Computational and informatics advances for reproducible data analysis in neuroimaging
It tackles reproducibility issues for neuroimaging researchers, offering a model that could benefit other scientific fields.
The paper addresses the reproducibility crisis in neuroimaging by proposing an ecosystem based on openness, transparency, and tools like data standards and open-source software, resulting in improved sharing and reuse of large datasets.
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.