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An Integrated Toolkit for Extensible and Reproducible Neuroscience.

Jordan K Matelsky, Luis M Rodriguez, Daniel Xenes

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    Large neuroimaging datasets require advanced computer science for analysis. We introduce intern, a standardized interface to unify data backends and simplify complex data management for scientists.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Data Science

    Background:

    • Neuroimaging datasets are rapidly increasing in size and complexity.
    • Existing data standards and ecosystems present challenges for handling large datasets that exceed RAM.
    • Leveraging research investments in data infrastructure is hindered by heterogeneous systems.

    Purpose of the Study:

    • To propose a unified approach for managing large neuroimaging datasets.
    • To abstract complex computer science challenges from neuroscientists.
    • To facilitate easier access, processing, and sharing of neuroimaging data.

    Main Methods:

    • Developing standardized interfaces to unify diverse computational backends.
    • Designing desirable patterns for data management systems.
    • Introducing 'intern' as a reference implementation.

    Main Results:

    • A framework for abstracting computer science complexities in neuroimaging data analysis.
    • A reference implementation, 'intern', demonstrating the proposed approach.
    • Simplified data handling for datasets not fitting into RAM.

    Conclusions:

    • Standardized interfaces can overcome barriers in neuroimaging data management.
    • 'intern' provides a unified solution for heterogeneous computational backends.
    • This approach empowers scientists to focus on research rather than data infrastructure.