Scietometrics

The present research paper entitled Indian Contribution to Big Data: A Scientometric Study is a systematic study of the research output of the Big Data. The data has been taken from Scopus database during 2015-2019. A total of 1059 articles were retrieved on Big Data through the concerned database during 2015-2019. it has been seen that the research productivity on Big Introduction:

Society is becoming increasingly dependent on energy. It is a stimulating factor for economic growth and technological change, but also the most significant limiting factor. The analysis of research trends on energy is of great importance because it provides a picture of the development directions of the future. The goal of the paper is to evaluate the performance and tendencies of researches on energy in the World, Eastern Europe region and in the Republic of Moldova. This goal is achieved by using bibliometric methods. Bibliometrics has become standard method for measuring the impact of research, which is accepted as a valid way of ranking scientific performance and tendencies. The novelty of the work consists in the wider geographical and chronological coverage of the study in comparison with similar studies, as well as that the analysis was carried out based on data extracted from two international and one national databases: Scopus Elsevier, Web of Science Core Collection and N...

Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. This paper presents new funding, publication, and scholarly network metrics and visualizations that were validated via expert surveys. The metrics and visualizations exemplify the emergence and convergence of three areas of strategic interest: artificial intelligence (AI), robotics, and internet of things (IoT) over the last 20 years (1998-2017). For 32,716 publications and 4,497 NSF awards, we identify their topical coverage (using the UCSD map of science), evolving co-author networks, and increasing convergence. The results support data-driven decision making when setting proper research and development (R&D) priorities; developing future S&T investment strategies; or performing effective research program assessment.