  The growth of large, programatically accessible bibliometrics databases
presents new opportunities for complex analyses of publication metadata. In
addition to providing a wealth of information about authors and institutions,
databases such as those provided by Dimensions also provide conceptual
information and links to entities such as grants, funders and patents. However,
data is not the only challenge in evaluating patterns in scholarly work: These
large datasets can be challenging to integrate, particularly for those
unfamiliar with the complex schemas necessary for accommodating such
heterogeneous information, and those most comfortable with data mining may not
be as experienced in data visualisation. Here, we present an open-source Python
library that streamlines the process accessing and diagramming subsets of the
Dimensions on Google BigQuery database and demonstrate its use on the freely
available Dimensions COVID-19 dataset. We are optimistic that this tool will
expand access to this valuable information by streamlining what would otherwise
be multiple complex technical tasks, enabling more researchers to examine
patterns in research focus and collaboration over time.
