Search (180 results, page 1 of 9)

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Date

17.11.2020 12:22:59

Theme

Citation indexing

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Abstract

Altmetrics are web-based quantitative impact or attention indicators for academic articles that have been proposed to supplement citation counts. This article reports the first assessment of the extent to which mature altmetrics from Altmetric.com and Mendeley associate with individual article quality scores. It exploits expert norm-referenced peer review scores from the UK Research Excellence Framework 2021 for 67,030+ journal articles in all fields 2014-2017/2018, split into 34 broadly field-based Units of Assessment (UoAs). Altmetrics correlated more strongly with research quality than previously found, although less strongly than raw and field normalized Scopus citation counts. Surprisingly, field normalizing citation counts can reduce their strength as a quality indicator for articles in a single field. For most UoAs, Mendeley reader counts are the best altmetric (e.g., three Spearman correlations with quality scores above 0.5), tweet counts are also a moderate strength indicator in eight UoAs (Spearman correlations with quality scores above 0.3), ahead of news (eight correlations above 0.3, but generally weaker), blogs (five correlations above 0.3), and Facebook (three correlations above 0.3) citations, at least in the United Kingdom. In general, altmetrics are the strongest indicators of research quality in the health and physical sciences and weakest in the arts and humanities.

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Abstract

A citation index is a bibliographic database that provides citation links between documents. The first modern citation index was suggested by the researcher Eugene Garfield in 1955 and created by him in 1964, and it represents an important innovation to knowledge organization and information retrieval. This article describes citation indexes in general, considering the modern citation indexes, including Web of Science, Scopus, Google Scholar, Microsoft Academic, Crossref, Dimensions and some special citation indexes and predecessors to the modern citation index like Shepard's Citations. We present comparative studies of the major ones and survey theoretical problems related to the role of citation indexes as subject access points (SAP), recognizing the implications to knowledge organization and information retrieval. Finally, studies on citation behavior are presented and the influence of citation indexes on knowledge organization, information retrieval and the scientific information ecosystem is recognized.

Object

Science Citation Index

Theme

Citation indexing

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Abstract

Ensuring Wikipedia cites scholarly publications based on quality and relevancy without biases is critical to credible and fair knowledge dissemination. We investigate gender- and country-based biases in Wikipedia citation practices using linked data from the Web of Science and a Wikipedia citation dataset. Using coarsened exact matching, we show that publications by women are cited less by Wikipedia than expected, and publications by women are less likely to be cited than those by men. Scholarly publications by authors affiliated with non-Anglosphere countries are also disadvantaged in getting cited by Wikipedia, compared with those by authors affiliated with Anglosphere countries. The level of gender- or country-based inequalities varies by research field, and the gender-country intersectional bias is prominent in math-intensive STEM fields. To ensure the credibility and equality of knowledge presentation, Wikipedia should consider strategies and guidelines to cite scholarly publications independent of the gender and country of authors.

Date

22. 1.2023 18:53:32

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Abstract

This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

Theme

Citation indexing

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Abstract

This article discusses how letters to the editor boost publishing metrics for journals and authors, and then examines letters published since 2015 in six elite journals, including the Journal of the Association for Information Science and Technology. The initial findings identify some potentially anomalous use of letters and unusual self-citation patterns. The article proposes that Clarivate Analytics consider slightly reconfiguring the Journal Impact Factor to more fairly account for letters and that journals transparently explain their letter submission policies.

Date

6. 4.2022 19:22:26

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Abstract

Der Data Citation Index (DCI) stellt eine durchsuchbare Sammlung bibliografischer Metadaten zu Forschungsdaten in Datensätzen und Datenstudien ausgewählter Repositorien dar. Der DCI deckt alle wissenschaftlichen Disziplinen ab.

Object

Data Citation Index

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Abstract

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Date

22. 1.2023 18:37:33

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Abstract

Collaboration is encouraged because it is believed to improve academic research, supported by indirect evidence in the form of more coauthored articles being more cited. Nevertheless, this might not reflect quality but increased self-citations or the "audience effect": citations from increased awareness through multiple author networks. We address this with the first science wide investigation into whether author numbers associate with journal article quality, using expert peer quality judgments for 122,331 articles from the 2014-20 UK national assessment. Spearman correlations between author numbers and quality scores show moderately strong positive associations (0.2-0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering and social sciences, with weak negative/positive or no associations in various arts and humanities, and a possible negative association for decision sciences. This gives the first systematic evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other nonquality factors account for the higher citation rates of coauthored articles in some fields.

Date

22. 6.2023 18:11:50

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Abstract

Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high-order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high-order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties-such as depth, width, structural virality, and size-which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans-domain (or indirectly related domain) works than to receive only intradomain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history.

Theme

Citation indexing

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Abstract

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation-based scores, for instance, the h-index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.

Date

6. 1.2020 18:10:22

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Abstract

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important.

Theme

Citation indexing

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Abstract

The quality of scientific publications can be measured by quantitative indices such as the h-index, Source Normalized Impact per Paper, or g-index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full-text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning-based architecture for citation context classification. Unlike feature-based state-of-the-art models, our proposed focal-loss and class-weight-aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state-of-the-art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.

Theme

Citation indexing

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Abstract

The VOSviewer software provides easy access to bibliometric mapping using data from Dimensions, Scopus and Web of Science. The properly formatted and structured citation data, and the ease in which it can be exported open up new avenues for use during citation searches and eference interviews. This paper details specific techniques for using advanced searches in Dimensions, exporting the citation data, and drawing insights from the maps produced in VOS Viewer. These search techniques and data export practices are fast and accurate enough to build into reference interviews for graduate students, faculty, and post-PhD researchers. The search results derived from them are accurate and allow a more comprehensive view of citation networks embedded in ordinary complex boolean searches.

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Abstract

Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.

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Abstract

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

Theme

Citation indexing

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Abstract

Empirical evidence demonstrates that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution. Such asymmetry has sparked significant debate regarding the use of citations for research evaluation. However, a consensus has yet to be established concerning the historical trends in citation concentration. Are citations becoming more concentrated in a small number of articles? Or have recent geopolitical and technical changes in science led to more decentralized distributions? This ongoing debate stems from a lack of technical clarity in measuring inequality. Given the variations in citation practices across disciplines and over time, it is crucial to account for multiple factors that can influence the findings. This article explores how reference-based and citation-based approaches, uncited articles, citation inflation, the expansion of bibliometric databases, disciplinary differences, and self-citations affect the evolution of citation concentration. Our results indicate a decreasing trend in citation concentration, primarily driven by a decline in uncited articles, which, in turn, can be attributed to the growing significance of Asia and Europe. On the whole, our findings clarify current debates on citation concentration and show that, contrary to a widely-held belief, citations are increasingly scattered.

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Abstract

MEDLINE is the National Library of Medicine's (NLM) journal citation database. It contains over 28 million references to biomedical and life science journal articles, and a key feature of the database is that all articles are indexed with NLM Medical Subject Headings (MeSH). The library employs a team of MeSH indexers, and in recent years they have been asked to index close to 1 million articles per year in order to keep MEDLINE up to date. An important part of the MEDLINE indexing process is the assignment of articles to indexers. High quality and timely indexing is only possible when articles are assigned to indexers with suitable expertise. This article introduces the NLM indexer assignment dataset: a large dataset of 4.2 million indexer article assignments for articles indexed between 2011 and 2019. The dataset is shown to be a valuable testbed for expert matching and assignment algorithms, and indexer article assignment is also found to be useful domain-adaptive pre-training for the closely related task of reviewer assignment.

Date

22. 1.2023 18:49:49

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Abstract

Efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur. Citations are relevant as potential evidence in appointment and promotion decisions, but it is unclear whether there have been historical gender differences in average citation impact that might explain the current shortfall of senior female academics. This study investigates the evolution of gender differences in citation impact 1996-2018 for six million articles from seven large English-speaking nations: Australia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA. The results show that a small female citation advantage has been the norm over time for all these countries except the USA, where there has been no practical difference. The female citation advantage is largest, and statistically significant in most years, for Australia and the UK. This suggests that any academic bias against citing female-authored research cannot explain current employment inequalities. Nevertheless, comparisons using recent citation data, or avoiding it altogether, during appointments or promotion may disadvantage females in some countries by underestimating the likely greater impact of their work, especially in the long term.

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Abstract

Research collaboration is promoted by governments and research funders, but if the relative prevalence and merits of collaboration vary internationally then different national and disciplinary strategies may be needed to promote it. This study compares the team size and field normalized citation impact of research across all 27 Scopus broad fields in the 10 countries with the most journal articles indexed in Scopus 2008-2012. The results show that team size varies substantially by discipline and country, with Japan (4.2) having two-thirds more authors per article than the United Kingdom (2.5). Solo authorship is rare in China (4%) but common in the United Kingdom (27%). While increasing team size associates with higher citation impact in almost all countries and fields, this association is much weaker in China than elsewhere. There are also field differences in the association between citation impact and collaboration. For example, larger team sizes in the Business, Management & Accounting category do not seem to associate with greater research impact, and for China and India, solo authorship associates with higher citation impact in this field. Overall, there are substantial international and field differences in the extent to which researchers collaborate and the extent to which collaboration associates with higher citation impact.