| Summary: | Few research studies have quantitatively analyzed metadata elements associated with scientific data reuse. By using metadata and dataset download rates from the National Snow and Ice Data Center, we address whether there are key indicators in data repository metadata that show a statistically significant correlation with the download count of a dataset and whether we can predict data reuse using machine learning techniques. We used the download rate by unique IP addresses for individual datasets as our dependent variable and as a proxy for data reuse. Our analysis shows that the following metadata elements in NSIDC datasets are positively correlated with download rates: year of citation, number of data formats, number of contributors, number of platforms, number of spatial coverage areas, number of locations, and number of keywords. Our results are applicable to researchers and professionals working with data and add to the small body of work addressing metadata best practices for increasing discovery of data.
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