This book is written for anyone who is interested in how a field of research evolves & the fundamental role of understanding uncertainties involved in different levels of analysis ranging from macroscopic views to meso- & microscopic ones We introduce a series of computational & visual analytic techniques from research areas such as text mining deep learning information visualization & 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 & 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 & findings discovered by generations of researchers & 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 & 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 & 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 & critical part of our domain expertise