Analyzing Email Patterns with Timelines on Researcher Data

This paper proposes a procedure that easily extracts a feature that helps differentiate between similar researcher names in articles. We examined email patterns and their timelines to identify researchers. Our statistical analysis results show multiple email address usage patterns are found in the case of approximately 43% researchers, and 5% of the patterns are overlapped. Base on the statistics, we conclude that the identification of researchers is still required to enhance performance of the researcher-centric analytics systems and applications.