Predicting the Dynamics of Research Impact


Book Description

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.




Data Science for Economics and Finance


Book Description

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.







Dynamic Personality Science. Integrating Between-Person Stability and Within-Person Change


Book Description

Personality can be understood from at least two perspectives. One focuses on stable, between-person differences, or traits. The other perspective focuses on within-person differences and dynamics, i.e., fluctuations in personality in response to situations and across time. This Research Topic reflects recent developments in personality research to integrate both trait and dynamic perspectives. An integrated view on personality recognizes both stability in between-person differences and within-person change. Contributors are drawn from research teams across Europe, North America and Australasia, and from basic and applied fields, including organizational, educational, and clinical. The studies reported provide new evidence in support of an integrative approach, highlight currently active areas of research and propose new directions of research. Current streams of research include the study of contingent units of personality and within-person processes underlying traits, the comparisons of findings based on within- vs. between-person data, the conceptualisation and operationalization of perceived and objective change in situation variables, the malleability of personality and the potential for personality interventions. Integrative approaches using within-person designs provide new, bottom-up insights into general principles of personality that explain differences between people while reflecting the complexities of within-person personality dynamics at the level of the individual.







Multidisciplinary Research in Arts, Science & Commerce (Volume-2)


Book Description




Federal Register


Book Description