  Discovering authoritative links between publications and the datasets that
they use can be a labor-intensive process. We introduce a natural language
processing pipeline that retrieves and reviews publications for informal
references to research datasets, which complements the work of data librarians.
We first describe the components of the pipeline and then apply it to expand an
authoritative bibliography linking thousands of social science studies to the
data-related publications in which they are used. The pipeline increases recall
for literature to review for inclusion in data-related collections of
publications and makes it possible to detect informal data references at scale.
We contribute (1) a novel Named Entity Recognition (NER) model that reliably
detects informal data references and (2) a dataset connecting items from social
science literature with datasets they reference. Together, these contributions
enable future work on data reference, data citation networks, and data reuse.
