Sharing Heterogeneous Data: The National Database for Autism Research

Abstract

The National Database for Autism Research (NDAR) is a secure research data repository designed to promote scientific data sharing and collaboration among autism spectrum disorder investigators. The goal of the project is to accelerate scientific discovery through data sharing, data harmonization, and the reporting of research results. Data from over 25,000 research participants are available to qualified investigators through the NDAR portal. Summary information about the available data is available to everyone through that portal.

See without looking: joint visualization of sensitive multi-site datasets

“…Fortunately, in neuroimaging not all data is private and unshareable. Public repositories of MRI data are popular and they provide diverse datasets for analyses (Castellanos et al, 2013;Hall et al, 2012;Ivory, 2015).…”

RD-Connect: An Integrated Platform Connecting Databases, Registries, Biobanks and Clinical Bioinformatics for Rare Disease Research

“…This allows data on a single individual to be accumulated across projects over time, regardless of where and when the data was collected, and enables researchers to define a study population using data collected in different centers. In the United States, the National Institutes of Health (NIH)-funded National Database for Autism Research (NDAR) 25 has developed an identification system that is now being extended under the auspices of the NIH Office of Rare Diseases Research for use in linking patient clinical information with biospecimens. Similarly, a unique identifier for Huntington's Disease patients taking part in international research studies has been developed.…”

PRN: a preprint service for catalyzing R-fMRI and neuroscience related studies

“…A subfield of neuroscience, neuroimaging, especially that which focuses on resting-state functional magnetic resonance imaging (R-fMRI), has emerged as field which is embracing innovations such as open data sharing (e.g., ADHD-200-Consortium, 2012; Biswal et al , 2010; Di Martino et al , 2014; Hall et al , 2012; Mennes et al , 2013; Milham, 2012; Mueller et al , 2005; Satterthwaite et al , 2014; Van Essen et al , 2013; Zuo et al , 2014), open software sharing (e.g., Bellec et al , 2012; Rubinov & Sporns, 2010; Sikka et al , 2014; Song et al , 2011; Taylor & Saad, 2013; Whitfield-Gabrieli & Nieto-Castanon, 2012; Xia et al , 2013; Chao-Gan & Yu-Feng, 2010; Zang et al , 2012; Zuo & Xing, 2014) and sharing of learning resources (e.g., Training Course in fMRI ( http://sitemaker.umich.edu/fmri.training.course) and The R-fMRI Course ( http://rfmri.org/Course)). As a method to investigate ongoing brain activity in basic, translational and clinical neuroscience studies, R-fMRI has become an increasingly prevalent research area especially in recent years ( Fornito & Bullmore, 2012; Fox & Raichle, 2007; Kelly et al , 2012; Van Dijk et al , 2010) considering its sensitivity to characterize developmental, aging and pathological features ( Andrews-Hanna et al , 2007; Fair et al , 2008; Greicius, 2008; Zuo et al , 2010), subject-friendly data collection procedures in clinical samples, and high comparability and consistency across studies and sites ( ADHD-200-Consortium, 2012; Biswal et al , 2010; Mennes et al , 2013; Tomasi & Volkow, 2012).…”