Data dictionary services in XNAT and the Human Connectome Project

Area of Science:

• Biomedical Informatics
• Neuroimaging Data Management
• Research Data Sharing

Background:

• XNAT is an open-source informatics platform widely used by biomedical imaging researchers.
• XNAT's extensible architecture allows custom data types but has limited capacity for broadcasting data meaning.
• Lack of standardized data meaning hinders interoperability between XNAT installations and other software.

Purpose of the Study:

• To implement a data dictionary service for the XNAT informatics platform.
• To provide a framework for defining data element relationships and metadata within XNAT.
• To enhance data interoperability and facilitate migration to standards-based formats.

Main Methods:

• Developed and implemented a data dictionary service integrated into XNAT.
• Defined metadata for data structures, including value types, templates, ranges, and field lists.
• Enabled conversion of XNAT data to Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML).

Main Results:

• The data dictionary service is operational on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website.
• Established a framework for defining relationships between core data, taxonomical structures, security, subject groups, and research protocols.
• Facilitated the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures.

Conclusions:

• The implemented data dictionary service significantly improves the management and sharing of biomedical imaging data within the XNAT ecosystem.
• Enhanced data standardization and interoperability promote seamless integration with other research data management services and standards.
• This advancement supports broader data accessibility and utilization in biomedical research, particularly in neuroimaging.