  Open-sourcing research publications is a key enabler for the reproducibility
of studies and the collective scientific progress of a research community. As
all fields of science develop more advanced algorithms, we become more
dependent on complex computational toolboxes -- sharing research ideas solely
through equations and proofs is no longer sufficient to communicate scientific
developments. Over the past years, several efforts have highlighted the
importance and challenges of transparent and reproducible research; code
sharing is one of the key necessities in such efforts. In this article, we
study the impact of code release on scientific research and present statistics
from three research communities: machine learning, robotics, and control. We
found that, over a six-year period (2016-2021), the percentages of papers with
code at major machine learning, robotics, and control conferences have at least
doubled. Moreover, high-impact papers were generally supported by open-source
codes. As an example, the top 1% of most cited papers at the Conference on
Neural Information Processing Systems (NeurIPS) consistently included
open-source codes. In addition, our analysis shows that popular code
repositories generally come with high paper citations, which further highlights
the coupling between code sharing and the impact of scientific research. While
the trends are encouraging, we would like to continue to promote and increase
our efforts toward transparent, reproducible research that accelerates
innovation -- releasing code with our papers is a clear first step.
