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

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.Data sharing is recognized as a way to promote scientific collaboration and reproducibility, but some are concerned over whether research based on shared data can achieve high impact. Here, the authors show that neuroimaging papers using shared data are no less likely to appear in top-ranked journals.

[1]  Oluwasanmi Koyejo,et al.  Toward open sharing of task-based fMRI data: the OpenfMRI project , 2013, Front. Neuroinform..

[2]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[3]  Michael W. Weiner,et al.  Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014 , 2015, Alzheimer's & Dementia.

[4]  M. Milham,et al.  The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience , 2012, Front. Syst. Neurosci..

[5]  Henk F. Moed,et al.  Citation Analysis in Research Evaluation , 1899 .

[6]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[7]  Richard Van Noorden Controversial impact factor gets a heavyweight rival , 2016, Nature.

[8]  Xi-Nian Zuo,et al.  A Connectome Computation System for discovery science of brain , 2015 .

[9]  Virginia Gewin,et al.  Data sharing: An open mind on open data , 2016, Nature.

[10]  Empty rhetoric over data sharing slows science , 2017, Nature.

[11]  Yufeng Zang,et al.  Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes , 2013, NeuroImage.

[12]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[13]  B J C Perera Data sharing: Some points of view for scrutiny , 2017 .

[14]  Vince D. Calhoun,et al.  The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository , 2016, NeuroImage.

[15]  Dimitris Samaras,et al.  Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.

[16]  J. Drazen,et al.  Data Sharing. , 2016, The New England journal of medicine.

[17]  Li Qingyang,et al.  Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC) , 2013 .

[18]  Thomas E. Nichols,et al.  Can parametric statistical methods be trusted for fMRI based group studies? , 2015, 1511.01863.

[19]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[20]  N. Volkow,et al.  Functional connectivity density mapping , 2010, Proceedings of the National Academy of Sciences.

[21]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[22]  Roland N. Boubela,et al.  Fully exploratory network independent component analysis of the 1000 functional connectomes database , 2012, Front. Hum. Neurosci..

[23]  Bryn Nelson Data sharing: Empty archives , 2009, Nature.

[24]  Bharat B. Biswal,et al.  Making data sharing work: The FCP/INDI experience , 2013, NeuroImage.

[25]  Michael S. Gazzaniga,et al.  Why share data? Lessons learned from the fMRIDC , 2013, NeuroImage.

[26]  Farid Neema,et al.  Data sharing , 1998 .

[27]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[28]  Marcia McNutt,et al.  Data sharing , 2016, Science.

[29]  R. Poldrack,et al.  The publication and reproducibility challenges of shared data , 2015, Trends in Cognitive Sciences.

[30]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

[31]  T L Spires-Jones,et al.  Opening up: open access publishing, data sharing, and how they can influence your neuroscience career , 2016, The European journal of neuroscience.

[32]  Richard van Noorden Controversial impact factor gets a heavyweight rival , 2016, Nature.

[33]  Hans Knutsson,et al.  Correction for Eklund et al., Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[34]  Bing Chen,et al.  An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.

[35]  Krzysztof J. Gorgolewski,et al.  Making Data Sharing Count: A Publication-Based Solution , 2012, Front. Neurosci..

[36]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.