Open Reproducible Neuroscience Research on Cloud with Infrastructure as Code

Abstract

Reproducibility is a key component of scientific research, and its significance has been increasingly recognized in the field of Neuroscience. This paper explores the origin, need, and benefits of reproducibility in Neuroscience research, as well as the current landscape surrounding this practice, and further adds how boundaries of current reproducibility should be expanded to computing infrastructure. The reproducibility movement stems from concerns about the credibility and reliability of scientific findings in various disciplines, including Neuroscience. The need for reproducibility arises from the importance of building a robust knowledge base and ensuring the reliability of research findings. Reproducible studies enable independent verification, reduce the dissemination of false or misleading results, and foster trust and integrity within the scientific community. Collaborative efforts and knowledge sharing are facilitated, leading to accelerated scientific progress and the translation of research into practical applications. On the data front, we have platforms such as openneuro for open data sharing, on the analysis front we have containerized processing pipelines published in public repos which are reusable. There are also platforms such as openneuro, NeuroCAAS, brainlife etc which caters to the need for a computing platform. However, along with benefits these platforms have limitations as only set types of processing pipelines can be run on the data. Also, in the world of data integrity and governance, it may not be far in the future that some countries may require to process the data within the boundaries limiting the usage of the platform. To introduce customized, scalable neuroscience research, alongside open data, containerized analysis open to all, we need a way to deploy cloud infrastructure required for the analysis with templates. These templates are a blueprint of infrastructure required for reproducible research/analysis in a form of code. This will empower anyone to deploy computational infrastructure on cloud and use data processing pipeline on their own infrastructure of their choice and magnitude. Just as docker files are created for any analysis software developed, an IAC template accompanied with any published analysis pipeline, will enable users to deploy infrastructure on cloud required to carry out analysis on their data.