  Data citations provide a foundation for studying research data impact.
Collecting and managing data citations is a new frontier in archival science
and scholarly communication. However, the discovery and curation of research
data citations is labor intensive. Data citations that reference unique
identifiers (i.e. DOIs) are readily findable; however, informal mentions made
to research data are more challenging to infer. We propose a natural language
processing (NLP) paradigm to support the human task of identifying informal
mentions made to research datasets. The work of discovering informal data
mentions is currently performed by librarians and their staff in the
Inter-university Consortium for Political and Social Research (ICPSR), a large
social science data archive that maintains a large bibliography of data-related
literature. The NLP model is bootstrapped from data citations actively
collected by librarians at ICPSR. The model combines pattern matching with
multiple iterations of human annotations to learn additional rules for
detecting informal data mentions. These examples are then used to train an NLP
pipeline. The librarian-in-the-loop paradigm is centered in the data work
performed by ICPSR librarians, supporting broader efforts to build a more
comprehensive bibliography of data-related literature that reflects the
scholarly communities of research data users.
