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.
