Research Analytics: Boosting University Productivity and Competitiveness Through Scientometrics

ISBN-13: 9781498785426 / Angielski / Twarda / 2017 / 264 str.

Francisco J. Cantu-Ortiz

Opis

Scientific knowledge is doubling every 10 to 15 years worldwide. Who generates this knowledge? Where is this information stored? How is it being utilized? These are some issues addressed in this book on data analytics and scientometrics, which brings together scientific production, research evaluation, and reputation of world-class universities.

Spis treści

Data Analytics and Scientometrics: Concepts, Methods, Synergies. The Growth of Scientific Knowledge. Scientometrics: Fundamentals and Applications. The Thomson Reuters Web of Science Database. The Elsevier SCOPUS Database. The Google Scholar Database. Institutional Repositories. The Academic Ranking of World Universities. The QS World University Ranking. The TIMES Higher Education World University Ranking. The US News Best Global University Rankings. Knowledge distribution through Web Domains: The Webometrics Ranking. The Image of Science: The SCIMAGO Ranking. Data Analytics and Scientometrics: Lessons Learned and Prospective Analysis.

O autorze

Dr. Francisco J. Cantu-Ortiz holds a PhD in Artificial Intelligence from the University of Edinburgh, a MSc in Computer Science from North Dakota State University, and a BSc in Computer Systems Engineering from Tecnológico de Monterrey, México. He is a Full Professor of Computer Science and Artificial Intelligence and Associate Vice-Provost for Research at Tecnológico de Monterey. He was head of the school’s Center for Artificial Intelligence and the Center for Informatics Research. He has published around 60 scientific articles in international journals and conferences and around 20 edited books. He has been an invited speaker in various international conferences and is accredited as National Researcher by the National Council for Science and Technology, Mexico. His research interests include knowledge-based systems and inference, machine learning and data mining using Bayesian and statistical techniques for research intelligence and science and technology management. He has an interest in epistemology and philosophy of science and religion.