  Research data are often released upon journal publication to enable result
verification and reproducibility. For that reason, research dissemination
infrastructures typically support diverse datasets coming from numerous
disciplines, from tabular data and program code to audio-visual files.
Metadata, or data about data, is critical to making research outputs adequately
documented and FAIR. Aiming to contribute to the discussions on the development
of metadata for research outputs, I conducted an exploratory analysis to
determine how research datasets cluster based on what researchers organically
deposit together. I use the content of over 40,000 datasets from the Harvard
Dataverse research data repository as my sample for the cluster analysis. I
find that the majority of the clusters are formed by single-type datasets,
while in the rest of the sample, no meaningful clusters can be identified. For
the result interpretation, I use the metadata standard employed by DataCite, a
leading organization for documenting a scholarly record, and map existing
resource types to my results. About 65% of the sample can be described with a
single-type metadata (such as Dataset, Software or Report), while the rest
would require aggregate metadata types. Though DataCite supports an aggregate
type such as a Collection, I argue that a significant number of datasets, in
particular those containing both data and code files (about 20% of the sample)
would be more accurately described as a Replication resource metadata type.
Such resource type would be particularly useful in facilitating research
reproducibility.
