An accurate and fair assessment of the efficiency and impact of scientific work is, despite a lot of recent research effort, still an open problem. The measurement of quality and success of individual scientists and research groups can be approached from many different directions, none of which is universal. A reason for this is inherently different behavior of different scientists within the global research community. A complex evaluation of ones publication activities requires a careful consideration of a wide variety of factors. The well-known H-index is one of the most used bibliometric indices. Despite its many imperfections, its simplicity and ease of interpretation make it a popular scientometric method. This short paper uses the ideas behind the H-index to analyze communities of authors who cite publishing scientists. A new author evaluation measure named aH-index is proposed, and intuitive interpretations of its properties and semantics are presented. Preliminary experiments with authors with high H-index active in the area of computer science are presented to demonstrate the properties of the proposed measure.
After the introduction of the , many improvements that addressed its fundamental drawbacks were proposed. In @cite_5 , Zhang proposed a new index that is suitable for evaluating highly cited scientists and comparing groups of scientists with an identical . present in @cite_10 a comprehensive review on the and related indicators. They studied their main advantages, drawbacks, and main applications. In @cite_11 , present a study of 37 different variants of . They show a high correlation between the and most of its variants.
@cite_5 @cite_10 @cite_11
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The h-index and some related bibliometric indices have received a lot of attention from the scientific community in the last few years due to some of their good properties (easiness of computation, balance between quantity of publications and their impact and so on). Many different indicators have been developed in order to extend and overcome the drawbacks of the original Hirsch proposal. In this contribution we present a comprehensive review on the h-index and related indicators field. From the initial h-index proposal we study their main advantages, drawbacks and the main applications that we can find in the literature. A description of many of the h-related indices that have been developed along with their main characteristics and some of the works that analyze and compare them are presented. We also review the most up to date standardization studies that allow a fair comparison by means of the h-index among scientists from different research areas and finally, some works that analyze the computation of the h-index and related indices by using different citation databases (ISI Citation Indexes, Google Scholar and Scopus) are introduced. Background The h-index has already been used by major citation databases to evaluate the academic performance of individual scientists. Although effective and simple, the h-index suffers from some drawbacks that limit its use in accurately and fairly comparing the scientific output of different researchers. These drawbacks include information loss and low resolution: the former refers to the fact that in addition to h2 citations for papers in the h-core, excess citations are completely ignored, whereas the latter means that it is common for a group of researchers to have an identical h-index. Methodology Principal Findings To solve these problems, I here propose the e-index, where e2 represents the ignored excess citations, in addition to the h2 citations for h-core papers. Citation information can be completely depicted by using the h-index together with the e-index, which are independent of each other. Some other h-type indices, such as a and R, are h-dependent, have information redundancy with h, and therefore, when used together with h, mask the real differences in excess citations of different researchers. Conclusions Significance Although simple, the e-index is a necessary h-index complement, especially for evaluating highly cited scientists or for precisely comparing the scientific output of a group of scientists having an identical h-index. We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed improvement. Such decoding is enabled by: (1) separating full word features from partial word features so that feature templates can be instantiated incrementally, according to whether the current character is separated or appended; (2) deciding the POS-tag of a potential word when its first character is processed. Early-update is used with perceptron training so that the linear model gives a high score to a correct partial candidate as well as a full output. Effective scoring of partial structures allows the decoder to give high accuracy with a small beam-size of 16. In our 10-fold cross-validation experiments with the Chinese Tree-bank, our system performed over 10 times as fast as Zhang and Clark (2008) with little accuracy loss. The accuracy of our system on the standard CTB 5 test was competitive with the best in the literature. Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index.