CAREER: Advanced Containers for Reproducibility in Computational and Data Science

About This Grant

Reproducibility is essential for scientific progress and to establish trust in scientific results. Published computational results, increasingly, lack sufficient capture and description of companion information that enables subsequent confirmation and extension of results. This project will design and implement a novel container-based approach for sharing and reproducing scientific results. Reproducible containers developed from this project will package code, data, environment, provenance, and assumptions across heterogeneous computing platforms. In contrast to taking a "devops"-based approach, which burdens the user to manage reproducibility of experiments, this project uses reference executions of scientific experiments as a virtualization method for containerizing associated artifacts. While a container-based approach can help to verify repeat computations, further advancements in container technology are needed to enable advanced forms of reproducibility. This project aims to enable reproducibility even if computations include non-determinism and race conditions; code, data-sets, and parameters are changed; computations are performed on distributed platforms; and containers are shared with sensitive data and undocumented content. To that end, the project will develop an open-source container runtime that will offer primitives for enabling re-runnability, extensibility, and publish-ability of containers. The work leverages portable containers developed previously for computational sciences. This award will lay the foundation for an essential building block for establishing reproducibility of real-world computational and data science use cases. The project will increase awareness of the need for computational reproducibility tools through an integrated research and education plan involving scientists, students, and instructors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.