  As scientific data repositories and filesystems grow in size and complexity,
they become increasingly disorganized. The coupling of massive quantities of
data with poor organization makes it challenging for scientists to locate and
utilize relevant data, thus slowing the process of analyzing data of interest.
To address these issues, we explore an automated clustering approach for
quantifying the organization of data repositories. Our parallel pipeline
processes heterogeneous filetypes (e.g., text and tabular data), automatically
clusters files based on content and metadata similarities, and computes a novel
"cleanliness" score from the resulting clustering. We demonstrate the
generation and accuracy of our cleanliness measure using both synthetic and
real datasets, and conclude that it is more consistent than other potential
cleanliness measures.
