What transforms project-specific code into sustainable scientific software? Principles for developing reusable open-source research tools

Abstract

Computational research increasingly depends on custom software, yet much of this software begins as project-specific analysis code written to answer one immediate scientific question. Such code can be appropriate and productive in its original context, but it often becomes fragile when it is reused, shared, cited, maintained, or extended by others. In this perspective, we frame the transition from project-specific analysis code to sustainable scientific software as a deliberate development process rather than an automatic consequence of increasing code size, algorithmic sophistication, or wider use. This transition depends on development decisions that make computational work reproducible, inspectable, reusable, and creditable. We propose seven principles for sustainable research software: modular design, version control, automated testing, documentation, public availability and open licensing, persistent citation, and transparent development. These principles are most effective when considered from the beginning of a project, but they can also be introduced retrospectively as part of software maturation. We discuss how the meaning of code changes when it becomes shared scientific infrastructure, why AI-assisted and agentic coding require accountable development practices, and why research software should be understood as a core topic in research data management. Finally, we illustrate how the principles can be implemented in practice through a pseudo-code example. Sustainable scientific software is not merely cleaner code. It is research infrastructure that can be inspected, reused, tested, cited, and maintained.

Acknowledgments

This work was prepared as a contribution to Great Data, Great Science – The 2026 TRA Workshop on Data Management and Data Science Across Disciplines, held on 6 October 2026 in Bonn, Germany.