Best practices in software citation
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.
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BibTeX
@misc{vanlane2026best,
title = {{Best practices in software citation}},
author = {Phil R. Van-Lane and Floor S. Broekgaarden and Daniel S. Katz and Bhavesh Patel and Pengyin Shan and Jonathan Starr and Samantha Teplitzky and Peter K. G. Williams and Alice Allen and Lucas M. de Sá and Andrew Fullard and Sandra Gesing and Tom Wagg and Andrea Zonca},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00528},
eprint = {2610.00528},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00528},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Best practices in software citation
AU - Phil R. Van-Lane
AU - Floor S. Broekgaarden
AU - Daniel S. Katz
AU - Bhavesh Patel
AU - Pengyin Shan
AU - Jonathan Starr
AU - Samantha Teplitzky
AU - Peter K. G. Williams
AU - Alice Allen
AU - Lucas M. de Sá
AU - Andrew Fullard
AU - Sandra Gesing
AU - Tom Wagg
AU - Andrea Zonca
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.00528
UR - https://arxiv.org/abs/2610.00528
AB - 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.
ER -