  Considerable scientific work involves locating, analyzing, systematizing, and
synthesizing other publications. Its results end up in a paper's "background"
section or in standalone articles, which include meta-analyses and systematic
literature reviews. The required research is aided through the use of online
scientific publication databases and search engines, such as Web of Science,
Scopus, and Google Scholar. However, use of online databases suffers from a
lack of repeatability and transparency, as well as from technical restrictions.
Thankfully, open data, powerful personal computers, and open source software
now make it possible to run sophisticated publication studies on the desktop in
a self-contained environment that peers can readily reproduce. Here we report a
Python software package and an associated command-line tool that can populate
embedded relational databases with slices from the complete set of Crossref
publication metadata, ORCID author records, and other open data sets, for
in-depth processing through performant queries. We demonstrate the software's
utility by analyzing the underlying dataset's contents, by visualizing the
evolution of publications in diverse scientific fields and relationships among
them, by outlining scientometric facts associated with COVID-19 research, and
by replicating commonly-used bibliometric measures of productivity, impact, and
disruption.
