TY - JOUR T1 - Assessment of the impact of shared data on the scientific literature JF - bioRxiv DO - 10.1101/183814 SP - 183814 AU - Michael P. Milham AU - R. Cameron Craddock AU - Michael Fleischmann AU - Jake Son AU - Jon Clucas AU - Helen Xu AU - Bonhwang Koo AU - Anirudh Krishnakumar AU - Bharat B. Biswal AU - F. Xavier Castellanos AU - Stan Colcombe AU - Adriana Di Martino AU - Xi-Nian Zuo AU - Arno Klein Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/09/04/183814.abstract N2 - 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 (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used ‘pay to play’ models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally. ER -