Code Contribution and Credit in Science

• URL: http://arxiv.org/abs/2510.16242v1
• Date: Fri, 17 Oct 2025 22:17:38 GMT
• Title: Code Contribution and Credit in Science
• Authors: Eva Maxfield Brown, Isaac Slaughter, Nicholas Weber,
• Abstract summary: We investigate how software development activities influence credit allocation in collaborative scientific settings.<n>Nearly 30% of articles include non-author code contributors.<n>Authors who contribute code more frequently exhibit progressively lower h-indices than non-coding colleagues.
• Score: 1.5484595752241122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software development has become essential to scientific research, but its relationship to traditional metrics of scholarly credit remains poorly understood. We develop a dataset of approximately 140,000 paired research articles and code repositories, as well as a predictive model that matches research article authors with software repository developer accounts. We use this data to investigate how software development activities influence credit allocation in collaborative scientific settings. Our findings reveal significant patterns distinguishing software contributions from traditional authorship credit. We find that nearly 30% of articles include non-author code contributors- individuals who participated in software development but received no formal authorship recognition. While code-contributing authors show a modest $\sim$4.2% increase in article citations, this effect becomes non-significant when controlling for domain, article type, and open access status. First authors are significantly more likely to be code contributors than other author positions. Notably, we identify a negative relationship between coding frequency and scholarly impact metrics. Authors who contribute code more frequently exhibit progressively lower h-indices than non-coding colleagues, even when controlling for publication count, author position, domain, and article type. These results suggest a disconnect between software contributions and credit, highlighting important implications for institutional reward structures and science policy.

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