: a data-driven cluster analysis
<p>This is the processed data for the manuscript<em> Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: a data-driven cluster analysis.</em></p> <p>Dataset evaluated how patients’ baseline cognitive functioning could be associated to different individual characteristics and different trends of functioning across the cognitive rehabilitation process with CoRe. Identifying such predictors of responsiveness could promote to the development of a “personalized” or “stratified” precision medicine in the context of the broad spectrum of CT interventions. To this end, we used an unsupervised clustering technique in order to find subgroups of patients with different cognitive profiles and study how these clusters respond to the cognitive treatment. Cognitive profiles were developed by considering participants’ global cognition and processing speed (PS).</p> <p>Fifty-seven subjects underwent to a computer-based cognitive training (CCT) for 3 weeks and were evaluated at baseline (T0), post-intervention (T1), and after 6 (T2) and 12 (T3) months. Clusters of cognitive profiles were explored with k-means analysis. The analysis revealed two clusters, which were composed by 27 and 30 patients characterized by lower (Cluster 1) and higher (Cluster 2) cognitive functioning. At T1, cognitive performance improved in both groups, but Cluster 1 gained more benefits in global cognitive functioning than Cluster 2. However, at T3, Cluster 2 remained stable in its clinical condition, whereas Cluster 1 showed a pronounced worsening. In conclusion, Cluster 1 profile was associated with a more marked but also short-lasting responsiveness to CCT, whereas patients fitting with Cluster 2 characteristics seemed to obtain more CCT benefits in terms of stability or even delay of cognitive/functional decline. These findings may have relevant implications in informing the timing and modality of delivery of CCT.</p> <p> </p> <p> </p>
Dataset related to the article "Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"
<p>This record contains raw data related to the article “Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"</p> <p><strong>Purpose: </strong>To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.</p> <p><strong>Methods: </strong>One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DL<sub>rest</sub>) and stress dataset (CTP-DL<sub>stress</sub>) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DL<sub>rest</sub>, and CCTA + CTP-DL<sub>stress</sub> was measured and compared. The time of analysis for CTP stress, CTP-DL<sub>rest</sub>, and CTP-DL<sub>Stress</sub> was recorded.</p> <p><strong>Results: </strong>Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTP<sub>Stress</sub> were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DL<sub>rest</sub> and CCTA + DL<sub>stress</sub> were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DL<sub>Rest</sub>, and CCTA + CTP-DL<sub>Stress</sub> significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).</p> <p><strong>Conclusion: </strong>Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress<sub>.</sub>.</p>
Looking at One's Self Through Facebook Increases Mental Stress: A Computational Psychometric Analysis by Using Eye-Tracking and Psychophysiology
<p>The aim of this study was to investigate if Mental Stress was superior, inferior, or equal navigating on Facebook own profile or others profiles. An experimental manipulation would invalidate the results since it would force the participants to navigate in only one condition each time. To overcome this problem, we used an eye-tracker to get clear time markers that identified the areas where the participants focused during all of the Facebook navigation. While the gazes were being recorded for 30 participants, we simultaneously recorded their psychophysiological signals, which were extracted and paired with each specific focus area. Consequently, we obtained the psychophysiological correlates of Facebook navigation for both the conditions related to “own” and “others.” The areas related to own were about the own profile (such as exploring and focusing on one's own information, posting one's own news, and similar activities). The areas related to others were about Facebook friends (e.g., exploring others' profiles and reading comments). The results showed that, based on cardiovascular measures (strong measurements of psychological stress), looking at one's own profile increased mental stress level. Bayesian analyses showed that these differences between the two conditions were not due to the cognitive load or the different attentional and emotional content in the two conditions. The study posed new questions about the expression of one's self to others, and indicated potential detrimental effects of chronic stress deriving from being more oriented to the self than the others.</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.