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Register a new userNon-English language publications in Citation Indexes -- quantity and quality
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Added by
Mark Akoev
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Authors
Olga Moskaleva
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No Arabic abstract
We analyzed publications data in WoS and Scopus to compare publications in native languages vs publications in English and find any distinctive patterns. We analyzed their distribution by research areas, languages, type of access and citation patterns. The following trends were found: share of English publications increases over time; native-language publications are read and cited less than English-language outside the origin country; open access impact on views and citation is higher for native languages; journal ranking correlates with the share of English publications for multi-language journals. We conclude also that the role of non-English publications in research evaluation in non-English speaking countries is underestimated when research in social science and humanities is assessed only by publications in Web of Science and Scopus.
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The past year has seen movement on several fronts for improving software citation, including the Center for Open Sciences Transparency and Openness Promotion (TOP) Guidelines, the Software Publishing Special Interest Group that was started at Januarys AAS meeting in Seattle at the request of that organizations Working Group on Astronomical Software, a Sloan-sponsored meeting at GitHub in San Francisco to begin work on a cohesive research software citation-enabling platform, the work of Force11 to transform and improve research communication, and WSSSPEs ongoing efforts that include software publication, citation, credit, and sustainability. Brief reports on these efforts were shared at the BoF, after which participants discussed ideas for improving software citation, generating a list of recommendations to the community of software authors, journal publishers, ADS, and research authors. The discussion, recommendations, and feedback will help form recommendations for software citation to those publishers represented in the Software Publishing Special Interest Group and the broader community.
Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.
Empirical analysis results about the possible causes leading to non-citation may help increase the potential of researchers work to be cited and editorial staffs of journals to identify contributions with potential high quality. In this study, we conduct a survey on the possible causes leading to citation or non-citation based on a questionnaire. We then perform a statistical analysis to identify the major causes leading to non-citation in combination with the analysis on the data collected through the survey. Most respondents to our questionnaire identified eight major causes that facilitate easy citation of ones papers, such as research hotspots and novel topics of content, longer intervals after publication, research topics similar to my work, high quality of content, reasonable self-citation, highlighted title, prestigious authors, academic tastes and interests similar to mine.They also pointed out that the vast difference between their current and former research directions as the primary reason for their previously uncited papers. They feel that text that includes notes, comments, and letters to editors are rarely cited, and the same is true for too short or too lengthy papers. In comparison, it is easier for reviews, articles, or papers of intermediate length to be cited.
Citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation context and content analysis, citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.
We perform a statistical analysis of scientific-publication data with a goal to provide quantitative analysis of scientific process. Such an investigation belongs to the newly established field of scientometrics: a branch of the general science of science that covers all quantitative methods to analyze science and research process. As a case study we consider download and citation statistics of the journal `Europhysics Letters (EPL), as Europes flagship letters journal of broad interest to the physics community. While citations are usually considered as an indicator of academic impact, downloads reflect rather the level of attractiveness or popularity of a publication. We discuss peculiarities of both processes and correlations between them.
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