  Reproducibility, the ability to reproduce the results of published papers or
studies using their computer code and data, is a cornerstone of reliable
scientific methodology. Studies where results cannot be reproduced by the
scientific community should be treated with caution. Over the past decade, the
importance of reproducible research has been frequently stressed in a wide
range of scientific journals such as \textit{Nature} and \textit{Science} and
international magazines such as \textit{The Economist}. However, multiple
studies have demonstrated that scientific results are often not reproducible
across research areas such as psychology and medicine. Statistics, the science
concerned with developing and studying methods for collecting, analyzing,
interpreting and presenting empirical data, prides itself on its openness when
it comes to sharing both computer code and data. In this paper, we examine
reproducibility in the field of statistics by attempting to reproduce the
results in 93 published papers in prominent journals utilizing functional
magnetic resonance imaging (fMRI) data during the 2010-2021 period. Overall,
from both the computer code and the data perspective, among all the 93 examined
papers, we could only reproduce the results in 14 (15.1%) papers, that is, the
papers provide both executable computer code (or software) with the real fMRI
data, and our results matched the results in the paper. Finally, we conclude
with some author-specific and journal-specific recommendations to improve the
research reproducibility in statistics.
