Fully synthetic neuroimaging data for replication and exploration.

ABSTRACT: Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were us

SUBMITTER: Vaden KI 

PROVIDER: S-EPMC7688496 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature