Abstract
Assessing an individual's research impact on the basis of a transparent algorithm is an important task for evaluation and comparison purposes. Besides simple but also inaccurate indices such as counting the mere number of publications or the accumulation of overall citations, and highly complex but also overwhelming full-range publication lists in their raw format, Hirsch (2005) introduced a single figure cleverly combining different approaches. The so-called h-index has undoubtedly become the standard in scientometrics of individuals' research impact (note: in the present paper I will always use the term “research impact” to describe the research performance as the logic of the paper is based on the h-index, which quantifies the specific “impact” of, e.g., researchers, but also because the genuine meaning of impact refers to quality as well). As the h-index reflects the number h of papers a researcher has published with at least h citations, the index is inherently positively biased towards senior level researchers. This might sometimes be problematic when predictive tools are needed for assessing young scientists' potential, especially when recruiting early career positions or equipping young scientists' labs. To be compatible with the standard h-index, the proposed index integrates the scientist's research age (Carbon_h-factor) into the h-index, thus reporting the average gain of h-index per year. Comprehensive calculations of the Carbon_h-factor were made for a broad variety of four research-disciplines (economics, neuroscience, physics and psychology) and for researchers performing on three high levels of research impact (substantial, outstanding and epochal) with ten researchers per category. For all research areas and output levels we obtained linear developments of the h-index demonstrating the validity of predicting one's later impact in terms of research impact already at an early stage of their career with the Carbon_h-factor being approx. 0.4, 0.8, and 1.5 for substantial, outstanding and epochal researchers, respectively.
Highlights
Assessing an individual’s research impact on the basis of a transparent algorithm is an important task for evaluation and comparison purposes as this method abstracts from individually used and interpreted facts
The integration of multidimensional sources of evidence for research impact, such as the number and scope of research projects, academic prizes, prestigious keynotes and being a trigger for influential public debates, would be helpful, but appears to be impracticable
Mean correlations between research age and h-index are given in the respective third row
Summary
Assessing an individual’s research impact on the basis of a transparent algorithm is an important task for evaluation and comparison purposes as this method abstracts from individually used and interpreted facts. The integration of multidimensional sources of evidence for research impact (note: in the present paper I will always use the term ‘‘research impact’’ to describe the research performance as the logic of the paper is based on the h-index, which quantifies the specific ‘‘impact’’ of, e.g., researchers, and because the genuine meaning of impact refers to quality as well), such as the number and scope of research projects, academic prizes, prestigious keynotes and being a trigger for influential public debates, would be helpful, but appears to be impracticable Such evaluations often overwhelm the evaluator, second, such information can hardly be obtained to full extent, third, any evaluation on such a basis is likely to be incommensurable, and cannot be compared among different candidates. As it is being incorporated as a regular part of several research information platforms [7,8], such as ISI Web of Science by Thomson Reuters or Scopus by Elsevier B.V., the standard measure for assessing research impact (cf. [9,10])
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