Reinterpretation and Long-Term Preservation of Data and Code

TL;DR

This paper emphasizes the importance of preserving experimental data and code for future scientific reinterpretation, highlighting current challenges and recommending increased dedicated funding and infrastructure development.

Contribution

It provides a comprehensive overview of the state of data and code preservation efforts and offers strategic recommendations for future support and infrastructure.

Findings

Preservation maximizes scientific return and enables future analyses.

Current infrastructure and funding are insufficient for long-term preservation.

Recommendations include increased funding and development of dedicated preservation programs.

Abstract

Careful preservation of experimental data, simulations, analysis products, and theoretical work maximizes their long-term scientific return on investment by enabling new analyses and reinterpretation of the results in the future. Key infrastructure and technical developments needed for some high-value science targets are not in scope for the operations program of the large experiments and are often not effectively funded. Increasingly, the science goals of our projects require contributions that span the boundaries between individual experiments and surveys, and between the theoretical and experimental communities. Furthermore, the computational requirements and technical sophistication of this work is increasing. As a result, it is imperative that the funding agencies create programs that can devote significant resources to these efforts outside of the context of the operations of individual major experiments, including smaller experiments and theory/simulation work. In this Snowmass 2021 Computational Frontier topical group report (CompF7: Reinterpretation and long-term preservation of data and code), we summarize the current state of the field and make recommendations for the future.