Making big data open: data sharing in neuroimaging

By Russell A. Poldrack, Krzysztof J. Gorgolewski. Nature Neuroscience, 2014

421 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2b78b4cc963b81d114814d9d3e474232a3c35cd8

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In Cognitive Neuroscience

In Health, Toxicology and Mutagenesis

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “Second, as enthusiasm about “big data” (Poldrack and Gorgolewski, 2014) and data-driven analysis approaches has grown, sample sizes have increased while effect sizes have decreased.”
The OpenNeuro resource for sharing of neuroscience data (2021, positive): “Discussions of ‘big data’ in neuroimaging ( Poldrack and Gorgolewski, 2014 ; Smith and Nichols, 2018 ) have largely focused on datasets including large numbers of individuals.”
Machine Learning for Precision Psychiatry: Opportunities and Challenges. (2017, background): “While some investigators deem restricted access to research data unethical, data sharing also invokes privacy concerns (46), such as recognizing a participant’s face in anatomical brain scans.”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “Reusing shared data across investigators can increase sample sizes while keeping bounds on data-acquisition costs (Poldrack and Gorgolewski, 2014).”
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015, positive): “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.”
Network neuroscience (2017, neutral): “The size and complexity of these data require sophisticated strategies for statistical inference and dimensionality reduction 7 and present important challenges for scalable computing 8 , data sharing, and reproducibility 9 .”