Scientometrics (SCIENTOMETRICS)

Publisher: Akadémiai Kiadó

Journal description

Scientometrics aims at publishing original studies short communications preliminary reports review papers letters to the editor and book reviews on scientometrics. The topics covered are results of research concerned with the quantitative features and characteristics of science. Emphasis is placed on investigations in which the development and mechanism of science are studied by means of (statistical) mathematical methods. The Journal also provides the reader with important up-to-date information about international meetings and events in scientometrics and related fields. Appropriate bibliographic compilations are published as a separate section. Due to its fully interdisciplinary character Scientometrics is indispensable to research workers and research administrators throughout the world. It provides valuable assistance to librarians and documentalists in central scientific agencies ministries research institutes and laboratories. Scientometrics includes the Journal of Research Communication Studies. Consequently its aims and scope cover that of the latter namely to bring the results of research investigations together in one place in such a form that they will be of use not only to the investigators themselves but also to the entrepreneurs and research workers who form the object of these studies.

Current impact factor: 2.27

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 2.274
2012 Impact Factor 2.133
2011 Impact Factor 1.966
2010 Impact Factor 1.905
2009 Impact Factor 2.167
2008 Impact Factor 2.328
2007 Impact Factor 1.472
2006 Impact Factor 1.363
2005 Impact Factor 1.738
2004 Impact Factor 1.12
2003 Impact Factor 1.251
2002 Impact Factor 0.855
2001 Impact Factor 0.676
2000 Impact Factor 0.66
1999 Impact Factor 0.931
1998 Impact Factor 0.71
1997 Impact Factor 0.691
1996 Impact Factor 0.582
1995 Impact Factor 0.444
1994 Impact Factor 0.593
1993 Impact Factor 0.519
1992 Impact Factor 0.634

Impact factor over time

Impact factor

Additional details

5-year impact 2.21
Cited half-life 6.50
Immediacy index 0.45
Eigenfactor 0.01
Article influence 0.60
Website Scientometrics website
Other titles Scientometrics (Online)
ISSN 0138-9130
OCLC 45496762
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Akadémiai Kiadó

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Authors own final version only can be archived
    • Publisher's version/PDF cannot be used
    • On author's personal website or institutional repository or any repository mandated by Author's funding body
    • Published source must be acknowledged
    • Must state that the file is not the final published version of the paper
    • Must link to publisher version([DOI of the Article without brackets])
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper intends to describe the population evolution of a scientific information web service during 2011-2012. Quarterly samples from December 2011 to December 2012 were extracted from Google Scholar Citations to analyse the number of members, distribution of their bibliometric indicators, positions, institutional and country affiliations and the labels to describe their scientific activity. Results show that most of the users are young researchers, with a starting scientific career and mainly from disciplines related to information sciences and technologies. Another important result is that this service is settled by waves emanating from specific institutions and countries. This work concludes that this academic social network presents some biases in the population distribution that does not make it representative of the real scientific population.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1593-7
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper provides a formal study on manuscript quality control in peer review. Within this analysis, a biased editor is defined operationally as an editor that exerts a higher (lower) level of quality control. Here we show that if the editor is more biased than the manuscript’s author then the author undertakes the type of revision that the editor prefers instead of following his or her own opinion. Moreover, authors with a strong belief about the required level of quality control will be very motivated under editors who agree with them. By contrast, when authors do not undertake the revision type that the editor prefers, they will be very demotivated under editors that exert a different level of quality control and more so as the associate editor is more biased. The effects of editors’ bias on authors’ satisfaction and motivation cause sorting in the authors who submit manuscripts to scholarly journals, and therefore, match authors and journals with similar quality standards. It will decrease the demotivating effect that editors’ bias had on some authors, so that bias becomes more effective at the peer review stage. Moreover, some journals will be forced to lower the quality standards in order to be able to compete with journals of more biased editors. This paper also shows that, under fairly weak conditions, it is optimal for the Editor-in-Chief to assign manuscripts to an editor that exerts a quality control higher than the journal’s standard, against the competing journal whose editor holds the journal’s standard.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1566-x
  • [Show abstract] [Hide abstract]
    ABSTRACT: The use of quantitative performance measures to evaluate the productivity, impact and quality of research has spread to almost all parts of public R&D systems, including Big Science where traditional measures of technical reliability of instruments and user oversubscription have been joined by publication counts to assess scientific productivity. But such performance assessment has been shown to lead to absurdities, as the calculated average cost of single journal publications easily may reach hundreds of millions of dollars. In this article, the issue of productivity and impact is therefore further qualified by the use of additional measures such as the immediacy index as well as network analysis to evaluate qualitative aspects of the impact of contemporary Big Science labs. Connecting to previous work within what has been called “facilitymetrics”, the article continues the search for relevant bibliometric measures of the performance of Big Science labs with the use of a case study of a recently opened facility that is advertised as contributing to “breakthrough” research, by using several more measures and thus qualifying the topic of performance evaluation in contemporary Big Science beyond simple counts of publications, citations, and costs.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1577-7
  • [Show abstract] [Hide abstract]
    ABSTRACT: The objective of this research is to examine the dynamic impact and diffusion patterns at the subfield level. Using a 15-year citation data set, this research reveals the characteristics of the subfields of computer science from the aspects of citation characteristics, citation link characteristics, network characteristics, and their dynamics. Through a set of indicators including incoming citations, number of citing areas, cited/citing ratios, self-citations ratios, PageRank, and betweenness centrality, the study finds that subfields such as Computer Science Applications, Software, Artificial Intelligence, and Information Systems possessed higher scientific trading impact. Moreover, it also finds that Human-Computer Interaction, Computational Theory and Mathematics, and Computer Science Applications are among the subfields of computer science that gained the fastest growth in impact. Additionally, Engineering, Mathematics, and Decision Sciences form important knowledge channels with subfields in computer science.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1594-6
  • [Show abstract] [Hide abstract]
    ABSTRACT: The South Korea’s innovation system has been transformed in tandem with rapid economic growth over the last three decades. In order to explore the evolution process of the innovation system in Korea, this study examines the trends and patterns in collaboration activities among the triple helix actors, such as university, industry, and government (UIG), using co-patent data. The triple helix framework is employed to analyze innovation dynamics within the networks of the bi- and trilateral relations embedded in patent collaborations. The analyses focus on how the triple helix dynamics have been shaped and transformed in the course of development of the innovation system. The results reveal that collaboration activities among UIG largely increased across three developmental phases from 1980 to 2012. In the early periods, strategic R&D alliances between industry and government sector were set up to strengthen enterprises’ innovation capabilities. When the Korean large conglomerates, Chaebols, became a dominant driver of domestic innovation activities, the primary agents of the collaborations shifted from industry-government to industry-university. The network analysis shows that university-industry collaboration is the strongest within the triple helix in recent years, followed by industry-government relations and then UIG relations. The tripartite collaboration has emerged with the rise of entrepreneur universities, but its network has rather been weak and inactive. While Korea has experienced a transition from statist model to a triple helix, the full-fledged triple helix model has not been established yet.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1541-6
  • [Show abstract] [Hide abstract]
    ABSTRACT: The large amounts of publicly available bibliographic repositories on the web provide us great opportunities to study the scientific behaviors of scholars. This paper aims to study the way we collaborate, model the dynamics of collaborations and predict future collaborations among authors. We investigate the collaborations in three disciplines including physics, computer science and information science,and different kinds of features which may influence the creation of collaborations. Path-based features are found to be particularly useful in predicting collaborations. Besides, the combination of path-based and attribute-based features achieves almost the same performance as the combination of all features considered. Inspired by the findings, we propose an agent-based model to simulate the dynamics of collaborations. The model merges the ideas of network structure and node attributes by leveraging random walk mechanism and interests similarity. Empirical results show that the model could reproduce a number of realistic and critical network statistics and patterns. We further apply the model to predict collaborations in an unsupervised manner and compare it with several state-of-the-art approaches. The proposed model achieves the best predictive performance compared with the random baseline and other approaches. The results suggest that both network structure and node attributes may play an important role in shaping the evolution of collaboration networks.
    Scientometrics 07/2015; 104(1). DOI:10.1007/s11192-015-1597-3
  • [Show abstract] [Hide abstract]
    ABSTRACT: Detecting intellectual structure of a knowledge domain is valuable to track the dynamics of scientific research. Formal concept analysis (FCA) provides a new perspective for knowledge discovery and data mining. In this paper we introduce a FCA-based approach to detect intellectual structure of library and information science (LIS). Our approach relies on the mathematical theory which formulates the understanding of “concept” as a unit of extension (scholars) and intension (keywords) as a way of modelling the intellectual structure of a domain. By analyzing the papers published in sixteen prominent journals of LIS domain from 2001 to 2013, the intellectual structure of LIS in the new century has been identified and visualized. Nine major research themes of LIS were detected together with the core keywords and authors to describe each theme. The significant advantage of our approach is that the mathematical formulae produce a conceptual structure which automatically provides generalization and specialization relationships among the concepts. This provides additional information not available from other methods, especially when shared interests of authors from different granularities are also visualized in concept lattice.
    Scientometrics 06/2015; DOI:10.1007/s11192-015-1629-z
  • [Show abstract] [Hide abstract]
    ABSTRACT: We describe the structural dynamics of two groups of scientists in relation to the independent simultaneous discovery (i.e., definition and application) of Linear Canonical Transforms. This mathematical construct was built as the transfer kernel of paraxial optical systems by Prof. Stuart A. Collins, working in the ElectroScience Laboratory in Ohio State University. At roughly the same time, it was established as the integral kernel that represents the preservation of uncertainty in quantum mechanics by Prof. Marcos Moshinsky and his postdoctoral associate, Dr. Christiane Quesne, at the Instituto de Física of the Universidad Nacional Autónoma de México. We are interested in the birth and parallel development of the two follower groups that have formed around the two seminal articles, which for more than two decades did not know and acknowledge each other. Each group had different motivations, purposes and applications, and worked in distinct professional environments. As we will show, Moshinsky-Quesne had been highly cited by his associates and students in Mexico and Europe when the importance of his work started to permeate various other mostly theoretical fields; Collins’ paper took more time to be referenced, but later originated a vast following notably among Chinese applied optical scientists. Through social network analysis we visualize the structure and development of these two major coauthoring groups, whose community dynamics shows two distinct patterns of communication that illustrate the disparity in the diffusion of theoretical and technological research.
    Scientometrics 06/2015; DOI:10.1007/s11192-015-1602-x
  • [Show abstract] [Hide abstract]
    ABSTRACT: Aiming to investigate the citation advantage of author-pays model, the present communication compares open access (OA) and Toll Access (TA) papers recognition in author-pays OA journals in 2007–2011. This is the first large scale study concentrating on all APC-funded OA journals published by Springer and Elsevier as the two greatest publishers authorizing and embracing the model. According to the research findings, the OA papers have been exponentially increased in recent years. They are, also, found to outperform the TA ones in their impacts whether in the annual comparisons or across disciplines. The annual OA citation advantages range from 21.36 % for 2009 to 49.71 % for 2008. Social Sciences and Humanities (with 3.14 %) and Natural Sciences (with 35.95 %) gain the lowest and the highest advantages, respectively. The citation advantage can be attributed to the higher visibility of the OA articles, implying the popularity and usefulness of the OA author-pays model to their readership. It may, also, have roots in the selectivity of the authors in choosing the author-pays outlet to publish their high-quality papers, signifying the overall prestige of the OA papers published in the model. Whatever may be the ultimate interpretation, i.e. correlation or causation, the OA citation advantage may encourage the authors who are willing to support OA movement, while seeking to get published in the well-established traditional journals. This may help approach the not-yet-achieved critical mass necessary to evaluate the success of the model.
    Scientometrics 06/2015; DOI:10.1007/s11192-015-1607-5
  • [Show abstract] [Hide abstract]
    ABSTRACT: Quantitative evaluation of citation data to support funding decisions has become widespread. For this purpose there exist many measures (indices) and while their properties were well studied there is little comprehensive experimental comparison of the ranking lists obtained when using different methods. A further problem of the existing studies is that lack of available data about net citations prevents researchers from studying the effect of measuring scientific impact by using net citations (all citations minus self-citations). In this paper we use simulated data to study factors that could potentially influence the degree of agreement between the rankings obtained when using different indices with the emphasis given to the comparison of the number of net citations per author to other more established indices. We observe that the researchers publishing papers with a large number of co-authors are systematically ranked higher when using h-index or total citations (TC) instead of the number of citations per author (TCA), that the researchers who publish a small proportion of papers which receive many citations while the rest of their papers receive only few citations are systematically ranked higher when using TCA or TC instead of h-index, and that the authors who have lower proportion of self-citations are ranked higher when considering indices which include the number of net citations in comparison with indices considering only the total citation count. Results are verified and illustrated also by analyzing a large dataset from the field of medical science in Slovenia for the period 1986–2007.
    Scientometrics 06/2015; DOI:10.1007/s11192-015-1622-6
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tenure decisions and university rankings are just two examples where interfield comparison of academic output is needed. There are differences in publication performances among fields when the number of papers is used as the quantity measure and the Journal Impact Factor is used as the quality measure. For example, it is well known that the economics departments publish less than the chemistry departments and their journals have less impact factors. But there is no consensus on the magnitude of the difference and the methodology for the adjustment. Every decision maker makes his own adjustment and uses a different formula. In this paper, we quantify the publication performance differences among nine academic fields by using data from 1417 departments in the United States. We use two quality measures. First we weigh the publications by the impact factor of the journals. Second, we only consider the publications in the journals that are in the top quartile of the subject categories. We see that there are vast interfield differences in terms of the number of publications. Moreover, we find that the interfield differences are augmented when we consider the quality of the publications. Lastly, we rank the departments according to the quality of their graduate programs. We see that there are also huge differences among the departments with graduate programs of comparable rank.
    Scientometrics 06/2015; DOI:10.1007/s11192-015-1621-7
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper analyses the interrelationship between perceived journal reputation and its relevance for academics’ work. Based on a survey of 705 members of the German Economic Association (GEA), we find a strong interrelationship between perceived journal reputation and relevance where a journal’s perceived relevance has a stronger effect on its reputation than vice versa. Moreover, past journal ratings conducted by the Handelsblatt and the GEA directly affect journals’ reputation among German economists and indirectly also their perceived relevance, but the effect on reputation is more than twice as large as the effect on perceived relevance. In general, citations have a non-linear impact on perceived journal reputation and relevance. While the number of landmark articles published in a journal (as measured by the so-called H-index) increases the journal’s reputation, an increase in the H-index even tends to decrease a journal’s perceived relevance, as long as this is not simultaneously reflected in a higher Handelsblatt and/or GEA rating. This suggests that a journal’s relevance is driven by average article quality, while reputation depends more on truly exceptional articles. We also identify significant differences in the views on journal relevance and reputation between different age groups.
    Scientometrics 06/2015; 103(3):849-877. DOI:10.1007/s11192-015-1542-5
  • [Show abstract] [Hide abstract]
    ABSTRACT: Social networks are said to have a positive impact on scientific development. Conventionally, it is argued that female and male researchers differ in access to and participation in networks and hence experience unequal career opportunities. Due to limited capacities of time and resources as well as homophily, top-level scientists may structure their contacts to reduce problems of complexity and uncertainty. The outcomes of the structuring can be cohesive subgroups within networks of relation. Women in science might suffer exclusion from cliques because of being dissimilar in the arena. The present paper aims to explore integration in and composition of scientific cliques. A three-step analysis is conducted: Firstly, cliques are identified. Secondly, overlap structures are examined. Thirdly, group compositions are analysed in terms of other personal attributes of the researchers involved. Building on network data of female and male investigators, the article applies a comparative case study design including two cutting edge research institutions from the German Excellence Initiative. The study contrasts a Cluster of Excellence with a Graduate School and the corresponding formal with the informal networks. The results imply that the general hypothesis of unfavourably embedded female researchers cannot be supported. Although women are less integrated in scientific cliques, the majority is involved in an inner social circle which enables access to career-relevant network resources.
    Scientometrics 06/2015; 103(3):897-922. DOI:10.1007/s11192-015-1572-z
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study draws on publication and citation data related to plant biotechnology from a 10-year (2004-2013) period to assess the research performance, impact, and collaboration of member states of the Association of Southeast Asian Nations (ASEAN). Plant biotechnology is one of the main areas of cooperation between ASEAN member states and among the research areas promoted to achieve regional food security and sustainable development. In general, findings indicate increased scientific output, influence, and overall collaboration of ASEAN countries in plant biotechnology over time. Research performance and collaboration (domestic, regional, and international) of the region in plant biotechnology are linked to the status of the economic development of each member country. Thailand produced the most publications of the ASEAN member states while Singapore had the highest influence as indicated by its citation activity in plant biotechnology among the ASEAN countries. Domestic and international collaborations on plant biotechnology are numerous. Regional collaboration or partnership among ASEAN countries was, however, was found to be very limited, which is a concern for the region’s goal of economic integration and science and technology cooperation. More studies using bibliometric data analysis need to be conducted to understand plant biotechnology cooperation and knowledge flows between ASEAN countries.
    Scientometrics 06/2015; 103(3):1043-1059. DOI:10.1007/s11192-015-1582-x