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TheHiveDB image data management and analysis framework.

J-Sebastian Muehlboeck1, Eric Westman2, Andrew Simmons3

  • 1Department of Neuroimaging, Institute of Psychiatry, King's College London London, UK ; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet Stockholm, Sweden ; J-S Muehlboeck Inc., Montreal QC, Canada.

Frontiers in Neuroinformatics
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

The hive database system (theHiveDB) streamlines brain imaging research by managing diverse data and processing workflows. This collaborative platform supports multi-center studies and integrates various data types for comprehensive analysis.

Keywords:
data managementdata queryimage processingneuroimaging collaboration and workflowsneuroimaging database frameworkquery interfaceweb 2.0 application

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Area of Science:

  • Neuroscience
  • Bioinformatics
  • Data Management

Background:

  • Managing complex, multi-center brain imaging studies presents significant data integration and workflow challenges.
  • Existing systems often struggle to handle heterogeneous data types (imaging, clinical, genetic) and cross-sectional/longitudinal designs.
  • Researchers require a unified platform to guide the entire research process, from data organization to analysis.

Purpose of the Study:

  • To introduce the hive database system (theHiveDB) as a comprehensive solution for brain imaging database management, collaboration, and activity tracking.
  • To demonstrate theHiveDB's capability in handling cross-sectional and longitudinal multi-center studies.
  • To showcase the integration of diverse data modalities and the scheduling of image processing workflows.

Main Methods:

  • TheHiveDB is a web-based system designed for organizing and integrating heterogeneous data from various projects.
  • It incorporates an activity management system for scheduling image processing on private and cloud resources, including established pipelines like Freesurfer.
  • Algorithm developers can provide access to virtual machines hosting their tools for collaborators and the wider community.

Main Results:

  • TheHiveDB effectively organizes, processes, and analyzes brain imaging data, as illustrated by a use case with the Alzheimer Disease Neuroimaging Initiative.
  • It facilitates collaboration by providing a centralized platform for data and computational resources.
  • The system supports common image archival, management tasks, and pipeline processing.

Conclusions:

  • The hive database system (theHiveDB) offers a robust solution for managing complex brain imaging research workflows.
  • It enhances collaboration and data integration across multi-center studies and diverse data types.
  • TheHiveDB empowers researchers and algorithm developers by providing a flexible and scalable platform for neuroimaging research.