Search (44 results, page 2 of 3)

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Abstract

Jorge Hirsch recently proposed the h index to quantify the research output of individual scientists. The new index has attracted a lot of attention in the scientific community. The claim that the h index in a single number provides a good representation of the scientific lifetime achievement of a scientist as well as the (supposed) simple calculation of the h index using common literature databases lead to the danger of improper use of the index. We describe the advantages and disadvantages of the h index and summarize the studies on the convergent validity of this index. We also introduce corrections and complements as well as single-number alternatives to the h index.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.9, S.1381-1385

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Abstract

Using data compiled for the SCImago Institutions Ranking, we look at whether the subject area type an institution (university or research-focused institution) belongs to (in terms of the fields researched) has an influence on its ranking position. We used latent class analysis to categorize institutions based on their publications in certain subject areas. Even though this categorization does not relate directly to scientific performance, our results show that it exercises an important influence on the outcome of a performance measurement: Certain subject area types of institutions have an advantage in the ranking positions when compared with others. This advantage manifests itself not only when performance is measured with an indicator that is not field-normalized but also for indicators that are field-normalized.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2310-2316

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Abstract

Peer review is the backbone of modern science. F1000Prime is a postpublication peer review system of the biomedical literature (papers from medical and biological journals). This study is concerned with the interrater reliability and convergent validity of the peer recommendations formulated in the F1000Prime peer review system. The study is based on about 100,000 papers with recommendations from faculty members. Even if intersubjectivity plays a fundamental role in science, the analyses of the reliability of the F1000Prime peer review system show a rather low level of agreement between faculty members. This result is in agreement with most other studies that have been published on the journal peer review system. Logistic regression models are used to investigate the convergent validity of the F1000Prime peer review system. As the results show, the proportion of highly cited papers among those selected by the faculty members is significantly higher than expected. In addition, better recommendation scores are also associated with higher performing papers.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2415-2426

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Abstract

In this study, reference standards and reference multipliers are suggested as a means to compare the citation impact of earlier research publications in physics (from the period of "Little Science" in the early 20th century) with that of contemporary papers (from the period of "Big Science," beginning around 1960). For the development of time-specific reference standards, the authors determined (a) the mean citation rates of papers in selected physics journals as well as (b) the mean citation rates of all papers in physics published in 1900 (Little Science) and in 2000 (Big Science); this was accomplished by relying on the processes of field-specific standardization in bibliometry. For the sake of developing reference multipliers with which the citation impact of earlier papers can be adjusted to the citation impact of contemporary papers, they combined the reference standards calculated for 1900 and 2000 into their ratio. The use of reference multipliers is demonstrated by means of two examples involving the time adjusted h index values for Max Planck and Albert Einstein.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.10, S.2061-20690

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Abstract

In the humanities and social sciences, bibliometric methods for the assessment of research performance are (so far) less common. This study uses a concrete example in an attempt to evaluate a research institute from the area of social sciences and humanities with the help of data from Google Scholar (GS). In order to use GS for a bibliometric study, we developed procedures for the normalization of citation impact, building on the procedures of classical bibliometrics. In order to test the convergent validity of the normalized citation impact scores, we calculated normalized scores for a subset of the publications based on data from the Web of Science (WoS) and Scopus. Even if scores calculated with the help of GS and the WoS/Scopus are not identical for the different publication types (considered here), they are so similar that they result in the same assessment of the institute investigated in this study: For example, the institute's papers whose journals are covered in the WoS are cited at about an average rate (compared with the other papers in the journals).

Source

Journal of the Association for Information Science and Technology. 67(2016) no.11, S.2778-2789

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Abstract

Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique-segmented regression analysis-which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2215-2222

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Abstract

Citations measure an aspect of scientific quality: the impact of publications (A.F.J. van Raan, 1996). Percentiles normalize the impact of papers with respect to their publication year and field without using the arithmetic average. They are suitable for visualizing the performance of a single scientist. Beam plots make it possible to present the distributions of percentiles in the different publication years combined with the medians from these percentiles within each year and across all years.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.1, S.206-208

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Abstract

This Brief Communication discusses the benefits of citation analysis in research evaluation based on Galton's "Wisdom of Crowds" (1907). Citations are based on the assessment of many which is why they can be considered to have some credibility. However, we show that citations are incomplete assessments and that one cannot assume that a high number of citations correlates with a high level of usefulness. Only when one knows that a rarely cited paper has been widely read is it possible to say-strictly speaking-that it was obviously of little use for further research. Using a comparison with "like" data, we try to determine that cited reference analysis allows for a more meaningful analysis of bibliometric data than times-cited analysis.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1288-1292

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Abstract

The purpose of this paper is to propose an approach for identifying landmark papers in the long run. These publications reach a very high level of citation impact and are able to remain on this level across many citing years. In recent years, several studies have been published which deal with the citation history of publications and try to identify landmark publications. Design/methodology/approach In contrast to other studies published hitherto, this study is based on a broad data set with papers published between 1980 and 1990 for identifying the landmark papers. The authors analyzed the citation histories of about five million papers across 25 years. Findings The results of this study reveal that 1,013 papers (less than 0.02 percent) are "outstandingly cited" in the long run. The cluster analyses of the papers show that they received the high impact level very soon after publication and remained on this level over decades. Only a slight impact decline is visible over the years. Originality/value For practical reasons, approaches for identifying landmark papers should be as simple as possible. The approach proposed in this study is based on standard methods in bibliometrics.

Source

Journal of documentation. 74(2018) no.2, S.278-288

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Abstract

In a recently published PNAS paper, Radicchi, Fortunato, and Castellano (2008) propose the relative indicator cf as an unbiased indicator for citation performance across disciplines (fields, subject areas). To calculate cf, the citation rate for a single paper is divided by the average number of citations for all papers in the discipline in which the single paper has been categorized. cf values are said to lead to a universality of discipline-specific citation distributions. Using a comprehensive dataset of an evaluation study on Angewandte Chemie International Edition (AC-IE), we tested the advantage of using this indicator in practical application at the micro level, as compared with (1) simple citation rates, and (2) z-scores, which have been used in psychological testing for many years for normalization of test scores. To calculate z-scores, the mean number of citations of the papers within a discipline is subtracted from the citation rate of a single paper, and the difference is then divided by the citations' standard deviation for a discipline. Our results indicate that z-scores are better suited than cf values to produce universality of discipline-specific citation distributions.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.8, S.1664-1670

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Abstract

Purpose - The purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non-scientific, reasons. Design/methodology/approach - The review covers research published from the early 1960s up to mid-2005 (approximately 30 studies on citing behavior-reporting results in about 40 publications). Findings - The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non-scientific, factors that play a part in the decision to cite. However, the results of the studies must also be deemed scarcely reliable: the studies vary widely in design, and their results can hardly be replicated. Many of the studies have methodological weaknesses. Furthermore, there is evidence that the different motivations of citers are "not so different or 'randomly given' to such an extent that the phenomenon of citation would lose its role as a reliable measure of impact". Originality/value - Given the increasing importance of evaluative bibliometrics in the world of scholarship, the question "What do citation counts measure?" is a particularly relevant and topical issue.

Source

Journal of documentation. 64(2008) no.1, S.45-80

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Abstract

In recent years, a number of studies have introduced methods for identifying papers with delayed recognition (so called "sleeping beauties," SBs) or have presented single publications as cases of SBs. Most recently, Ke, Ferrara, Radicchi, and Flammini (2015, Proceedings of the National Academy of Sciences of the USA, 112(24), 7426-7431) proposed the so called "beauty coefficient" (denoted as B) to quantify how much a given paper can be considered as a paper with delayed recognition. In this study, the new term smart girl (SG) is suggested to differentiate instant credit or "flashes in the pan" from SBs. Although SG and SB are qualitatively defined, the dynamic citation angle ß is introduced in this study as a simple way for identifying SGs and SBs quantitatively - complementing the beauty coefficient B. The citation angles for all articles from 1980 (n?=?166,870) in natural sciences are calculated for identifying SGs and SBs and their extent. We reveal that about 3% of the articles are typical SGs and about 0.1% typical SBs. The potential advantages of the citation angle approach are explained.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.3, S.359-367

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Abstract

In November 2014, the Nature Index (NI) was introduced (see http://www.natureindex.com) by the Nature Publishing Group (NPG). The NI comprises the primary research articles published in the past 12 months in a selection of reputable journals. Starting from two short comments on the NI (Haunschild & Bornmann, 2015a, 2015b), we undertake an empirical analysis of the NI using comprehensive country data. We investigate whether the huge efforts of computing the NI are justified and whether the size-dependent NI indicators should be complemented by size-independent variants. The analysis uses data from the Max Planck Digital Library in-house database (which is based on Web of Science data) and from the NPG. In the first step of the analysis, we correlate the NI with other metrics that are simpler to generate than the NI. The resulting large correlation coefficients point out that the NI produces similar results as simpler solutions. In the second step of the analysis, relative and size-independent variants of the NI are generated that should be additionally presented by the NPG. The size-dependent NI indicators favor large countries (or institutions) and the top-performing small countries (or institutions) do not come into the picture.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.3, S.653-659

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Abstract

In a comprehensive research project, we investigated the predictive validity of selection decisions and reviewers' ratings at the open access journal Atmospheric Chemistry and Physics (ACP). ACP is a high-impact journal publishing papers on the Earth's atmosphere and the underlying chemical and physical processes. Scientific journals have to deal with the following question concerning the predictive validity: Are in fact the "best" scientific works selected from the manuscripts submitted? In this study we examined whether selecting the "best" manuscripts means selecting papers that after publication show top citation performance as compared to other papers in this research area. First, we appraised the citation impact of later published manuscripts based on the percentile citedness rank classes of the population distribution (scaling in a specific subfield). Second, we analyzed the association between the decisions (n = 677 accepted or rejected, but published elsewhere manuscripts) or ratings (reviewers' ratings for n = 315 manuscripts), respectively, and the citation impact classes of the manuscripts. The results confirm the predictive validity of the ACP peer review system.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.61-71

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Abstract

University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), in this study we offer thresholds with which it is possible to assess whether an institution belongs to the top 1%, top 5%, top 10%, top 25%, or top 50% of institutions in the world. The thresholds are based on the excellence rate or PPtop 10%. Both indicators measure the proportion of an institution's publications which belong to the 10% most frequently cited publications and are the most important indicators for measuring institutional impact. For example, while an institution must achieve a value of 24.63% in the Leiden Ranking 2013 to be considered one of the top 1% of institutions worldwide, the SCImago Institutions Ranking requires 30.2%.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.732-736

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Abstract

The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.5, S.509-525

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Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.428-429

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Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2715-2716

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Source

Journal of the Association for Information Science and Technology. 67(2016) no.4, S.1016-1017

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Abstract

Citation data for institutes are generally provided as numbers of citations or as relative citation rates (as, for example, in the Leiden Ranking). These numbers can then be compared between the institutes. This study aims to present a new approach for the evaluation of citation data at the institutional level, based on regression models. As example data, the study includes all articles and reviews from the Web of Science for the publication year 2003 (n?=?886,416 papers). The study is based on an in-house database of the Max Planck Society. The study investigates how much the expected number of citations for a publication changes if it contains the address of an institute. The calculation of the expected values allows, on the one hand, investigating how the citation impact of the papers of an institute appears in comparison with the total of all papers. On the other hand, the expected values for several institutes can be compared with one another or with a set of randomly selected publications. Besides the institutes, the regression models include factors which can be assumed to have a general influence on citation counts (e.g., the number of authors).

Source

Journal of the Association for Information Science and Technology. 67(2016) no.9, S.2274-2282