Best practices in software citation

2026-09-30

AI Summary

This study addresses the challenges of reproducibility and missing academic attribution caused by non-standardized, machine-unreadable software citations. Through community workshop analysis, stakeholder responsibility mapping, and evaluation of existing infrastructure, it systematically examines cultural barriers, standardization requirements, and technical gaps. The research proposes an intervention strategy that synergistically advances both technical and cultural dimensions, identifying journal editors as the highest-leverage stakeholders while advocating for streamlined workflows and clearly defined responsibilities. Ultimately, this work constructs a best-practice framework for software citation, promoting the establishment of clear community norms and improved technical processes to provide a systematic solution for enhancing scientific reproducibility.

Abstract

Software is both a foundational tool and a primary output of modern computational research, yet citation practices for software remain inconsistent, incomplete, and rarely machine-actionable. Existing infrastructure designed for paper and data citation does not adequately serve the distinct needs of software citation, leaving a gap that impedes reproducibility, misattributes scholarly credit, and obscures the labor embedded in research pipelines. Drawing on a NASA-funded community workshop held in April 2026, we present an analysis of four interconnected themes: (I)~the cultural barriers to consistent citation practice; (II)~the need for clearer community norms and conventions; (III)~gaps in existing technical infrastructure and workflow; and (IV)~the emerging challenges posed by AI-assisted research. For each theme we identify targeted interventions and assign responsibility across stakeholder groups. We conclude that meaningful progress requires simultaneous action on technical and cultural fronts. Journal editors and publishers represent the single highest-leverage point for accelerating this change, and correct citation must become the path of least resistance within researchers' existing workflows.

Problem

• software citation
• reproducibility
• scholarly credit
• research infrastructure
• AI-assisted research

Innovation

• Software Citation
• Machine-actionable Infrastructure
• AI-assisted Research
• Research Workflows
• Reproducibility