Identifying research talent using web-centric databases

Abstract

Metrics play a key part in the assessment of scholars. These metrics are primarily computed using bibliometric data collected in offline procedures. In this work, we compare the usage of a publication database based on a Web crawl and a traditional publication database for computing scholarly metrics. We focus on metrics that determine the independence of researchers from their supervisor, which are used to assess the growth of young researchers. We describe two types of graphs that can be constructed from online data: the co-author network of the young researcher, and the combined topic network of the young researcher and their supervisor, together with a series of network properties that describe these graphs. Finally, we show that, for the purpose of discovering emerging talent, dynamic online publication resources provide better coverage than more traditional datasets, and more importantly, lead to very different results. Copyright 2013 ACM.