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Found 4 result(s)

PAIN repository

Pain and Interoception Imaging Network Repository

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The PAIN Repository is a recently funded NIH initiative, which has two components: an archive for already collected imaging data (Archived Repository), and a repository for structural and functional brain images and metadata acquired prospectively using standardized acquisition parameters (Standardized Repository) in healthy control subjects and patients with different types of chronic pain. The PAIN Repository provides the infrastructure for storage of standardized resting state functional, diffusion tensor imaging and structural brain imaging data and associated biological, physiological and behavioral metadata from multiple scanning sites, and provides tools to facilitate analysis of the resulting comprehensive data sets.

InCHIANTI

The InCHIANTI Study

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<<<!!!<<< This repository is no longer available. >>>!!!>>>

UCSD Metabolomics Workbench

Metabolomics Workbench

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With the creation of the Metabolomics Data Repository managed by Data Repository and Coordination Center (DRCC), the NIH acknowledges the importance of data sharing for metabolomics. Metabolomics represents the systematic study of low molecular weight molecules found in a biological sample, providing a "snapshot" of the current and actual state of the cell or organism at a specific point in time. Thus, the metabolome represents the functional activity of biological systems. As with other ‘omics’, metabolites are conserved across animals, plants and microbial species, facilitating the extrapolation of research findings in laboratory animals to humans. Common technologies for measuring the metabolome include mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), which can measure hundreds to thousands of unique chemical entities. Data sharing in metabolomics will include primary raw data and the biological and analytical meta-data necessary to interpret these data. Through cooperation between investigators, metabolomics laboratories and data coordinating centers, these data sets should provide a rich resource for the research community to enhance preclinical, clinical and translational research.

PhysioNet

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Modern signal processing and machine learning methods have exciting potential to generate new knowledge that will impact both physiological understanding and clinical care. Access to data - particularly detailed clinical data - is often a bottleneck to progress. The overarching goal of PhysioNet is to accelerate research progress by freely providing rich archives of clinical and physiological data for analysis. The PhysioNet resource has three closely interdependent components: An extensive archive ("PhysioBank"), a large and growing library of software ("PhysioToolkit"), and a collection of popular tutorials and educational materials