NMRhub: An NMR Data Ecosystem Spanning the Complete Data Lifecycle.

Area of Science:

• Biomolecular NMR spectroscopy
• Data science
• Computational chemistry

Background:

• Reproducible science faces challenges in data provenance, annotation accuracy, and data federation.
• Existing systems struggle to maintain data integrity from generation to publication.
• Machine learning (ML) offers potential for unlocking knowledge but requires integrated data.

Purpose of the Study:

• To introduce NMRhub, an integrated ecosystem designed to address challenges in NMR data management and facilitate ML applications.
• To link the NSF Network for Advanced NMR (NAN), NMRbox, and the Biological Magnetic Resonance Data Bank (BMRB) for a unified data lifecycle.
• To enhance data accessibility, analysis, and discovery through a centralized platform.

Main Methods:

• Automated data harvesting and archiving via NAN from spectrometers.
• Virtualization and archiving of analysis software environments with high-performance computing via NMRbox.
• Curated data deposition, annotation, and persistent identification by BMRB.
• Integration of platforms with single sign-on, ML-enhanced search, and data transfer tools.

Main Results:

• NMRhub provides a seamless ecosystem spanning the complete NMR data lifecycle, from instrument to deposition.
• Facilitates automated data capture, secure archiving, and robust analysis environments.
• Enables ML-enhanced discovery across linked NMR data resources and external scientific databases.

Conclusions:

• NMRhub significantly advances reproducible science by ensuring data provenance and accessibility.
• The integrated platform empowers researchers to leverage ML for deeper insights from NMR data.
• NMRhub establishes a sustainable framework for managing and utilizing biomolecular NMR data.