Seminar: 50th Anniversary Lecture Series

Abstract

    Computational reproducibility is achieved when a computational result can be recomputed using the same code and data as in the original computation.  Computational reproducibility has become a central requirement in many scientific fields, due to the increasing reliance of experimental sciences toward digital data. This seminar will introduce computational reproducibility, present technical solutions currently available to achieve it, highlight remaining issues, and illustrate it with examples from the neuroimaging domain.

Bio

Tristan Glatard is Associate Professor in the Department of Computer Science and Software Engineering, Canada Research Chair (Tier II) in Big Data Infrastructures for Neuroinformatics, and co-director of the Concordia Applied AI Institute.