Data sharing in neuroimaging research

By Jean-Baptiste Poline, J. Breeze, Satrajit S. Ghosh, Krzysztof J. Gorgolewski, Yaroslav O. Halchenko, Michael Hanke, C. Haselgrove, Karl G. Helmer, D. Keator, Daniel S. Marcus and 4 more. Front. Neuroinform., 2012

296 citations recorded by the source corpus.

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/f5158aea5fb271e83d76a3eaa497bdbde3b23daf

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Citations in the corpus

Open is Not Enough. Let's Take the Next Step: An Integrated, Community-Driven Computing Platform for Neuroscience (2012, background): “…one solution has been clearing houses, such as the INCF Software Center 1 , and the NITRC portal, which facilitate efforts of peer-to-peer software and data sharing that were previously limited to only well-funded formal consortia (see, Poline et al., 2012, for a recent summary of the status quo ).”
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015, unknown): “Through the introduction of international data sharing initiatives in the neuroimaging field [6, 7, 8, 9, 10, 11, 12], it has become possible to evaluate the statistical methods using real data.”
Making big data open: data sharing in neuroimaging (2014, methodology): “We will focus particularly on sharing task-based fMRI data, which poses the greatest challenges because of the complex metadata needed for analysis; for overviews of data sharing in neuroimaging, see recent papers 2–4 .”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014, unknown): “New ways of acknowledging data acquisition are being developed concurrently (Poline et al. 2012).”
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2014, background): “Even though a single study can take years to conduct, cost hundreds of thousands of dollars, and require the eﬀort of dozens of highly trained scientists and volunteers, the output is usually reduced to an academic article, and the original data are rarely shared (Poline et al., 2012).”
Toward open sharing of task-based fMRI data: the OpenfMRI project (2013, background): “The open sharing of fMRI data has the potential to revolutionize cognitive neuroscience in much the same way (Van Horn and Gazzaniga, 2002; Poline et al., 2012).”
Psychological measurement and the replication crisis: Four sacred cows. (2020, background): “Fortunately, the neuroscience and infancy literatures are beginning to grapple with the challenges posed by low-powered studies by developing collaborative protocols shared across multiple laboratories (e.g., the ManyBabies Project; Frank et al., 2017; Poline et al., 2012).”
The OpenNeuro resource for sharing of neuroscience data (2021, unknown): “Starting with early pioneering efforts by Gazzaniga and Van Horn to establish an fMRI Data Center in 1999 ( Van Horn and Gazzaniga, 2013 ), data sharing has become well established in the domain of neuroimaging ( Milham et al., 2018 ; Poldrack and Gorgolewski, 2014 ; Poline et al., 2012 ).”
The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities (2024, unknown): “…ongoing conversations about how data sharing could be promoted and realized in the digital age ( Choud-hury et al., 2014 ; Insel et al., 2003 ; Poldrack and Poline, 2015 ; Poline et al., 2012 ; Sejnowski et al., 2014 ; Van Horn and Gazzaniga, 2002 ; Van Horn and Toga, 2009 ; Teeters et al., 2008 ).”