Authors:Mark Elkins, Christopher Maher, Robert Herbert, Anne Moseley, and Catherine Sherrington
To determine the degree of correlation among journal citation indices that reflect the average number of citations per article,
the most recent journal ratings were downloaded from the websites publishing four journal citation indices: the Institute
of Scientific Information’s journal impact factor index, Eigenfactor’s article influence index, SCImago’s journal rank index and Scopus’ trend line index. Correlations were determined for each pair of indices, using ratings from all journals that could be identified as
having been rated on both indices. Correlations between the six possible pairings of the four indices were tested with Spearman’s
rho. Within each of the six possible pairings, the prevalence of identifiable errors was examined in a random selection of
10 journals and among the 10 most discordantly ranked journals on the two indices. The number of journals that could be matched
within each pair of indices ranged from 1,857 to 6,508. Paired ratings for all journals showed strong to very strong correlations,
with Spearman’s rho values ranging from 0.61 to 0.89, all p < 0.001. Identifiable errors were more common among scores for journals that had very discordant ranks on a pair of indices.
These four journal citation indices were significantly correlated, providing evidence of convergent validity (i.e. they reflect
the same underlying construct of average citability per article in a journal). Discordance in the ranking of a journal on
two indices was in some cases due to an error in one index.
The investigators studied author research impact using the number of citers per publication an author’s research has been
able to attract, as opposed to the more traditional measure of citations. A focus on citers provides a complementary measure
of an author’s reach or influence in a field, whereas citations, although possibly numerous, may not reflect this reach, particularly
if many citations are received from a small number of citers. In this exploratory study, Web of Science was used to tally
citer and citation-based counts for 25 highly cited researchers in information studies in the United States and 26 highly
cited researchers from the United Kingdom. Outcomes of the tallies based on several measures, including an introduced ch-index,
were used to determine whether differences arise in author rankings when using citer-based versus citation-based counts. The
findings indicate a strong correlation between some citation and citer-based measures, but not with others. The findings of
the study have implications for the way authors’ research impact may be assessed.
The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received.
The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations
accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications
within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that
it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features
using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and
their statistical analysis provides greater insight into citation behavior.
In order to measure the degree to which Google Scholar can compete with bibliographical databases, search results from this
database is compared with Thomson’s ISI WoS (Institute for Scientific Information, Web of Science). For earth science literature
85% of documents indexed by ISI WoS were recalled by Google Scholar. The rank of records displayed in Google Scholar and ISI
WoS, is compared by means of Spearman’s footrule. For impact measures the h-index is investigated. Similarities in measures were significant for the two sources.
Witnessing a substantial growth rate in its scientific production, Iran is considered as one of the recently rising stars
in scientific contribution scene. However, its impact in science progress is widely unknown, especially at global level. Studying
Iran’s scholarly publications and recognition in SCI, the present communication tries to clarify the country’s science system
performance using regression analyses and then to compare its performance to that of the world, using Relative Citation Rate
(RCR) and Relative Subfield Citedness (RW). The results of the regression analyses reveal that although Iran displays considerable
weaknesses in its performance, it is increasingly recognized as its outputs grow. According to the RCR values, Iran performed
at/above the global level in 21 subfields. However, the RW values show that the country’s performance is above the global
level in only two subfields. Although Iran is very far from an ideal situation; these evidences can be considered as heralds
of a successful movement towards a wealthy scientific future.
Hirsch’s concept of h-index was used to define a similarity measure for journals. The h-similarity is easy to calculate from
the publicly available data of the Journal Citation Reports, and allows for plausible interpretation. On the basis of h-similarity,
a relative eminence indicator of journals was determined: the ratio of the JCR impact factor to the weighted average of that
of similar journals. This standardization allows journals from disciplines with lower average citation level (mathematics,
engineering, etc.) to get into the top lists.
Authors:Pablo Dorta-González and María-Isabel Dorta-González
The citation distribution of a researcher shows the impact of their production and determines the success of their scientific career. However, its application in scientific evaluation is difficult due to the bi-dimensional character of the distribution. Some bibliometric indexes that try to synthesize in a numerical value the principal characteristics of this distribution have been proposed recently. In contrast with other bibliometric measures, the biases that the distribution tails provoke, are reduced by the h-index. However, some limitations in the discrimination among researchers with different publication habits are presented in this index. This index penalizes selective researchers, distinguished by the large number of citations received, as compared to large producers. In this work, two original sets of indexes, the central area indexes and the central interval indexes, that complement the h-index to include the central shape of the citation distribution, are proposed and compared.
This paper reports on a bibliometric study of the characteristics and impact of research in the library and information science
(LIS) field which was funded through research grant programs, and compares it with research that received no extra funding.
Seven core LIS journals were examined to identify articles published in 1998 that acknowledge research grant funding. The
distribution of these articles by various criteria (e.g., topic, affiliation, funding agency) was determined. Their impact
as indicated by citation counts during 1998–2008 was evaluated against that of articles without acknowledging extra funding
and published in the same journals in the same year using citation data collected from Scopus’ Citation Tracker. The impact
of grant-funded research as measured by citation counts was substantially higher than that of other research, both overall
and in each journal individually. Scholars from outside LIS core institutions contributed heavily to grant-funded research.
The two highest-impact publications by far reported non-grant-based research, and grant-based funding of research reported
in core LIS journals was biased towards the information retrieval (IR) area, particularly towards research on IR systems.
The percentage of articles reporting grant-funded research was substantially higher in information-oriented journals than
in library-focused ones.
Citationanalysis as a mature quantitative research method in bibliometrics and scientometrics has been applied to many disciplines at home and abroad, especially in describing evolution of disciplines, evaluating
Budapest, Leiden, Leuven, Beijing, Shanghai, etc.) or as independent commercial enterprises (e.g., Science-Metrix in Montreal). Two major companies (Thomson Reuters and Elsevier) are also active in this market. In other words, citationanalysis has become