Give me a hi-5! An additional version of the h-index

Publishing is a key measure of academic performance and an important determinant of career success. There is a cottage industry devising different methods to measure the impact of scientific publications and this has spawned controversy as to which method is best (Bornmann et al., 2011; Cleary et al., 2013; Panaretos and Malesios, 2009). The number of publications, the impact factor of the journal in which these are published and number of times publications are cited are all ‘measures’ used to evaluate scientific merit and the output of individual scientists. Two questions follow: Which is the best measure? And, how can an equitable comparison of impact be achieved between well-established scientists and those early in their career? Recently, the CEO of the National Health and Medical Research Council (NHMRC) announced it no longer considers where an article is published (see ‘Death of the journal impact factor’) but instead asks chief investigators to select their most important articles within the last 5-year period to selfassess recent output, and provide examples from the last 15-year period to demonstrate long-term significance (Anderson, 2013). Here we consider methods for assessing the impact of published research via citation counts. In recent years, the h-index (Hirsch, 2005) has become the most popular measure of scientific performance. As most readers will know, a researcher with an h-index of 20 has at least 20 publications that have each been cited at least 20 times. We (Hunt et al., 2010) and others have shown how to obtain your h-index and the advantages of using this measure to determine the impact of research (Alonso et al., 2009; Bornmann et al., 2008; Costas and Bordons, 2007; Glanzel and Moed, 2013; Patro and Aggarwal, 2011). But one of the disadvantages of the h-index is when it is used to assess the impact of scientists having different career lengths. This is because the h-index represents accumulated career citations and therefore it does not decrease over time and may not capture recent productivity. Hirsch (2005) recognised this weakness and suggested that career length could be factored in by dividing h by the age of the paper (which Hirsch called the m quotient). Others variants accounting for career length or age have also been suggested (Bornmann et al., 2011; De Visscher, 2010; Sidiropoulos et al., 2007). However, all of these fall well short of the mark when it comes to comparing outcomes between older and younger scientists. Notably, papers published late in a scientist’s career will take many years to receive sufficient citations to enter the h-core (see below) and add to their already high h-index. Another problem with the h-index is that excess citations are completely ignored once an article enters the h-core of papers. Recently, the e-index was proposed to account for these excess citations and was shown to be useful when evaluating highly cited scientists with identical h-indexes (Zhang, 2009). An additional way to view and express the h-index is to divide it into 5-year retrospective blocks. By focusing on the most recent 5-year block, a more equitable analysis of early, mid and late career researchers may be achieved by allowing a focus on recent productivity rather than career longevity. Below we analyse the h-index of various researchers computed over the last 15 years, considering the entire 15-year period and also when these 15 years are divided in three consecutive 5-year blocks. We refer to an h-index over a 5-year interval as a ‘hi-5’. We also use the e-index to complement the h-index to measure excess citations. Examples of research output from de-identified academics and clinical academics from the University of Sydney and two other universities are used to illustrate this method and its possible advantages.