Bibliometric Analyses In Data Driven De

The book provides essential insights and practical tools needed to effectively navigate the evolving landscape of scholarly research, helping enhance the understanding of publication trends, citation impacts, and collaboration networks across multiple fields.

Bibliometric Analyses in Data-Driven Decision-Making offers a comprehensive guide to researchers, academics, and practitioners interested in utilizing bibliometric analysis to understand and navigate the dynamic landscape of the increasingly vital field of data-driven decision-making and its applications across many areas. It provides insights into growth, impact, and trends within the field, using bibliometric tools and methodologies. This volume adopts a pragmatic approach, balancing theoretical concepts with practical applications of data-driven decision-making models through the perspectives of bibliometric analyses using real-world examples, case studies, and step-by-step guides.

The reader will find the book:

• Gives practical guidance on conducting bibliometric analyses across a range of applications for data-driven decision-making;
• Illustrates the application of bibliometric tools in the field with real-world case studies;
• Provides in-depth coverage of various bibliometric indicators and metrics;
• Explores emerging trends and challenges in bibliometric analysis;
• Provides a comprehensive overview of software and tools available for bibliometric research.

Audience

Librarians and Information professionals involved in research management, knowledge discovery, and the evaluation of scholarly communication, as well as professionals in industries reliant on cutting-edge research and development, technology assessment, and innovation. Also, a range of researchers and scholars seeking how to apply bibliometric analysis to assess the impact of their work, and advanced insights into bibliometric metrics, collaboration networks, and research trends.

Prasenjit Chatterjee, PhD is a post-doctoral fellow in the Department of Applied Data Science, Noroff University College, and a professor of mechanical engineering and Dean of Research and Consultancy at the MCKV Institute of Engineering, India. He has published over 135 research papers in various international journals and conferences, authored and edited more than 43 books on intelligent decision-making, fuzzy computing, supply chain management, optimization techniques, risk management, and sustainability modelling.

Abhijit Saha, PhD is an assistant professor in the Department of Computing Technologies at SRM Institute of Science and Technology, Tamil Nadu, India with over ten years of teaching and research experience. He has published over 50 research articles in international journals and serves on the editorial boards of three international journals. His research interests encompass fuzzy set theory, soft set theory, aggregation operators, optimization, and decision-making.

Seifedine Kadry, PhD is a professor in the Department of Computer Science and Mathematics at the Lebanese American University and the Department of Applied Data Science at Noroff University College. He has published over 100 peer-reviewed papers and authored several books. His research interests include data science, system prognostics, stochastic systems, smart learning, social network analysis, and e-systems.

Gülay Demir, PhD is affiliated with Sivas Cumhuriyet University Vocational School of Health Services. She has over 75 publications, primarily in graduate-level textbooks. Her research interests include fuzzy logic, multi-criteria analysis, mathematical statistics, statistical analysis, data analysis, non-parametric statistics, statistical calculation, and parametric statistics.