Search (599 results, page 1 of 30)

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Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.

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Abstract

Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However, as indicated by Rotolo et al. (Research Policy 44, 1827-1843, 2015), the most serious problems are the lack of an acknowledged definition of emergence and incomplete elaboration of the linkages between the definitions that are used and the indicators that are created. With these issues in mind, this study first adjusts the definition of an emerging technology that Rotolo et al. (2015) have proposed to accommodate the analysis. Next, a set of criteria for the identification of emerging topics is proposed according to the adjusted definition and attributes of emergence. Using two sets of parameter values, several emerging research topics are identified. Finally, evaluation tests are conducted by demonstration of the proposed approach and comparison with previous studies. The strength of the present methodology lies in the fact that it is fully transparent, straightforward, and flexible.

Type

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Abstract

This year marks 350 years since the inaugural publications of both the Journal des Sçavans and the Philosophical Transactions, first published in 1665 and considered the birth of the peer-reviewed journal article. This form of scholarly communication has not only remained the dominant model for disseminating new knowledge (particularly for science and medicine), but has also increased substantially in volume. Derek de Solla Price - the "father of scientometrics" (Merton and Garfield, 1986, p. vii) - was the first to document the exponential increase in scientific journals and showed that "scientists have always felt themselves to be awash in a sea of the scientific literature" (Price, 1963, p. 15), as, for example, expressed at the 1948 Royal Society's Scientific Information Conference: Not for the first time in history, but more acutely than ever before, there was a fear that scientists would be overwhelmed, that they would be no longer able to control the vast amounts of potentially relevant material that were pouring forth from the world's presses, that science itself was under threat (Bawden and Robinson, 2008, p. 183).
One of the solutions to help scientists filter the most relevant publications and, thus, to stay current on developments in their fields during the transition from "little science" to "big science", was the introduction of citation indexing as a Wellsian "World Brain" (Garfield, 1964) of scientific information: It is too much to expect a research worker to spend an inordinate amount of time searching for the bibliographic descendants of antecedent papers. It would not be excessive to demand that the thorough scholar check all papers that have cited or criticized such papers, if they could be located quickly. The citation index makes this check practicable (Garfield, 1955, p. 108). In retrospective, citation indexing can be perceived as a pre-social web version of crowdsourcing, as it is based on the concept that the community of citing authors outperforms indexers in highlighting cognitive links between papers, particularly on the level of specific ideas and concepts (Garfield, 1983). Over the last 50 years, citation analysis and more generally, bibliometric methods, have developed from information retrieval tools to research evaluation metrics, where they are presumed to make scientific funding more efficient and effective (Moed, 2006). However, the dominance of bibliometric indicators in research evaluation has also led to significant goal displacement (Merton, 1957) and the oversimplification of notions of "research productivity" and "scientific quality", creating adverse effects such as salami publishing, honorary authorships, citation cartels, and misuse of indicators (Binswanger, 2015; Cronin and Sugimoto, 2014; Frey and Osterloh, 2006; Haustein and Larivière, 2015; Weingart, 2005).
Furthermore, the rise of the web, and subsequently, the social web, has challenged the quasi-monopolistic status of the journal as the main form of scholarly communication and citation indices as the primary assessment mechanisms. Scientific communication is becoming more open, transparent, and diverse: publications are increasingly open access; manuscripts, presentations, code, and data are shared online; research ideas and results are discussed and criticized openly on blogs; and new peer review experiments, with open post publication assessment by anonymous or non-anonymous referees, are underway. The diversification of scholarly production and assessment, paired with the increasing speed of the communication process, leads to an increased information overload (Bawden and Robinson, 2008), demanding new filters. The concept of altmetrics, short for alternative (to citation) metrics, was created out of an attempt to provide a filter (Priem et al., 2010) and to steer against the oversimplification of the measurement of scientific success solely on the basis of number of journal articles published and citations received, by considering a wider range of research outputs and metrics (Piwowar, 2013). Although the term altmetrics was introduced in a tweet in 2010 (Priem, 2010), the idea of capturing traces - "polymorphous mentioning" (Cronin et al., 1998, p. 1320) - of scholars and their documents on the web to measure "impact" of science in a broader manner than citations was introduced years before, largely in the context of webometrics (Almind and Ingwersen, 1997; Thelwall et al., 2005):
There will soon be a critical mass of web-based digital objects and usage statistics on which to model scholars' communication behaviors - publishing, posting, blogging, scanning, reading, downloading, glossing, linking, citing, recommending, acknowledging - and with which to track their scholarly influence and impact, broadly conceived and broadly felt (Cronin, 2005, p. 196). A decade after Cronin's prediction and five years after the coining of altmetrics, the time seems ripe to reflect upon the role of social media in scholarly communication. This Special Issue does so by providing an overview of current research on the indicators and metrics grouped under the umbrella term of altmetrics, on their relationships with traditional indicators of scientific activity, and on the uses that are made of the various social media platforms - on which these indicators are based - by scientists of various disciplines.

Date

20. 1.2015 18:30:22

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Abstract

Purpose The introduction of "altmetrics" as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for measuring scientific impact. Mendeley readership has been identified as one of the most important altmetric sources, with several features that are similar to citations. The purpose of this paper is to perform an in-depth analysis of the differences and similarities between the distributions of Mendeley readership and citations across fields. Design/methodology/approach The authors analyze two issues by using in each case a common analytical framework for both metrics: the shape of the distributions of readership and citations, and the field normalization problem generated by differences in citation and readership practices across fields. In the first issue the authors use the characteristic scores and scales method, and in the second the measurement framework introduced in Crespo et al. (2013). Findings There are three main results. First, the citations and Mendeley readership distributions exhibit a strikingly similar degree of skewness in all fields. Second, the results on "exchange rates (ERs)" for Mendeley readership empirically supports the possibility of comparing readership counts across fields, as well as the field normalization of readership distributions using ERs as normalization factors. Third, field normalization using field mean readerships as normalization factors leads to comparably good results. Originality/value These findings open up challenging new questions, particularly regarding the possibility of obtaining conflicting results from field normalized citation and Mendeley readership indicators; this suggests the need for better determining the role of the two metrics in capturing scientific recognition.

Date

20. 1.2015 18:30:22

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Abstract

To this day the analysis of citations has been aimed mainly to the exploration of different ways to count them, such as the total count, the h-index or the s-index, in order to quantify a researcher's overall contribution and impact. In this work we show how the consideration of the structured metadata that accompany citations, such as the publication outlet in which they have appeared, can lead to a considerably more insightful understanding of the ways in which a researcher has impacted the work of others.

Source

Semantic keyword-based search on structured data sources: COST Action IC1302. Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8-9, 2016, Revised Selected Papers. Eds.: A. Calì, A. et al

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Abstract

Using Web of Science data, portfolio analysis in terms of journal coverage can be projected onto a base map for units of analysis such as countries, cities, universities, and firms. The units of analysis under study can be compared statistically across the 10,000+ journals. The interdisciplinarity of the portfolios is measured using Rao-Stirling diversity or Zhang et?al.'s improved measure 2D3. At the country level we find regional differentiation (e.g., Latin American or Asian countries), but also a major divide between advanced and less-developed countries. Israel and Israeli cities outperform other nations and cities in terms of diversity. Universities appear to be specifically related to firms when a number of these units are exploratively compared. The instrument is relatively simple and straightforward, and one can generalize the application to any document set retrieved from the Web of Science (WoS). Further instruction is provided online at http://www.leydesdorff.net/portfolio.

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Abstract

We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the underlying occurrence matrix. Neither the cosine nor the Pearson correlation should be used for the normalization of co-occurrence matrices because the similarity is then normalized twice, and therefore overestimated; the Ochiai coefficient can be used instead. Results are shown using a small matrix (5 cases, 4 variables) for didactic reasons, and also Ahlgren et?al.'s (2003) co-occurrence matrix of 24 authors in library and information sciences. The overestimation is shown numerically and will be illustrated using multidimensional scaling and cluster dendograms. If the occurrence matrix is not available (such as in internet research or author cocitation analysis) using Ochiai for the normalization is preferable to using the cosine.

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Abstract

In this paper we analyze a major part of the research output of the Networked Knowledge Organization Systems (NKOS) community in the period 2000 to 2015 from a network analytical perspective. We fo- cus on the paper output presented at the European NKOS workshops in the last 15 years. Our open dataset, the "NKOS bibliography", includes 14 workshop agendas (ECDL 2000-2010, TPDL 2011-2015) and 4 special issues on NKOS (2001, 2004, 2006 and 2015) which cover 171 papers with 218 distinct authors in total. A focus of the analysis is the visualization of co-authorship networks in this interdisciplinary eld. We used standard network analytic measures like degree and betweenness centrality to de- scribe the co-authorship distribution in our NKOS dataset. We can see in our dataset that 15% (with degree=0) of authors had no co-authorship with others and 53% of them had a maximum of 3 cooperations with other authors. 32% had at least 4 co-authors for all of their papers. The NKOS co-author network in the "NKOS bibliography" is a typical co- authorship network with one relatively large component, many smaller components and many isolated co-authorships or triples.

Source

Proceedings of the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) co-located with the 20th International Conference on Theory and Practice of Digital Libraries 2016 (TPDL 2016), Hannover, Germany, September 9, 2016. Edi. by Philipp Mayr et al. [http://ceur-ws.org/Vol-1676/=urn:nbn:de:0074-1676-5]

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Abstract

If you are an academic researcher but did not earn (yet) your Nobel prize or your retirement, it is unlikely you never heard about research metrics. These metrics aim at quantifying various aspects of the research process, at the level of individual researchers (e.g. h-index, altmetrics), scientific journals (e.g. impact factors) or entire universities/ countries (e.g. rankings). Although such "measurements" have existed in a simple form for a long time, their widespread calculation was enabled by the advent of the digital era (large amount of data available worldwide in a computer-compatible format). And in this new era, what becomes technically possible will be done, and what is done and appears to simplify our lives will be used. As a result, a rapidly growing number of statistics-based numerical indices are nowadays fed into decisionmaking processes. This is true in nearly all aspects of society (politics, economy, education and private life), and in particular in research, where metrics play an increasingly important role in determining positions, funding, awards, research programs, career choices, reputations, etc.

Content

Inhalt: Metrics in Research - For better or worse? / Jozica Dolenc, Philippe Hünenberger Oliver Renn - A brief visual history of research metrics / Oliver Renn, Jozica Dolenc, Joachim Schnabl - Bibliometry: The wizard of O's / Philippe Hünenberger - The grip of bibliometrics - A student perspective / Matthias Tinzl - Honesty and transparency to taxpayers is the long-term fundament for stable university funding / Wendelin J. Stark - Beyond metrics: Managing the performance of your work / Charlie Rapple - Scientific profiling instead of bibliometrics: Key performance indicators of the future / Rafael Ball - More knowledge, less numbers / Carl Philipp Rosenau - Do we really need BIBLIO-metrics to evaluate individual researchers? / Rüdiger Mutz - Using research metrics responsibly and effectively as a researcher / Peter I. Darroch, Lisa H. Colledge - Metrics in research: More (valuable) questions than answers / Urs Hugentobler - Publication of research results: Use and abuse / Wilfred F. van Gunsteren - Wanted: Transparent algorithms, interpretation skills, common sense / Eva E. Wille - Impact factors, the h-index, and citation hype - Metrics in research from the point of view of a journal editor / Renato Zenobi - Rashomon or metrics in a publisher's world / Gabriella Karger - The impact factor and I: A love-hate relationship / Jean-Christophe Leroux - Personal experiences bringing altmetrics to the academic market / Ben McLeish - Fatally attracted by numbers? / Oliver Renn - On computable numbers / Gerd Folkers, Laura Folkers - ScienceMatters - Single observation science publishing and linking observations to create an internet of science / Lawrence Rajendran.

Editor

Hünenberger, P. et al.

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Date

18. 3.2014 19:13:22

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a

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Abstract

A model based on a set of bibliometric indicators is proposed for the prediction of the ranking of applicants to an academic position as produced by a committee of peers. The results show that a very small number of indicators may lead to a robust prediction of about 75% of the cases. We start with 12 indicators to build a few composite indicators by factor analysis. Following a discrete choice model, we arrive at 3 comparatively good predicative models. We conclude that these models have a surprisingly good predictive power and may help peers in their selection process.

Date

18. 3.2014 18:22:21

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Abstract

Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, 2013). In this paper, we conceptualize P100 and propose an improvement which we call P100'. Advantages and disadvantages of citation-rank indicators are noted.

Date

22. 8.2014 17:05:18

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Date

20. 1.2019 11:22:31

Source

Knowledge organization for a sustainable world: challenges and perspectives for cultural, scientific, and technological sharing in a connected society : proceedings of the Fourteenth International ISKO Conference 27-29 September 2016, Rio de Janeiro, Brazil / organized by International Society for Knowledge Organization (ISKO), ISKO-Brazil, São Paulo State University ; edited by José Augusto Chaves Guimarães, Suellen Oliveira Milani, Vera Dodebei

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Abstract

This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures

Date

22. 1.2011 13:02:21

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a

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Abstract

This paper uncovers patterns of knowledge dissemination among scientific disciplines. Although the transfer of knowledge is largely unobservable, citations from one discipline to another have been proven to be an effective proxy to study disciplinary knowledge flow. This study constructs a knowledge-flow network in which a node represents a Journal Citation Reports subject category and a link denotes the citations from one subject category to another. Using the concept of shortest path, several quantitative measurements are proposed and applied to a knowledge-flow network. Based on an examination of subject categories in Journal Citation Reports, this study indicates that social science domains tend to be more self-contained, so it is more difficult for knowledge from other domains to flow into them; at the same time, knowledge from science domains, such as biomedicine-, chemistry-, and physics-related domains, can access and be accessed by other domains more easily. This study also shows that social science domains are more disunified than science domains, because three fifths of the knowledge paths from one social science domain to another require at least one science domain to serve as an intermediate. This work contributes to discussions on disciplinarity and interdisciplinarity by providing empirical analysis.

Date

26.10.2014 20:22:22

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a

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Abstract

This paper explores the possible citation chain reactions of a Nobel Prize using the mathematician Robert J. Aumann as a case example. The results show that the award of the Nobel Prize in 2005 affected not only the citations to his work, but also affected the citations to the references in his scientific oeuvre. The results indicate that the spillover effect is almost as powerful as the effect itself. We are consequently able to document a ripple effect in which the awarding of the Nobel Prize ignites a citation chain reaction to Aumann's scientific oeuvre and to the references in its nearest citation network. The effect is discussed using innovation decision process theory as a point of departure to identify the factors that created a bandwagon effect leading to the reported observations.

Date

22. 3.2013 16:21:09

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Abstract

I studied the factors (citations, self-citations, and number of articles) that influenced large changes in only 1 year in the impact factors (IFs) of journals. A set of 360 instances of journals with large increases or decreases in their IFs from a given year to the following was selected from journals in the Journal Citation Reports from 1998 to 2007 (40 journals each year). The main factor influencing large changes was the change in the number of citations. About 54% of the increases and 42% of the decreases in the journal IFs were associated with changes in the journal self-citations.

Date

22. 1.2011 12:53:00

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Abstract

Purpose - The purpose of this paper is to analyse the global scientific outputs of ontology research, an important emerging discipline that has huge potential to improve information understanding, organization, and management. Design/methodology/approach - This study collected literature published during 1900-2012 from the Web of Science database. The bibliometric analysis was performed from authorial, institutional, national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic distribution, co-word analysis, and a new index were applied to the selected data. Findings - Characteristics of publication outputs suggested that ontology research has entered into the soaring stage, along with increased participation and collaboration. The authors identified the leading authors, institutions, nations, and articles in ontology research. Authors were more from North America, Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of frequently used keywords were identified: applications in Semantic Web, applications in bioinformatics, philosophy theories, and common supporting technology. Semantic Web research played a core role, and gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to information science. Originality/value - This is the first study to quantify global research patterns and trends in ontology, which might provide a potential guide for the future research. The new index provides an alternative way to evaluate the multidisciplinary influence of researchers.

Date

20. 1.2015 18:30:22
17. 9.2018 18:22:23

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Abstract

Literature citation analysis plays a very important role in bibliometrics and scientometrics, such as the Science Citation Index (SCI) impact factor, h-index. Existing citation analysis methods assume that all citations in a paper are equally important, and they simply count the number of citations. Here we argue that the citations in a paper are not equally important and some citations are more important than the others. We use a strength value to assess the importance of each citation and propose to use the regression method with a few useful features for automatically estimating the strength value of each citation. Evaluation results on a manually labeled data set in the computer science field show that the estimated values can achieve good correlation with human-labeled values. We further apply the estimated citation strength values for evaluating paper influence and author influence, and the preliminary evaluation results demonstrate the usefulness of the citation strength values.

Date

22. 8.2014 17:12:35

Type

a

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Abstract

Purpose - The purpose of this paper is to attempt to provide a review of the growing literature on co-authorship networks and the research gaps that may be investigated for future studies in this field. Design/methodology/approach - The existing literature on co-authorship networks was identified, evaluated and interpreted. Narrative review style was followed. Findings - Co-authorship, a proxy of research collaboration, is a key mechanism that links different sets of talent to produce a research output. Co-authorship could also be seen from the perspective of social networks. An in-depth analysis of such knowledge networks provides an opportunity to investigate its structure. Patterns of these relationships could reveal, for example, the mechanism that shapes our scientific community. The study provides a review of the expanding literature on co-authorship networks. Originality/value - This is one of the first comprehensive reviews of network-based studies on co-authorship. The field is fast evolving, opening new gaps for potential research. The study identifies some of these gaps.

Date

20. 1.2015 18:30:22

Type

a