Bounds and Inequalities Relating h-Index, g-Index, e-Index and Generalized Impact Factor

Finding relationships among different indices such as h-index, g-index, e-index, and generalized impact factor is a challenging task. In this paper, we describe some bounds and inequalities relating h-index, g-index, e-index, and generalized impact factor. We derive the bounds and inequalities relating these indexing parameters from their basic definitions and without assuming any continuous model to be followed by any of them.Comment: 17 pages, 6 figures, 5 table

Specific character of citations in historiography (using the example of Polish history)

The first part of the paper deals with the assessment of international databases in relation to the number of historical publications (representation and relevance in comparison with the model database). The second part is focused on providing answer to the question whether historiography is governed by similar bibliometric rules as exact sciences or whether it has its own specific character. Empirical database for this part of the research constituted the database prepared ad hoc: The Citation Index of the History of Polish Media (CIHPM). Among numerous typically historical features the main focus was put on: linguistic localism, specific character of publishing forms, differences in citing of various sources (contributions and syntheses) and specific character of the authorship (the Lorenz Curve and the Lotka’s Law). Slightly more attention was devoted to the half-life indicator and its role in a diachronic study of a scientific field; also, a new indicator (HL14), depicting distribution of citations younger then half-life was introduced. Additionally, the comparison and correlation of selected parameters for the body of historical science (citations, HL14, the Hirsch Index, number of publications, volume and other) were also conducted

Ten Simple Rules for Getting Help from Online Scientific Communities

The increasing complexity of research requires scientists to work at the intersection of multiple fields and to face problems for which their formal education has not prepared them. For example, biologists with no or little background in programming are now often using complex scripts to handle the results from their experiments; vice versa, programmers wishing to enter the world of bioinformatics must know about biochemistry, genetics, and other fields. In this context, communication tools such as mailing lists, web forums, and online communities acquire increasing importance. These tools permit scientists to quickly contact people skilled in a specialized field. A question posed properly to the right online scientific community can help in solving difficult problems, often faster than screening literature or writing to publication authors. The growth of active online scientific communities, such as those listed in Table S1, demonstrates how these tools are becoming an important source of support for an increasing number of researchers. Nevertheless, making proper use of these resources is not easy. Adhering to the social norms of World Wide Web communication—loosely termed “netiquette”—is both important and non-trivial. In this article, we take inspiration from our experience on Internet-shared scientific knowledge, and from similar documents such as “Asking the Questions the Smart Way” and “Getting Answers”, to provide guidelines and suggestions on how to use online communities to solve scientific problems

Metrics to evaluate research performance in academic institutions: A critique of ERA 2010 as applied in forestry and the indirect H2 index as a possible alternative

Excellence for Research in Australia (ERA) is an attempt by the Australian Research Council to rate Australian universities on a 5-point scale within 180 Fields of Research using metrics and peer evaluation by an evaluation committee. Some of the bibliometric data contributing to this ranking suffer statistical issues associated with skewed distributions. Other data are standardised year-by-year, placing undue emphasis on the most recent publications which may not yet have reliable citation patterns. The bibliometric data offered to the evaluation committees is extensive, but lacks effective syntheses such as the h-index and its variants. The indirect H2 index is objective, can be computed automatically and efficiently, is resistant to manipulation, and a good indicator of impact to assist the ERA evaluation committees and to similar evaluations internationally.Comment: 19 pages, 6 figures, 7 tables, appendice

What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?

The paper is concerned with analysing what makes a great journal great in the sciences, based on quantifiable Research Assessment Measures (RAM). Alternative RAM are discussed, with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). Various ISI RAM that are calculated annually or updated daily are defined and analysed, including the classic 2-year impact factor (2YIF), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, PI-BETA (Papers Ignored - By Even The Authors), Impact Factor Inflation (IFI), and three new RAM, namely Historical Self-citation Threshold Approval Rating (H-STAR), 2 Year Self-citation Threshold Approval Rating (2Y-STAR), and Cited Article Influence (CAI). The RAM data are analysed for the 6 most highly cited journals in 20 highly-varied and well-known ISI categories in the sciences, where the journals are chosen on the basis of 2YIF. The application to these 20 ISI categories could be used as a template for other ISI categories in the sciences and social sciences, and as a benchmark for newer journals in a range of ISI disciplines. In addition to evaluating the 6 most highly cited journals in each of 20 ISI categories, the paper also highlights the similarities and differences in alternative RAM, finds that several RAM capture similar performance characteristics for the most highly cited scientific journals, determines that PI-BETA is not highly correlated with the other RAM, and hence conveys additional information regarding research performance. In order to provide a meta analysis summary of the RAM, which are predominantly ratios, harmonic mean rankings are presented of the 13 RAM for the 6 most highly cited journals in each of the 20 ISI categories. It is shown that emphasizing THE impact factor, specifically the 2-year impact factor, of a journal to the exclusion of other informative RAM can lead to a distorted evaluation of journal performance and influence on different disciplines, especially in view of inflated journal self citations

A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions

The large amount of information contained in bibliographic databases has recently boosted the use of citations, and other indicators based on citation numbers, as tools for the quantitative assessment of scientific research. Citations counts are often interpreted as proxies for the scientific influence of papers, journals, scholars, and institutions. However, a rigorous and scientifically grounded methodology for a correct use of citation counts is still missing. In particular, cross-disciplinary comparisons in terms of raw citation counts systematically favors scientific disciplines with higher citation and publication rates. Here we perform an exhaustive study of the citation patterns of millions of papers, and derive a simple transformation of citation counts able to suppress the disproportionate citation counts among scientific domains. We find that the transformation is well described by a power-law function, and that the parameter values of the transformation are typical features of each scientific discipline. Universal properties of citation patterns descend therefore from the fact that citation distributions for papers in a specific field are all part of the same family of univariate distributions.Comment: 9 pages, 6 figures. Supporting information files available at http://filrad.homelinux.or

Does Habitual Physical Activity Increase the Sensitivity of the Appetite Control System? A Systematic Review.

BACKGROUND: It has been proposed that habitual physical activity improves appetite control; however, the evidence has never been systematically reviewed. OBJECTIVE: To examine whether appetite control (e.g. subjective appetite, appetite-related peptides, food intake) differs according to levels of physical activity. DATA SOURCES: Medline, Embase and SPORTDiscus were searched for articles published between 1996 and 2015, using keywords pertaining to physical activity, appetite, food intake and appetite-related peptides. STUDY SELECTION: Articles were included if they involved healthy non-smoking adults (aged 18-64 years) participating in cross-sectional studies examining appetite control in active and inactive individuals; or before and after exercise training in previously inactive individuals. STUDY APPRAISAL AND SYNTHESIS: Of 77 full-text articles assessed, 28 studies (14 cross-sectional; 14 exercise training) met the inclusion criteria. RESULTS: Appetite sensations and absolute energy intake did not differ consistently across studies. Active individuals had a greater ability to compensate for high-energy preloads through reductions in energy intake, in comparison with inactive controls. When physical activity level was graded across cross-sectional studies (low, medium, high, very high), a significant curvilinear effect on energy intake (z-scores) was observed. LIMITATIONS: Methodological issues existed concerning the small number of studies, lack of objective quantification of food intake, and various definitions used to define active and inactive individuals. CONCLUSION: Habitually active individuals showed improved compensation for the energy density of foods, but no consistent differences in appetite or absolute energy intake, in comparison with inactive individuals. This review supports a J-shaped relationship between physical activity level and energy intake. Further studies are required to confirm these findings. PROSPERO REGISTRATION NUMBER: CRD42015019696

Matrix metalloproteinase-9 activity and a downregulated Hedgehog pathway impair blood-brain barrier function in an <i>in vitro</i> model of CNS tuberculosis

Central nervous system tuberculosis (CNS TB) has a high mortality and morbidity associated with severe inflammation. The blood-brain barrier (BBB) protects the brain from inflammation but the mechanisms causing BBB damage in CNS TB are uncharacterized. We demonstrate that Mycobacterium tuberculosis (Mtb) causes breakdown of type IV collagen and decreases tight junction protein (TJP) expression in a co-culture model of the BBB. This increases permeability, surface expression of endothelial adhesion molecules and leukocyte transmigration. TJP breakdown was driven by Mtb-dependent secretion of matrix metalloproteinase (MMP)-9. TJP expression is regulated by Sonic hedgehog (Shh) through transcription factor Gli-1. In our model, the hedgehog pathway was downregulated by Mtb-stimulation, but Shh levels in astrocytes were unchanged. However, Scube2, a glycoprotein regulating astrocyte Shh release was decreased, inhibiting Shh delivery to brain endothelial cells. Activation of the hedgehog pathway by addition of a Smoothened agonist or by addition of exogenous Shh, or neutralizing MMP-9 activity, decreased permeability and increased TJP expression in the Mtb-stimulated BBB co-cultures. In summary, the BBB is disrupted by downregulation of the Shh pathway and breakdown of TJPs, secondary to increased MMP-9 activity which suggests that these pathways are potential novel targets for host directed therapy in CNS TB

QRTEngine: An easy solution for running online reaction time experiments using Qualtrics

Performing online behavioral research is gaining increased popularity among researchers in psychological and cognitive science. However, the currently available methods for conducting online reaction time experiments are often complicated and typically require advanced technical skills. In this article, we introduce the Qualtrics Reaction Time Engine (QRTEngine), an open-source JavaScript engine that can be embedded in the online survey development environment Qualtrics. The QRTEngine can be used to easily develop browser-based online reaction time experiments with accurate timing within current browser capabilities, and it requires only minimal programming skills. After introducing the QRTEngine, we briefly discuss how to create and distribute a Stroop task. Next, we describe a study in which we investigated the timing accuracy of the engine under different processor loads using external chronometry. Finally, we show that the QRTEngine can be used to reproduce classic behavioral effects in three reaction time paradigms: a Stroop task, an attentional blink task, and a masked-priming task. These findings demonstrate that QRTEngine can be used as a tool for conducting online behavioral research even when this requires accurate stimulus presentation times

A comment to the paper by Waltman et al., Scientometrics, 87, 467–481, 2011

In reaction to a previous critique (Opthof and Leydesdorff, J Informetr 4(3):423–430, 2010), the Center for Science and Technology Studies (CWTS) in Leiden proposed to change their old “crown” indicator in citation analysis into a new one. Waltman (Scientometrics 87:467–481, 2011a) argue that this change does not affect rankings at various aggregated levels. However, CWTS data is not publicly available for testing and criticism. Therefore, we comment by using previously published data of Van Raan (Scientometrics 67(3):491–502, 2006) to address the pivotal issue of how the results of citation analysis correlate with the results of peer review. A quality parameter based on peer review was neither significantly correlated with the two parameters developed by the CWTS in the past citations per paper/mean journal citation score (CPP/JCSm) or CPP/FCSm (citations per paper/mean field citation score) nor with the more recently proposed h-index (Hirsch, Proc Natl Acad Sci USA 102(46):16569–16572, 2005). Given the high correlations between the old and new “crown” indicators, one can expect that the lack of correlation with the peer-review based quality indicator applies equally to the newly developed ones