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Dataset results
3,690 results for “Computing”
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data Set from the study Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Raw data for "Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm": unpublished figure and source data.
<p>Classification Performances for each stroke participant according to the GUIDER and the MANUAL procedure. </p>
Raw data for "Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface": unpublished figures and source data.
<p>Figures 1-4: Selected features (EEG-EMG and EMG-EMG couples) for movement vs rest classification (Ext R and L, Gra R and L). </p> <p>Figure 5: Distribution of Gra vs Ext classification among bands. </p> <p>Source data. </p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
3D Reconstruction of a Calcified Aortic Valve Cusp Based on Micro-Computed Tomography.
<p>Video showing 3D reconstruction of a calcified aortic valve cusp based on micro-computed tomography.</p>
How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks_data
<p>The data set includes the coding scheme, results of intercoder reliability tests and results of statistical tests supporting the paper " How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks "</p>
Elucidating the spectroscopic and physicochemical properties for a pH-dependent glyphosate structure from a computational perspective
<p>Input and output library of the article.</p>
Data associated to the manuscript "Size Matters: A computational study of hydrogen absorption in ionic liquids"
<p>Contains input files and analysis scripts used in the article: "Size Matters: A computational study of hydrogen absorption in ionic liquids"</p> <p> </p> <p> </p>
Center for Computational and Genomic Medicine Analytic Tools
The Center for Computational and Genomic Medicine, established in 2018 with the recruitment of Yi Xing, PhD, drives biological discoveries and medical innovations by integrating genomics, big data, and computing. The Center contributes to CHOP's entire ecosystem of research, including collaborations with other key Centers of Emphasis, including the Center for Cellular and Molecular Therapeutics and the Department of Biomedical and Health Informatics. The Center provides an intellectual home for recruiting tenure track faculty who are not only adept at using existing technology, but also can develop new genomic technologies or computational tools that can be offered to a broader community of scientists. The Center has hybrid character - computational biology research will pave the way for new projects in wet laboratories. Under Dr. Xing's direction, the Center aims to serve as an engine for technological and biomedical innovation, providing tight interaction from both the basic science and technology side, as well as the translational and clinical side of genomics and computational biology.
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.