This book is written for anyone who is interested in how a field of
research evolves and the fundamental role of understanding uncertainties
involved in different levels of analysis, ranging from macroscopic views
to meso- and microscopic ones. We introduce a series of computational
and visual analytic techniques, from research areas such as text mining,
deep learning, information visualization and science mapping, such that
readers can apply these tools to the study of a subject matter of their
choice. In addition, we set the diverse set of methods in an integrative
context, that draws upon insights from philosophical, sociological, and
evolutionary theories of what drives the advances of science, such that
the readers of the book can guide their own research with their enriched
theoretical foundations.
Scientific knowledge is complex. A subject matter is typically built on
its own set of concepts, theories, methodologies and findings,
discovered by generations of researchers and practitioners. Scientific
knowledge, as known to the scientific community as a whole, experiences
constant changes. Some changes are long-lasting, whereas others may be
short lived. How can we keep abreast of the state of the art as science
advances? How can we effectively and precisely convey the status of the
current science to the general public as well as scientists across
different disciplines?
The study of scientific knowledge in general has been overwhelmingly
focused on scientific knowledge per se. In contrast, the status of
scientific knowledge at various levels of granularity has been largely
overlooked. This book aims to highlight the role of uncertainties, in
developing a better understanding of the status of scientific knowledge
at a particular time, and how its status evolves over the course of the
development of research. Furthermore, we demonstrate how the knowledge
of the types of uncertainties associated with scientific claims serves
as an integral and critical part of our domain expertise.
