Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution

Area of Science:

• Neuroimaging
• Data Science
• Scientific Publishing

Background:

• Data sharing and reuse are crucial but hindered by a lack of standardized citation methods.
• Tracking data usage and impact is difficult without consistent attribution.
• Neuroimaging research requires robust methods for dataset identification, citation, and aggregation.

Purpose of the Study:

• To develop a system for clear identification and citation of neuroimaging datasets.
• To enable the creation of aggregate datasets from multiple sources while maintaining provenance.
• To facilitate the tracking of data reuse at the individual data level.

Main Methods:

• Integration of Digital Object Identifiers (DOI) and a standardized metadata schema.
• Implementation within a XNAT-based repository workflow.
• Development of group-level DOIs for aggregated datasets inheriting constituent data attributes.

Main Results:

• A functional prototype system for data identification at project and image levels.
• Ability to assign group-level DOIs to combined datasets from multiple sources.
• Enables tracking of data reuse down to the individual image level.

Conclusions:

• Standardized data identification and attribution are essential for practical and advantageous data citation.
• The developed system addresses critical needs in neuroimaging data sharing and reuse.
• Widespread adoption of such systems would benefit researchers, data repositories, and publishers.