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

Measuring Swampiness: Quantifying Chaos in Large Heterogeneous Data Repositories