A complete update to a classic, respected resource Invaluable reference, supplying a comprehensive overview on how to undertake and present research

All researchers need to write or speak about their work, and to have research that is worth presenting. Based on the author's decades of experience as a researcher and advisor, this third edition provides detailed guidance on writing and presentations and a comprehensive introduction to research methods, the how-to of being a successful scientist. Topics include: · Development of ideas into research questions; · How to find, read, evaluate and referee other research; · Design and evaluation of experiments and appropriate use of statistics; · Ethics, the principles of science and examples of science gone wrong. Much of the book is a step-by-step guide to effective communication, with advice on: · Writing style and editing; · Figures, graphs and tables; · Mathematics and algorithms; · Literature reviews and referees’ reports; · Structuring of arguments and results into papers and theses; · Writing of other professional documents; · Presentation of talks and posters. Written in an accessible style and including handy checklists and exercises, Writing for Computer Science is not only an introduction to the doing and describing of research, but is a valuable reference for working scientists in the computing and mathematical sciences.

This book encompasses the entire range of writing skills that today's experimental scientist may need to employ. Chapters cover routine forms, such as laboratory notes, abstracts, and memoranda; dissertations; journal articles; and grant proposals. Robert Goldbort discusses how best to approach various writing tasks as well as how to deal with the everyday complexities that may get in the way of ideal practice--difficult collaborators, experiments gone wrong, funding rejections. He underscores the importance of an ethical approach to science and scientific communication and insists on the necessity of full disclosure.

How the theoretical tools of literacy help us understand programming in its historical, social and conceptual contexts. The message from educators, the tech community, and even politicians is clear: everyone should learn to code. To emphasize the universality and importance of computer programming, promoters of coding for everyone often invoke the concept of “literacy,” drawing parallels between reading and writing code and reading and writing text. In this book, Annette Vee examines the coding-as-literacy analogy and argues that it can be an apt rhetorical frame. The theoretical tools of literacy help us understand programming beyond a technical level, and in its historical, social, and conceptual contexts. Viewing programming from the perspective of literacy and literacy from the perspective of programming, she argues, shifts our understandings of both. Computer programming becomes part of an array of communication skills important in everyday life, and literacy, augmented by programming, becomes more capacious. Vee examines the ways that programming is linked with literacy in coding literacy campaigns, considering the ideologies that accompany this coupling, and she looks at how both writing and programming encode and distribute information. She explores historical parallels between writing and programming, using the evolution of mass textual literacy to shed light on the trajectory of code from military and government infrastructure to large-scale businesses to personal use. Writing and coding were institutionalized, domesticated, and then established as a basis for literacy. Just as societies demonstrated a “literate mentality” regardless of the literate status of individuals, Vee argues, a “computational mentality” is now emerging even though coding is still a specialized skill.

Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Category theory is a branch of pure mathematics that is becoming an increasingly important tool in theoretical computer science, especially in programming language semantics, domain theory, and concurrency, where it is already a standard language of discourse. Assuming a minimum of mathematical preparation, Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Four case studies illustrate applications of category theory to programming language design, semantics, and the solution of recursive domain equations. A brief literature survey offers suggestions for further study in more advanced texts. Contents Tutorial • Applications • Further Reading

Computer Science: Reflections on the Field, Reflections from the Field provides a concise characterization of key ideas that lie at the core of computer science (CS) research. The book offers a description of CS research recognizing the richness and diversity of the field. It brings together two dozen essays on diverse aspects of CS research, their motivation and results. By describing in accessible form computer science's intellectual character, and by conveying a sense of its vibrancy through a set of examples, the book aims to prepare readers for what the future might hold and help to inspire CS researchers in its creation.

The identity of computing has been fiercely debated throughout its short history. Why is it still so hard to define computing as an academic discipline? Is computing a scientific, mathematical, or engineering discipline? By describing the mathematical, engineering, and scientific traditions of computing, The Science of Computing: Shaping a Discipline presents a rich picture of computing from the viewpoints of the field’s champions. The book helps readers understand the debates about computing as a discipline. It explains the context of computing’s central debates and portrays a broad perspective of the discipline. The book first looks at computing as a formal, theoretical discipline that is in many ways similar to mathematics, yet different in crucial ways. It traces a number of discussions about the theoretical nature of computing from the field’s intellectual origins in mathematical logic to modern views of the role of theory in computing. The book then explores the debates about computing as an engineering discipline, from the central technical innovations to the birth of the modern technical paradigm of computing to computing’s arrival as a new technical profession to software engineering gradually becoming an academic discipline. It presents arguments for and against the view of computing as engineering within the context of software production and analyzes the clash between the theoretical and practical mindsets. The book concludes with the view of computing as a science in its own right—not just as a tool for other sciences. It covers the early identity debates of computing, various views of computing as a science, and some famous characterizations of the discipline. It also addresses the experimental computer science debate, the view of computing as a natural science, and the algorithmization of sciences.