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Core principles for the implementation of the neurodata without borders data standard.

Marike L Reimer1, Lakshmi Bangalore2, Stephen G Waxman2

  • 1Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT 06510, USA; Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.

Journal of Neuroscience Methods
|November 6, 2020
PubMed
Summary

Integrating the Neurodata Without Borders (NWB) data standard with DataJoint database technology enhances neurophysiology data analysis and collaboration. This approach streamlines workflows and unlocks the potential of big data analytics for research.

Keywords:
Big dataData analyticsData scienceNeurodata without borders

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Area of Science:

  • Neuroscience
  • Data Science
  • Bioinformatics

Background:

  • The Neurodata Without Borders (NWB) data standard unifies diverse neurophysiology data modalities into a single format.
  • Integrating NWB with database technology enhances data integrity, collaboration, and the use of historical data.
  • NWB database technology underpins analytical systems at leading institutions like the Allen Institute and the International Brain Laboratory.

Purpose of the Study:

  • To demonstrate the benefits of incorporating NWB design principles into a big data analytics application.
  • To showcase the utility of combining NWB with DataJoint in a Jupyter-based electronic lab journal.
  • To highlight the analytical and collaborative advantages of adopting big data analytics.

Main Methods:

  • Implemented NWB in Jupyter notebooks using DataJoint to streamline database operations.
  • Converted open-field behavioral data (X, Y coordinates) to NWB format.
  • Developed additional notebooks for NWB file manipulation, data sharing, data integration, and retrospective analysis with query filtering.

Main Results:

  • Demonstrated the utility of combining NWB with DataJoint for processing behavioral data.
  • Showcased the creation and management of NWB files within a database framework.
  • Illustrated data sharing, integration of diverse data sources, and retrospective analysis capabilities.

Conclusions:

  • The joint use of NWB with DataJoint transforms laboratory datasets and workflows.
  • This integration significantly increases data analytical capabilities compared to NWB alone.
  • Jupyter notebooks facilitate the adoption of these big data analytics methods for various neurophysiology modalities.