  Systematic scientometric reviews, empowered by scientometric and visual
analytic techniques, offer opportunities to improve the timeliness,
accessibility, and reproducibility of conventional systematic reviews. While
increasingly accessible science mapping tools enable end users to visualize the
structure and dynamics of a research field, a common bottleneck in the current
practice is the construction of a collection of scholarly publications as the
input of the subsequent scientometric analysis and visualization. End users
often have to face a dilemma in the preparation process: the more they know
about a knowledge domain, the easier it is for them to find the relevant data
to meet their needs adequately; the little they know, the harder the problem
is. What can we do to avoid missing something valuable but beyond our initial
description? In this article, we introduce a flexible and generic methodology,
cascading citation expansion, to increase the quality of constructing a
bibliographic dataset for systematic reviews. Furthermore, the methodology
simplifies the conceptualization of globalism and localism in science mapping
and unifies them on a consistent and continuous spectrum. We demonstrate an
application of the methodology to the research of literature-based discovery
and compare five datasets constructed based on three use scenarios, namely a
conventional keyword-based search (one dataset), an expansion process starting
with a groundbreaking article of the knowledge domain (two datasets), and an
expansion process starting with a recently published review article by a
prominent expert in the domain (two datasets). The unique coverage of each of
the datasets is inspected through network visualization overlays with reference
to other datasets in a broad and integrated context.
