SILS researchers collaborate on multi-university project to improve neuroscience data sharing

October 8, 2020

Given the significant time and resources needed for most neuroimaging research, scientists in the field could greatly benefit from reusing data collected by other scientists. Unfortunately, current databases are disconnected, incomplete, and often difficult to search.   

Arcot Rajasekar and Ray Wang
Arcot Rajasekar and Ray Wang of UNC SILS

Professor Arcot Rajasekar and Assistant Professor Yue (Ray) Wang from the UNC School of Information and Library Science (SILS) are working with researchers from across the country to solve this problem by creating a new data sharing infrastructure called NeuroBridge.

In August, the National Institute on Drug Abuse (NIDA) awarded over $432,000 in funding for the project. Lei Wang of Northwestern University’s Feinberg School of Medicine, Jessica Turner from Georgia State University, José Luis Ambite from the University of Southern California, and Rajasekar will lead the multi-site collaboration as principal investigators.

The research team will include Ray Wang from SILS, Howard Lander and Hao Xu from the Renaissance Computing Institute (RENCI), and Daniel Marcus from Washington University.

While large amounts of data are available through different neuroimaging databases, these systems generally do not communicate with each other. Moreover, vast amounts of neuroimaging data are collected in hundreds of laboratories each year but not archived, though many of these datasets are described in journal publications. These underutilized data are part of the “long tail of science.”

“Not only do neuroscientists need a resource that is more comprehensive, but they also need one that will efficiently allow them to identify the most useful datasets available, ones that align with their own experimental methods, subjects, and other criteria,” said Ray Wang.

The NeuroBridge project will develop a user-friendly web portal, a mediator and text miner for generating metadata from databases and journals, and a data bridge for processing metadata to help assign rank and relevance for searches. To test the new system, the team will extend an existing collaboration with the XNAT platform on a use case of schizophrenia and related disorders.

NeuroBridge builds on the success of two previous projects, DataBridge for Neuroscience and SchizConnect.