Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC)

COINSTAC provides a platform to analyze data stored locally across multiple organizations without the need for pooling the data at any point during the analysis. It is intended to be an ultimate one-stop shop by which researchers can build any statistical or machine learning model collaboratively in a decentralized fashion. This framework implements a message passing infrastructure that will allow large scale analysis of decentralized data with results on par with those that would have been obtained if the data were in one place.

Project Author(s)

Sergey M. Plis; Anand D. Sarwate; Dylan Wood; Christopher Dieringer; Drew Landis; Cory Reed; Sandeep R. Panta; Jessica A. Turner; Jody M. Shoemaker; Kim W. Carter; Paul Thompson; Kent Hutchison; Vince D. Calhoun

Project Links

https://github.com/trendscenter/coinstac

This post was automatically generated by Kelly Rootes-Murdy