Evolving importance of anticancer research using herbal medicine: a scientometric analysis

Phytocompounds and herbal extracts have been utilized in Ayurveda, Siddha and Unani medicine since thousands of years for treatment of various ailments. Success behind herbal medicine strongly suggests the interaction between bio-active phytocompounds with crucial biochemical pathways in a human body without causing adverse effects. The increasing incidence of diseases like cancer instigated the scientific world to focus intently on their pathophysiology and prevention, leading to accelerated research activity in past three decades. This study aims at understanding the evolving global importance of herbal medicine and quality of research against various cancers through scientometric analyses by studying the output from research publications, followed by the contribution from various countries, research institutes, authors, scientific journals and areas of research. To visualize the research structure and dynamics, more than 5000 publications with Science Citation Index that appeared from 1984 to 2013 have been studied and compared for a trend in its growth of publication along with the contributions from various bibliometric parameters stated above. After using the ‘Web of Science’ database it became well evident that the concerned bibliometric parameters contributed substantially in projecting the overall scientific output in the field of herbal anticancer research as reflected from the citation analysis and h-index data. It has been observed that the number of publications increased with compound annual growth rate of 10.39% during the studied periods. Evolving trend of the research topics was visualised by drawing the keyword co-occurrence map in this field.     

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.     

Evaluation of <Emphasis Type="Italic">h</Emphasis>-index,its variants and extensions based on publication age &amp; citation intensity in civil engineering

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards.     

To be the Prince to wake up Sleeping Beauty: the rediscovery of the delayed recognition studies

In science, sleeping papers, previously known as “Sleeping Beauties”, refer to scientific papers that are recognized by the scientific community after a long hibernation period following their publication. Many factors may contribute to their delayed yet exceptional popularity, such as the introduction of new technologies or ideas that are beyond the capabilities at the time of publication. The recognition of a sleeping paper, often through a paper that cites the sleeping paper and has a profound impact on the research area, is important to the scientific community. Here, we proposed a method to identify the paper that rediscovers a sleeping paper, known as a rediscovering paper, based on the citation network of the sleeping paper. Based on the 15 rediscovering papers obtained from the top sleeping papers in science, we introduced 5 feature indices of the leading authors of these rediscovering papers (rediscovering authors) defined by an academic search system AMiner (https://cn.aminer.org/). The 5 feature indices depict academic achievements of researchers from various aspects: Publication, Citation, Longevity, H-index and Sociability. The rediscovering authors lead to most general scientific authors in the 5 feature indices. Our results reveal common features of potential rediscovering authors in the scientific community who may play significant roles in the propagation of citation networks.     

A scientometrics law about co-authors and their ranking: the co-author core

Rather than “measuring” a scientist impact through the number of citations which his/her published work can have generated, isn’t it more appropriate to consider his/her value through his/her scientific network performance illustrated by his/her co-author role, thus focussing on his/her joint publications, and their impact through citations? Whence, on one hand, this paper very briefly examines bibliometric laws, like the h-index and subsequent debate about co-authorship effects, but on the other hand, proposes a measure of collaborative work through a new index. Based on data about the publication output of a specific research group, a new bibliometric law is found. Let a co-author C have written J (joint) publications with one or several colleagues. Rank all the co-authors of that individual according to their number of joint publications, giving a rank r to each co-author, starting with r = 1 for the most prolific. It is empirically found that a very simple relationship holds between the number of joint publications J by coauthors and their rank of importance, i.e., J ∝ 1/r. Thereafter, in the same spirit as for the Hirsch core, one can define a “co-author core”, and introduce indices operating on an author. It is emphasized that the new index has a quite different (philosophical) perspective that the h-index. In the present case, one focusses on “relevant” persons rather than on “relevant” publications. Although the numerical discussion is based on one “main author” case, and two “control” cases, there is little doubt that the law can be verified in many other situations. Therefore, variants and generalizations could be later produced in order to quantify co-author roles, in a temporary or long lasting stable team(s), and lead to criteria about funding, career measurements or even induce career strategies.     

DeepPatent: patent classification with convolutional neural networks and word embedding

Keyword analysis has been an important research theme in bibliometrics. The deduction of new valuable bibliometric indicators/approaches through keyword analysis is important for prompting the further development of this subject area. In this study, the following three bibliometric indicators/approaches were thus derived. Indicator K was derived using the ratio between the average unique keyword number and average keyword frequency of a discipline for quantitatively describing the discipline’s development stages highlighted by scientific-philosopher Kuhn. Next, the correlation matrix analysis was used after k-core filtration to quantitatively expose the detailed correlations between topics for a large network. Thirdly, indicators I (node betweenness divided by node degree) and C (clustering coefficient) were collectively introduced to predict potential growth keywords. Diverse topical evolutions were categorized into a strategic diagram according to the tendencies of I and C. With sustainable development as a case study, we verified that the three new bibliometric indicators/approaches work well and can realize many new concepts beyond the scope of available indicators or approaches. In summary, the present paper makes a renewed effort to promote the development of bibliometrics. We hope our work could catalyze the further studies from the communities in the scientometric fields.     

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.     

Reflection of co-authorship networks in the Web: Web hyperlinks versus Web visibility rates

About ten years ago a new research field called “webometrics” emerged. Similarities between methods used in webometrics and scientometrics or informetrics are evident from the literature. Are there also similarities between scientometric and Web indicators of collaboration for possible use in technology policy making? Usually, the bibliometric method used to study collaboration is the investigation of co-authorships.In this paper, Web hyperlinks and Web visibility indicators are examined to establish their usefulness as indicators of collaboration and to explore whether similarities exist between Web-based structures and bibliographic structures.Three empirical studies of collaboration between institutions and individual scientists show that hyperlink structures at the Web don’t reflect collaboration structures collected by bibliographic data. However Web visibility indicators of collaboration are different from hyperlinks and can be successfully used as Web indicators of collaboration.     

<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.     

Rainbow ranking: an adaptable,multidimensional ranking method for publication sets

Various scientometric indices have been proposed in an attempt to express the quantitative and qualitative characteristics of scientific output. However, fully capturing the performance and impact of a scientific entity (author, journal, institution, conference, etc.) still remains an open research issue, as each proposed index focuses only on particular aspects of scientific performance. Therefore, scientific evaluation can be viewed as a multi-dimensional ranking problem, where dimensions represent the assorted scientometric indices. To address this problem, the skyline operator has been proposed in Sidiropoulos et al. (J Informetr 10(3):789–813, 2016) with multiple combinations of dimensions. In the present work, we introduce a new index derived from the utilization of the skyline operator, called Rainbow Ranking or RR-index that assigns a category score to each scientific entity instead of producing a strict ordering of the ranked entities. Our RR-index allows the combination of any known indices depending on the purposes of the evaluation and outputs a single number metric expressing multi-criteria relative ranking and can be applied to any scientific entity such as authors and journals. The proposed methodology was experimentally evaluated using a dataset of over 105,000 scientists from the Computer Science field.     

The impact of the socio-economic crisis of 2001 on the scientific system of Argentina from the scientometric perspective

In recent years a number of studies have focused on Argentina’s 2001 economic crisis and its political, social, and institutional repercussions. To date, however, no studies have analyzed its effects upon the country’s scientific system from a scientometric perspective, in terms of resources dedicated to scientific activity and the final output and impact. The present study does so by means of a set of scientometric indicators that reflect economic effort, human resources dedicated to research, publications, collaborative relations, and the international visibility of scientific contributions.     

Letter to the editor: Dimensionless citation indicators

Bornmann and Leydesdorff (Scientometrics, 2018.  https://doi.org/10.1007/s11192-018-2682-1) make an excellent case to use normalized citation based indicators instead of the h-index. In this Letter to the Editor we point out that h/P, p/P and i/P are all very simple and intuitive dimensionless citation indicators where h is the Hirsch h-index, p the performance index of Prathap, and i the value of impact as measured by the ratio of citations C to Publications P.     

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.     

New options for team research via international computer networks

In this paper, international team research (ITR) is discussed as an object for measurement, systems analysis, and management. The paper is intended as a contribution to the development of a problem orientation in scientometrics. In the authors' view, scientometric studies can help solve the problem of efficient ITR in several ways-for instance, by identifying needed improvements in (a) international scientific communication in general, (b) scientific interactions within and among research teams as part of the creative process, and (c) the overall international usage of existing scientific knowledge. The paper discusses the machinery of ITR, models of scientific communication, and some original experience in using computers and telecommunications as tools for scientific interaction. All of these are potential objects for scientometric investigation. The paper itself performs some first steps in obtaining economic parameters for the different forms of international team research.This article is an abbreviated and adapted version of the more comprehensive research results published in Refs.^26–29     

Research and innovation in South African universities: from the triple helix’s perspective

Different from most prior research studying the influence of networks on scientific knowledge creation by taking on a single social-based perspective, this study offers an integrated frame based upon both social-based and knowledge-based views. This study incorporates quantitative bibliometric methods of analysis and network analysis. Based on 51,972 alternative energy patents in 1998–2012, we build knowledge networks composed of technological classifications and social networks capturing collaborations. Based on patent citation networks, we use citations in every period to calculate researcher’s knowledge creation performance. We hypothesize that centrality of researchers in the knowledge network and their knowledge creation display an inverted-U relationship. What’s more, we propose that this curvilinear relation is moderated by their structural holes and centrality in the collaboration network. Based on the negative binomial model with fixed effects and robust tests, results indicate that (1) locations of the researchers in knowledge network contribute more to knowledge creation when their centrality is moderate, rather than high or low; (2) at a moderate level of knowledge network centrality, the researchers, who occupy more central positions or span more structural holes in collaboration network, will benefit more than the ones who do not. The findings of this research, besides having implications for research on knowledge networks, have implications on scientometric indicators, multiple networks study, and knowledge creation process.     

A Process-Based Explanation of the Psychic Distance Paradox: Evidence from Global Virtual Teams

Previous research has found evidence of a counter-intuitive positive relationship between psychic distance and performance, which has been labeled the “psychic distance paradox”. However, there is a dearth of literature explaining the causal mechanisms that elucidates such a positive relationship. Studying the effect of team-level psychic distance on the performance of global virtual teams, we build on the input-process-outcome framework of team research, which allows the integration of process variables to provide new insights into the underlying coherences of the psychic distance paradox. These variables include the team members’ expectation of challenges as well as the level of team effort toward the task. The team members’ motivational cultural intelligence is introduced to the model as a moderating factor. The data support the hypothesized causal path. The findings start unveiling the psychic distance paradox through the integration of the literatures on psychic distance and global virtual teams.     

China’s rising leadership in science and technology: quantitative and qualitative indicators

China has a long and proud history of world leadership in science and technology, but in the past two centuries it has experienced a period of instability that has challenged that leadership. However, since its political consolidation in the middle part of the 20th Century and its subsequent economic reforms, China’s rise in science has been meteoric. This rise was first detected by the scientometric community through its indicators, but it has now become obvious. Indeed in 2017 the question, “Will China come to lead world science?” was becoming to some, “Does China already lead world science?” This paper tries to make the case that the answer is “yes” (or at least “soon”)—but the answer depends on which metrics one considers. China already leads many countries in some measures of GDP, scientific paper production, researchers, plus high technology manufacturing and exports. China also recently passed the European Union in R&D investment. Even in some of those indicators where China has not yet taken the lead, reasonable forecasts predict that it soon will. However, there are some indicators where China is still far behind. For example while rising, it still lags the U.S. and EU in citations in Western publications, and will take years to catch up. Here, these quantitative measures are supplemented by qualitative ones from WTEC assessments and by survey results of scientists and the public, which present a more nuanced conclusion. While Chinese leadership may be difficult for Westerners to accept, it can be viewed as China merely regaining its historical position of leadership in science and technology.