Assessment of the impact of shared brain imaging data on the scientific literature

Michael P. Milham (), R. Cameron Craddock, Jake J. Son, Michael Fleischmann, Jon Clucas, Helen Xu, Bonhwang Koo, Anirudh Krishnakumar, Bharat B. Biswal, F. Xavier Castellanos, Stan Colcombe, Adriana Di Martino, Xi-Nian Zuo and Arno Klein
Additional contact information
Michael P. Milham: Child Mind Institute
R. Cameron Craddock: Child Mind Institute
Jake J. Son: Child Mind Institute
Michael Fleischmann: Child Mind Institute
Jon Clucas: Child Mind Institute
Helen Xu: Child Mind Institute
Bonhwang Koo: Child Mind Institute
Anirudh Krishnakumar: Child Mind Institute
Bharat B. Biswal: New Jersey Institute of Technology
F. Xavier Castellanos: Nathan S. Kline Institute for Psychiatric Research
Stan Colcombe: Nathan S. Kline Institute for Psychiatric Research
Adriana Di Martino: Hassenfeld Childrenʼs Hospital at NYU Langone
Xi-Nian Zuo: University of Chinese Academy of Sciences (CAS)
Arno Klein: Child Mind Institute

Nature Communications, 2018, vol. 9, issue 1, 1-7

Abstract: Abstract Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally.

Date: 2018
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DOI: 10.1038/s41467-018-04976-1

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