A Rejoinder on Energy versus Impact Indicators

Citation distributions are so skewed that using the mean or any other central tendency measure is ill-advised. Unlike G. Prathap's scalar measures (Energy, Exergy, and Entropy or EEE), the Integrated Impact Indicator (I3) is based on non-parametric statistics using the (100) percentiles of the distribution. Observed values can be tested against expected ones; impact can be qualified at the article level and then aggregated.Comment: Scientometrics, in pres

The Carbon_h-Factor: Predicting Individuals' Research Impact at Early Stages of Their Career

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

Impact Factor: outdated artefact or stepping-stone to journal certification?

A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.Comment: 25 pages, 12 figures, 6 table

Prediction and estimation of effective population size

Effective population size (Ne) is a key parameter in population genetics. It has important applications in evolutionary biology, conservation genetics, and plant and animal breeding, because it measures the rates of genetic drift and inbreeding and affects the efficacy of systematic evolutionary forces such as mutation, selection and migration. We review the developments in predictive equations and estimation methodologies of effective size. In the prediction part, we focus on the equations for populations with different modes of reproduction, for populations under selection for unlinked or linked loci, and for the specific applications to conservation genetics. In the estimation part, we focus on methods developed for estimating the current or recent effective size from molecular marker or sequence data. We discuss some underdeveloped areas in predicting and estimating Ne for future research