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Data sharing and reuse promise many benefits to science, but many researchers are reluctant to share and reuse data. Data papers, published as peer‐reviewed articles that provide descriptive information about specific datasets, are a potential solution as they may incentivize sharing by providing a mechanism for data producers to get citation credit and support reuse by providing contextual information about dataset production. Data papers can receive many citations. However, does citation of a data paper mean reuse of the underlying dataset? This paper presents preliminary findings from a content‐based citation analysis of data papers (n = 103) published in two specialized data journals, one in earth sciences and one in physical and chemical sciences. We conclude that while the genre of data papers facilitates some data sharing and reuse, they fail to live up to their full potential. Further, practices of reuse of datasets from data papers vary considerably between disciplines. We propose measures for academic publishers to enhance the data paper's role in scholarly communication to attract more attention from researchers and to inform discipline‐specific policy and practices related to data publication. [ABSTRACT FROM AUTHOR]

Peer review can evaluate the quality of academic articles involving the evaluation of some aspects, e.g., methodology, experiment, and aspects are usually the critical sections, substance and properties of the article concerned by reviewers. Previous research on content mining of peer review did not distinguish the round of reviews. Detecting differences peer review comments among different rounds can help us to understand the focus change of the reviewers in the different rounds. This paper takes the Nature Communications as an example to build the corpora of peer reviews in multiple rounds. We correlate review rounds and citations and extract aspects from the corpora to analyze the round characteristics of review comments. We find that there is no significant correlation between the review rounds and the citations frequency, but when the number of rounds is within 1–3, there is a weak positive correlation between the review rounds and citations. Additionally, the reviewers tend to pay more attention to the results analysis and the significance of the work firstly, and then they will focus on the details of the article, such as diagrams. This study can provide new ideas for peer review mining and the application of bibliometrics. [ABSTRACT FROM AUTHOR]

Based on publications in Chemistry in 2015, this paper investigated the citation impact on domestic and foreign scholarly communities of Chinese and US publications, and to what extent does such impact relate to international collaboration and government funding. First‐ or corresponding authorship in international collaboration is defined as dominance and is taken into consideration. Citations to publications are designated as domestic citations (Chinese or US citations), partner citations or other citations according to overlap of countries of citing and cited publications. Significant difference exists in the two countries. Most citations of Chinese publications are from domestic and partner countries, with domestic citations being the mainstay. In contrast, US papers receive more other citations than domestic or partner citations, and citations from partner countries contribute significantly. US papers have higher global impact than those of China. High domestic citation propensity is associated with non‐international collaboration, dominance of focal country in international collaboration, and national funding support. Such phenomenon is more pronounced in Chinese papers. In other words, citation impact of Chinese publications is more limited in domestic community, whereas that of the US is more global. [ABSTRACT FROM AUTHOR]

ABSTRACT There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we propose a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts. [ABSTRACT FROM AUTHOR]

It has always been argued that social sciences and humanities (SSH) impact evaluation calls for a broader range of indicators than only bibliometric indicators. This study analyzes the performance of scientific SSH publications using 1,327,924 records from the Social Sciences Citation Index (SSCI) and the Arts & Humanities Citation Index (A&HCI), and 629,586 records from Altmetric.com. The research covers a period of 5 years (2013–2017); three publication types (articles, reviews, and proceedings papers); four selected subject fields; three citation indicators; and 13 altmetric indicators. The comparative analysis of SSH performance is studied in both citation and altmetric pattern, and an attempt is made to interpret the results in both the metric and discipline contexts. Major contributions of this study include the extension of analysis methods for the SSH, as well as the validation of altmetric indicators. [ABSTRACT FROM AUTHOR]

In prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio‐based metrics to accompany the count‐based metrics described previously. We illustrate the mathematical properties of the ratio‐based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low‐scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis. [ABSTRACT FROM AUTHOR]

Semantic Web and Linked Data promise to change the way we identify, classify, share, find and reuse information by managing and interlinking data so that it can be understood by machines. The benefits identified by the World Wide Web Consortium and other Semantic Web advocates have lead information professionals to explore these possibilities by implementing several initiatives based on Semantic Web technologies in recent years. Just like information science, Semantic Web research is multidisciplinary in nature and can be applied in many different disciplines. In this article, we provide a portrait of research on the Semantic Web using bibliometric methods and investigate the specific contribution of information science literature on the topic. We analyzed 6,438 articles published from 2001 to 2016 retrieved from the Web of Science database to evaluate the evolution of the papers published by discipline and the way the topic was discussed by different discipline. A citation analysis is also conducted. [ABSTRACT FROM AUTHOR]

Citation content Analysis is very useful to reveal the nature of citations among scientific papers. However, most of previous studies had been done only from the side of citing papers and ignored the side of cited papers. We proposed a similarity‐based unsupervised citation text identification method and a ranking‐based supervised cited text identification method. Based on the identified citation and cited texts, we performed a fine‐grained citation analysis and found there was a difference in the cited texts between the citing papers in different disciplines. [ABSTRACT FROM AUTHOR]