Title: Computational and informatics advances for reproducible data analysis in neuroimaging,
Abstract: The reproducibility of scientific research has become a point of critical
concern. We argue that openness and transparency are critical for
reproducibility, and we outline an ecosystem for open and transparent science
that has emerged within the human neuroimaging community. We discuss the range
of open data sharing resources that have been developed for neuroimaging data,
and the role of data standards (particularly the Brain Imaging Data Structure)
in enabling the automated sharing, processing, and reuse of large neuroimaging
datasets. We outline how the open-source Python language has provided the basis
for a data science platform that enables reproducible data analysis and
visualization. We also discuss how new advances in software engineering, such
as containerization, provide the basis for greater reproducibility in data
analysis. The emergence of this new ecosystem provides an example for many
areas of science that are currently struggling with reproducibility.