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About this paper
D. B. Keator, K. Helmer, J. Steffener, J. A. Turner, T. G. M. Van Erp, S. Gadde, N. Ashish, G. A. Burns and 2 more
A four-component framework is presented for the structured sharing of raw and derived neuroimaging data, directly addressing limitations in current meta-data and provenance documentation across existing resources. The work develops a formal Neuroimaging Data Model (NI-DM) by integrating with the W3C PROV-DM, curates a structured terminology, defines a web service API, and provides provenance libraries with demonstrated integration into neuroimaging analysis tools.
Key insights
- Harmonizing neuroimaging data structures with a generic provenance model like W3C PROV-DM effectively bridges project, workflow, and derived data dimensions, enabling complex semantic queries.
- A community-driven, collaborative approach, exemplified by the Derived Data Working Group and its engagement with major software developers, is essential for promoting the widespread adoption of data sharing standards.
- Explicitly capturing comprehensive provenance for derived data is critical for validating findings, ensuring research reproducibility, and laying the groundwork for advanced knowledge engineering in neuroscience.
Summary by alphaXiv.
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