Making data sharing count: a publication-based solution.

Area of Science:

• Neuroscience
• Data Science
• Scientific Publishing

Background:

• Open data sharing is increasingly expected in neuroimaging research.
• Individual researchers lack clear incentives for sharing their data, despite its importance in large consortia.
• Existing data publication models in other fields provide a precedent for crediting and validating datasets.

Purpose of the Study:

• To address the need for incentivizing data sharing in the neuroimaging community.
• To explore the adaptation of the 'data paper' concept for neuroimaging datasets.
• To propose guidelines for structuring and reviewing neuroimaging data papers.

Main Methods:

• Discussion of challenges specific to neuroimaging data.
• Analysis of successful data publication models in other scientific fields.
• Development of proposed guidelines for data paper structure and peer review.

Main Results:

• Identification of the lack of clear incentives for individual researchers to share neuroimaging data.
• Recognition of the benefits of data papers, including citable credit and quality assurance via peer review.
• Formulation of specific recommendations for implementing data papers in neuroimaging.

Conclusions:

• Data papers can effectively incentivize neuroimaging data sharing by providing academic credit.
• Peer review of data papers ensures the quality and reliability of published neuroimaging datasets and metadata.
• Guidelines are proposed to facilitate the adoption and standardization of data papers within the neuroimaging community.