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24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “real-time dynamics”
Real-Time Dynamic Imaging Study of Normal Pharyngolaryngeal Structures During Barium Swallow (GLAM-III)
ClinicalTrials.gov study NCT07264322. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of Short-term Insulin Intensive Therapy Based on the Application of Insulin Pump and Real-time Dynamic Glucose Monitoring Technology on Reversing the Newly Diagnosed Type 2 Diabetes
ClinicalTrials.gov study NCT06127433. IPD Sharing: YES. Countries: 1. Publications: 0.
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO* Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity research community. The current research in structural health monitoring (SHM) is to provide continuous (or on demand) information about the state of a structure. The SHM system can be based on either active or passive sensor measurements. Though the current research on ultrasonic wave propagation based active sensing approach has the potential to estimate very small damage, it has severe drawbacks in terms of low sensing radius and external power requirements. To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently, few efforts have been made towards, time-series fatigue damage state estimation over the entire fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques demonstrates the clear trend in damage growth during the final failure regime (stage-III regime) or during when the damage is comparatively large enough. The present paper proposes a passive sensing technique that demonstrates a clear trend in damage growth almost over the entire stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks that can estimate the time-series fatigue damage state under random loading is proposed. For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the reference condition dynamic strain at a given instant of time. The predicted strains are compared with the actual sensor measurements to estimate the corresponding error signals. The error signals estimated at two different locations are correlated to estimate the corresponding fatigue damage state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with biaxial random loading.
Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device
<p>Supplementary data and code to accompany "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</p>
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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.