H-index Sequences across Fields

Abstract

This study presents the first analysis of h-index sequences on a larger scale. Exemplarily, we investigated researchers from three different fields within Computer Science. We use Google Scholar citation profiles as data source to construct the h-index sequences of individual researchers. Our ultimate goal is to develop a self-evaluation tool, to assess one's own development of the h-index in comparison to other researchers in the same field, maybe identify career role models in the field and assess career development with future chances of success. The results of this study show that the average h-index sequences behave differently for the datasets, which is partly due to the different sample sizes. Hence, further research will be needed to confirm if every research field behaves differently. In addition, we applied the algorithm developed by Wu et al. [22] to our data to classify the h-index sequences of individual authors according to five different shape categories. The majority of researchers has an S-shaped h-index sequence, followed by IS-shaped and linear sequences. Purely concave or convex sequences hardly ever occur. The researchers with the highest h-indices after 10 career years respectively belong to the S-shaped and IS-shaped categories with a few linear category occurrences. Hence, having a linear h-index is not only very hard to achieve, it is also not a guaranty to be the researcher with the highest h-index in a field.