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Open-sourcing research publications is a key enabler for the reproducibilityof studies and the collective scientific progress of a research community. Asall fields of science develop more advanced algorithms, we become moredependent on complex computational toolboxes -- sharing research ideas solelythrough equations and proofs is no longer sufficient to communicate scientificdevelopments. Over the past years, several efforts have highlighted theimportance and challenges of transparent and reproducible research; codesharing is one of the key necessities in such efforts. In this article, westudy the impact of code release on scientific research and present statisticsfrom three research communities: machine learning, robotics, and control. Wefound that, over a six-year period (2016-2021), the percentages of papers withcode at major machine learning, robotics, and control conferences have at leastdoubled. Moreover, high-impact papers were generally supported by open-sourcecodes. As an example, the top 1% of most cited papers at the Conference onNeural Information Processing Systems (NeurIPS) consistently includedopen-source codes. In addition, our analysis shows that popular coderepositories generally come with high paper citations, which further highlightsthe coupling between code sharing and the impact of scientific research. Whilethe trends are encouraging, we would like to continue to promote and increaseour efforts toward transparent, reproducible research that acceleratesinnovation -- releasing code with our papers is a clear first step.

What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities