Bibliometric analysis of biotechnology research in Pakistan

In this study we present an analysis of the research trends in Pakistan in the field of biotechnology for the period 1980–2011. Starting with just 15 publications in 1980 with a negligible annual growth rate for the initial 15 years, the number of publications reached 3,273 in 2011 with an annual growth rate of 22 % for the last 15 years. This growth in publications is studied through factors such as Relative Growth Rate and Doubling Time. A comparison of organizations actively engaged in research in biotechnology is made through factors such as their total publications, total citations, and average citations per paper and indices that determine the quality of publications like h-index, g-index, hg-index and p-index. University of Karachi shows the highest number of publications (2,698), while National Institute of Biotechnology and Genetic Engineering with fewer publications shows the highest average citation per paper (8.07). Agha Khan University however, shows the highest h, g, hg and p indices.     

<Emphasis Type="Italic">hg</Emphasis>-index: a new index to characterize the scientific output of researchers based on the <Emphasis Type="Italic">h</Emphasis>- and <Emphasis Type="Italic">g</Emphasis>-indices

To be able to measure the scientific output of researchers is an increasingly important task to support research assessment decisions. To do so, we can find several different measures and indices in the literature. Recently, the h-index, introduced by Hirsch in 2005, has got a lot of attention from the scientific community for its good properties to measure the scientific production of researchers. Additionally, several different indicators, for example, the g-index, have been developed to try to improve the possible drawbacks of the h-index. In this paper we present a new index, called hg-index, to characterize the scientific output of researchers which is based on both h-index and g-index to try to keep the advantages of both measures as well as to minimize their disadvantages.     

A scientometric assessment of research output in nanoscience and nanotechnology: Pakistan perspective

In this study we present an analysis of the research trends in Pakistan in the field of nanoscience and nanotechnology. Starting with just seven publications in the year 2000, this number has steadily increased to 542 for the year 2011. Among the top 15 institutions with publications in nanotechnology 13 are universities and only two are R&D organizations. Almost 35 % of the research publications are in the field of material sciences followed by chemistry and physics in that order. The growth in the publications for period 2000–2011 is studied through relative growth rate and doubling time. The authorship pattern is measured by different collaboration parameters, like collaborative index, degree of collaboration, collaboration coefficient and modified collaboration coefficient. Finally the quality of papers is assessed by means of the h-index, g-index, hg-index and p-index.     

The size and impact of the elite set of publications in scientometric assessments

Several scientometric impact indicators [total citations, h, g, and π-index, percentage rank position (PRP), weighted citation share (WCS)] of 190 elite papers of 15 members of the Hungarian Academy of Sciences active in three different fields were calculated. From the indices the PRP indicator proved to be independent of the citation practices in the fields. The PRP index of a journal paper can be calculated in per cent as unity minus (the rank number of the paper by citation frequency within the publishing journal minus one divided with the total number of papers in the journal) times hundred. The sum of the PRP index of the elite papers of a scientist may characterize his or her total publication performance. The size of the elite set of journal papers within the total was calculated by different methods. The h-index and g-index corresponds to the size of the elite, i.e. number of the elite papers according to the h-statistics and g-statistics, respectively. The number of papers in the π-set is equal to the square root of total papers. The π-index equals to one hundredth of citations to the π-set papers. In the present paper the size of the elite set is determined as the number of papers in the h-set, g-set, or π-set, and as 10 % of total papers, or number of papers cited 2, 3, or 5 times the mean citation rate (MCR) of the publishing journal. The π-citation threshold model is presented for demonstrating how MCR and the distribution of citations over the papers may influence the size of the elite set and the corresponding π-index. It was found that the scientific performances concluded from the π-index obtained from elite sets of different size are in good agreement.     

The H l -index: improvement of H-index based on quality of citing papers

This paper proposes h _l -index as an improvement of the h-index, a popular measurement for the research quality of academic researchers. Although the h-index integrates the number of publications and the academic impact of each publication to evaluate the productivity of a researcher, it assumes that all papers that cite an academic article contribute equally to the academic impact of this article. This assumption, of course, could not be true in most times. The citation from a well-cited paper certainly brings more attention to the article than the citation from a paper that people do not pay attention to. It therefore becomes important to integrate the impact of papers that cite a researcher’s work into the evaluation of the productivity of the researcher. Constructing a citation network among academic papers, this paper therefore proposes h _l -index that integrating the h-index with the concept of lobby index, a measures that has been used to evaluate the impact of a node in a complex network based on the impact of other nodes that the focal node has direct link with. This paper also explores the characteristics of the proposed h _l -index by comparing it with citations, h-index and its variant g-index.     

The success-index: an alternative approach to the h-index for evaluating an individual’s research output

Among the most recent bibliometric indicators for normalizing the differences among fields of science in terms of citation behaviour, Kosmulski (J Informetr 5(3):481?C485, 2011) proposed the NSP (number of successful paper) index. According to the authors, NSP deserves much attention for its great simplicity and immediate meaning??equivalent to those of the h-index??while it has the disadvantage of being prone to manipulation and not very efficient in terms of statistical significance. In the first part of the paper, we introduce the success-index, aimed at reducing the NSP-index??s limitations, although requiring more computing effort. Next, we present a detailed analysis of the success-index from the point of view of its operational properties and a comparison with the h-index??s ones. Particularly interesting is the examination of the success-index scale of measurement, which is much richer than the h-index??s. This makes success-index much more versatile for different types of analysis??e.g., (cross-field) comparisons of the scientific output of (1) individual researchers, (2) researchers with different seniority, (3) research institutions of different size, (4) scientific journals, etc.     

Predicting scientific impact based on <Emphasis Type="Italic">h</Emphasis>-index

Predicting the future impact of a scientist/researcher is a critical task. The objective of this work is to evaluate different h-index prediction models for the field of Computer Science. Different combinations of parameters have been identified to build the model and applied on a large data set taken from Arnetminer comprised of almost 1.8 million authors and 2.1 million publications’ record of Computer Science. Machine learning prediction technique, regression, is used to find the best set of parameters suitable for h-index prediction for the scientists from all career ages, without enforcing any constraint on their current h-index values with R ² as a metric to measure the accuracy. Further, these parameters are evaluated for different career ages and different thresholds for h-index values. Prediction results for 1 year are really good, having R ² 0.93 but for 5 years R ² declines to 0.82 on average. Hence inferred that prediction of h-index is difficult for longer periods. Predictions for the researchers having 1 year experience are not precise, having R ² 0.60 for 1 year and 0.33 for 5 years. Considering scientists of different career ages, average R ² values for researchers having 20–36 years of experience were 0.99. For the researches having different h-index values, researchers having low h-index were difficult to predict. Parameters set comprising of current h-index, average citations per paper, number of coauthors, years since publishing first article, number of publications, number of impact factor publications, and number of publications in distinct journals performed better than all other combinations.     

Applying the <Emphasis Type="Italic">h</Emphasis>-index in exploring bibliometric properties of elite marketing scholars

The h-index is a recent metric that captures a scholar’s influence. In the current work, it is used to: (1) obtain the h-index scores of the most productive scholars in the Journal of Consumer Research (JCR), and compare these to other elite scholars (including those of the other three premier marketing journals); (2) demonstrate the relationship between the h-indices and total number of citations of the top JCR producers; (3) examine the h-indices of Ferber winners (best interdisciplinary paper based on a doctoral dissertation published in JCR in a given year) and those having received honorable mentions; (4) explore the relationship between a marketing journal’s prestige and the corresponding h-index score of its editor. These varied analyses demonstrate the multitudinous ways in which the h-index can be used when investigating bibliometric phenomena within a given discipline.     

Certainty equivalent citation: generalized classes of citation indexes

Drawing from the existing literature on risk and inequality measurement, we implement the notion of “certainty equivalent citation” in order (i) to generalize most of the h-type citation indexes (h-, g-, $\tilde{g},$ t-, f-, w-index), and (ii) to highlight the centrality of the decision-maker’s preferences on distributive aspects (concentration aversion) for the ranking of citation profiles. In order to highlight the sensitivity of citation orderings with respect to concentration aversion, an application to both simulated and real citation profiles is presented.     

Use of h index and g index for American academic psychiatry

Several publication metrics are used for the evaluation of academic productivity. h index and g index are relatively new statistics for this purpose. Our aim is to evaluate academic psychiatrists’ h and g indices at different academic ranks in the United States. 30 psychiatry programs from the American Medical Association’s FREIDA online database were included to the study. From each academic rank, the total number of papers (P _total), the single authored papers (P _single) and the h and g indexes of faculty members were calculated by using one way ANOVA for multiple comparisons as primary analysis test. The metric medians as follows; P _total = 34.5, P _single = 13, g index = 19.5 and h index = 9. h index significantly differed between academic ranks except chairperson-professor. The other indices failed to distinguish junior academic ranks (associated professor-assistant professor) in addition to chairperson-professor. The strongest correlation was between h index and g indexes. Of the indices evaluated, the h-index is best tracked with academic ranking in psychiatry programs studied.     

Is there a correlation between journal impact factor and researchers’ performance? A study comprising the fields of clinical nephrology and neurosciences

Quantifying the scientific performance of investigators has become an integral part of decision-making in research policy. The aim of the present study was to evaluate if there is a correlation between journal impact factor (IF) and researchers’ influence among a selected group of Brazilian investigators in the fields of clinical nephrology and neurosciences. This study was based on 94 senior investigators (36 in clinical nephrology and 58 in clinical neurosciences) receiving productivity scholarships from the Brazilian Council for Scientific and Technological Development (CNPq) according to a list provided by the agency in February 2009. Scientific performance indicators included in the analysis were: number of papers indexed by the Web of Science and Scopus databases, number of citations, h- and m-index. IFs were analyzed as (1) cumulative IF (∑IF), (2) IF adjusted by time (IF/t), and (3) average IF. There was a moderate positive correlation only between ∑IF and two indicators: total number of citations (P < 0.001) and h-index (P < 0.001). There was also a positive correlation between IF/t and m-index (P < 0.001). There was an agreement in these correlations between both groups (clinical nephrology and neurosciences). No significant correlation between the average IF and any of the scientific indicators was detected. A cut-off of 10.53 for IF/t showed the best performance in predicting researchers with m-index equal to or greater than 1. According to our findings, other qualitative and quantitative instruments rather than IF are clearly needed for identifying researchers with outstanding scientific output.     

Evaluating research institutions: the potential of the success-index

Similarly to the h-index and other indicators, the success-index is a recent indicator that makes it possible to identify, among a general group of papers, those of greater citation impact. This indicator implements a field-normalization at the level of single paper and can therefore be applied to multidisciplinary groups of articles. Also, it is very practical for normalizations aimed at achieving the so-called size-independency. Thanks to these (and other) properties, this indicator is particularly versatile when evaluating the publication output of entire research institutions. This paper exemplifies the potential of the success-index by means of several practical applications, respectively: (i) comparison of groups of researchers within the same scientific field, but affiliated with different universities, (ii) comparison of different departments of the same university, and (iii) comparison of entire research institutions. A sensitivity analysis will highlight the success-index’s robustness. Empirical results suggest that the success-index may be conveniently extended to large-scale assessments, i.e., involving a large number of researchers and research institutions.     

Is there a place for a mock <Emphasis Type="Italic">h</Emphasis>-index?

The h-index has captured the imagination of scientometricians and bibliometricians to such an extent that one can now divide the history of the subject virtually into a pre-Hirsch and a post-Hirsch period. Beyond its academic value, it is now used as a tool for research assessment of individuals, research faculties and institutions and even for comparing performance of journals and countries. Since its introduction, many Hirsch-type variants have been proposed to overcome perceived limitations of the original index. In this paper, using ideas from mathematical modeling, another mock h-index is proposed which may complement the h-index and give it better resolving power.     

Analysis of bibliometric indicators for individual scholars in a large data set

Citation numbers and other quantities derived from bibliographic databases are becoming standard tools for the assessment of productivity and impact of research activities. Though widely used, still their statistical properties have not been well established so far. This is especially true in the case of bibliometric indicators aimed at the evaluation of individual scholars, because large-scale data sets are typically difficult to be retrieved. Here, we take advantage of a recently introduced large bibliographic data set, Google Scholar Citations, which collects the entire publication record of individual scholars. We analyze the scientific profile of more than 30,000 researchers, and study the relation between the h-index, the number of publications and the number of citations of individual scientists. While the number of publications of a scientist has a rather weak relation with his/her h-index, we find that the h-index of a scientist is strongly correlated with the number of citations that she/he has received so that the number of citations can be effectively be used as a proxy of the h-index. Allowing for the h-index to depend on both the number of citations and the number of publications, we find only a minor improvement.     

Fairly sharing the credit of multi-authored papers and its application in the modification of <Emphasis Type="Italic">h</Emphasis>-index and <Emphasis Type="Italic">g</Emphasis>-index

Except the alphabetic ordering authorship papers, the citations of multi-authored papers are allocated to the authors based on their contributions to the paper. For papers without clarification of contribution proportion, a function of author number and rank is presented to rightly determine the credit allocated proportion and allocated citations of each author. Our citation allocation scheme is between the equally fractional counting and the one using the inverse of author rank. It has a parameter to adjust the credit distribution among the different authors. The allocated citations can either be used alone to indicate one’s performance in a paper, or can be applied in the modification of h-index and g-index to represent the achievement of a scientist on the whole. The modified h-index and g-index of an author makes use of more papers in which he or she played important roles. Our method is suitable for the papers with wide range of author numbers.     

Detecting <Emphasis Type="Italic">h</Emphasis>-index manipulation through self-citation analysis

The h-index has received an enormous attention for being an indicator that measures the quality of researchers and organizations. We investigate to what degree authors can inflate their h-index through strategic self-citations with the help of a simulation. We extended Burrell’s publication model with a procedure for placing self-citations, following three different strategies: random self-citation, recent self-citations and h-manipulating self-citations. The results show that authors can considerably inflate their h-index through self-citations. We propose the q-index as an indicator for how strategically an author has placed self-citations, and which serves as a tool to detect possible manipulation of the h-index. The results also show that the best strategy for an high h-index is publishing papers that are highly cited by others. The productivity has also a positive effect on the h-index.     

Rational (successive) <Emphasis Type="Italic">h</Emphasis>-indices: An application to economics in the Republic of Ireland

We rank economics departments in the Republic of Ireland according to the number of publications, number of citations, and successive h-index of research-active staff. We increase the discriminatory power of the h ₁-index by introducing three generalizations, each of which is a rational number. The first (h ₁ ⁺) measures the excess over the actual h-index, while the other two (h ₁*, h ₁ ^Δ) measures the distance to the next h-index. At the individual level, h* and h ^Δ coincide while h ⁺ is undefined.     

The π<Subscript>v</Subscript>-index: a new indicator to characterize the impact of journals

For determining the eminence of scientific journals, a new indicator stressing the importance of papers in the “elite set” (i.e., highly cited papers) is suggested. The number of papers in the elite set (P _πv) is calculated with the equation: (10 log P) − 10, where P is the total number of papers in the set. The one-hundredth of citations (C) obtained by P _πv papers is regarded as the π_v-index which is field and time dependent. The π_v-index is closely correlated with the citedness (C/P) of P _πv papers, and it is also correlated with the Hirsch-index. Three types of Hirsch-sets are distinguished, depending on the relation of the number of citations received by the Hirsch-paper (ranked as h) and the paper next in rank (h + 1) by citation. The h-index of an Anomalous Hirsch-set (AH) may be increased by a single citation to a paper outside the Hirsch-core. (A set of papers may be regarded as AH, where the number of citations to the Hirsch-paper is higher than the h-index and the next paper in rank shows as many citations as the value of the h-index.)     

The effect of database dirty data on h-index calculation

As all databases, the bibliometric ones (e.g. Scopus, Web of Knowledge and Google Scholar) are not exempt from errors, such as missing or wrong records, which may obviously affect publication/citation statistics and—more in general—the resulting bibliometric indicators. This paper tries to answer to the question “What is the effect of database uncertainty on the evaluation of the h-index?”, breaking the paradigm of deterministic database analysis and treating responses to database queries as random variables. Precisely an informetric model of the h-index is used to quantify the variability of this indicator with respect to the variability stemming from errors in database records. Some preliminary results are presented and discussed.     

Experimenting with the partnership ability φ-index on a million computer scientists

Schubert introduced the partnership ability φ-index relying on a researcher’s number of co-authors and collaboration rate. As a Hirsch-type index, φ was expected to be consistent with Schubert–Glänzel’s model of h-index. Schubert demonstrated this relationship with the 34 awardees of the Hevesy medal in the field of nuclear and radiochemistry (r ² = 0.8484). In this paper, we upscale this study by testing the φ-index on a million researchers in computer science. We found that the Schubert–Glänzel’s model correlates with the million empirical φ values (r ² = 0.8695). In addition, machine learning through symbolic regression produces models whose accuracy does not exceed a 6.1 % gain (r ² = 0.9227). These results suggest that the Schubert–Glänzel’s model of φ-index is accurate and robust on the domain-wide bibliographic dataset of computer science.