Science Dynamics And Research Production

This book deals with methods to evaluate scientific productivity. In the book statistical methods, deterministic and stochastic models and numerous indexes are discussed that will help the reader to understand the nonlinear science dynamics and to be able to develop or construct systems for appropriate evaluation of research productivity and management of research groups and organizations. The dynamics of science structures and systems is complex, and the evaluation of research productivity requires a combination of qualitative and quantitative methods and measures. The book has three parts. The first part is devoted to mathematical models describing the importance of science for economic growth and systems for the evaluation of research organizations of different size. The second part contains descriptions and discussions of numerous indexes for the evaluation of the productivity of researchers and groups of researchers of different size (up to the comparison of research productivities of research communities of nations). Part three contains discussions of non-Gaussian laws connected to scientific productivity and presents various deterministic and stochastic models of science dynamics and research productivity. The book shows that many famous fat tail distributions as well as many deterministic and stochastic models and processes, which are well known from physics, theory of extreme events or population dynamics, occur also in the description of dynamics of scientific systems and in the description of the characteristics of research productivity. This is not a surprise as scientific systems are nonlinear, open and dissipative.

Evolution of Knowledge Production The Marketability and Commercialisation of Knowledge Massification of Research and Education The Case of the Humanities Competitiveness, Collaboration and Globalisation Reconfiguring Institutions Towards Managing Socially Distributed Knowledge.

Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.

This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction focuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks. The single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art. Due to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. The profiles of the readers may also be diverse ranging from researchers and professors in the respective fields to students and developers being curious about the covered subjects.

The interrelations of science and technology as an object of study seem to have drawn the attention of a number of disciplines: the history of both science and technology, sociology, economics and economic history, and even the philosophy of science. The question that comes to mind is whether the phenomenon itself is new or if advances in the disciplines involved account for this novel interest, or, in fact, if both are intercon nected. When the editors set out to plan this volume, their more or less explicit conviction was that the relationship of science and technology did reveal a new configuration and that the disciplines concerned with 1tS analysis failed at least in part to deal with the change because of conceptual and methodological preconceptions. To say this does not imply a verdict on the insufficiency of one and the superiority of any other one disciplinary approach. Rather, the situation is much more complex. In economics, for example, the interest in the relationship between science and technology is deeply influenced by the theoretical problem of accounting for the factors of economic growth. The primary concern is with technology and the problem is whether the market induces technological advances or whether they induce new demands that explain the subsequent diffusion of new technologies. Science is generally considered to be an exogenous factor not directly subject to market forces and, therefore, appears to be of no interest.

This book is a collection of works regarding the interactions of science, technology, and society.

Machining dynamics play an essential role in the performance of the machine tools and machining processes which directly affect the removal rate, workpiece surface quality and dimensional and form accuracy. Machining Dynamics: Fundamentals and Applications will be bought by advanced undergraduate and postgraduate students studying manufacturing engineering and machining technology in addition to manufacturing engineers, production supervisors, planning and application engineers, and designers.

This textbook covers handling and performance of both road and race cars. Mathematical models of vehicles are developed always paying attention to state the relevant assumptions and to provide explanations for each step. This innovative approach provides a deep, yet simple, analysis of the dynamics of vehicles. The reader will soon achieve a clear understanding of the subject, which will be of great help both in dealing with the challenges of designing and testing new vehicles and in tackling new research topics. The book deals with several relevant topics in vehicle dynamics that are not discussed elsewhere and this new edition includes thoroughly revised chapters, with new developments, and many worked exercises. Praise for the previous edition: Great book! It has changed drastically our approach on many topics. We are now using part of its theory on a daily basis to constantly improve ride and handling performances. --- Antonino Pizzuto, Head of Chassis Development Group at Hyundai Motor Europe Technical Center Astonishingly good! Everything is described in a very compelling and complete way. Some parts use a different approach than other books. --- Andrea Quintarelli, Automotive Engineer