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Data papers, as one of the channels to encourage researchers to open up research data under the open science movement, are expected to provide strong incentives through formal citations. However, few studies have investigated the drivers of this emerging type of publication. This study examines researchers' motivations, and considerations for data paper submission, as well as their perspectives on this scholarly publication. Through an in‐depth interview approach with ten data paper authors, our preliminary results found that, researchers are often driven by extrinsic factors to increase their publications, and data papers are sometimes viewed as territory claims before further research. Although the academic community widely recognizes the benefits of publishing data papers, some still cast a doubtful eye on its academic value and impact. We anticipate such insights on the driving forces and point of views of data papers could provide opportunities for stakeholders to fill gaps and strengthen the open science ecosystem. [ABSTRACT FROM AUTHOR]

Citations between papers and patents reflect transfer of knowledge between science and technology. Patents commonly cite papers but papers rarely cite patents. Here, we identified 6,033 paper‐to‐patent citations in a collection of 1.5 million PubMed Central open access articles. These citing papers and cited patents contained 132,536 paper‐to‐paper, 200,339 patent‐to‐patent, and 36,342 patent‐to‐paper citations. These four citation datasets were used to model the temporal patterns of knowledge transfer within and across patents and papers. We found that the cited patents are generally much older than the cited papers, regardless of whether they are cited by papers or patents. Discipline, affiliation type, and self‐citation also affect the age of the cited papers and patents. The recency of the citations partly explains the asymmetry in citations between papers and patents. [ABSTRACT FROM AUTHOR]

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]

To reveal whether there exists a correlation between the number of references and the number of citations in a paper of the Artificial Intelligence‐related conference, we present an experiment based on a total number of 9,458 papers published in CVPR, ICML, and AAAI from the year 2014 to 2018. Spearman's rank correlation tests show that the number of references and that of the citations are correlated with a correlation coefficient of 0.435, 0.464, and 0.180 respectively, in each p <.01. This means an increasing number of references in a paper may be contributing to the increase in citations. However, citing more references does not guarantee that a paper will be a highly cited paper. We hope this finding can provide a basis for the prediction of the citation impact of the Artificial Intelligence conference papers in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the effect of diffusing scientific articles on Twitter on their citation impact. For a set of 1.3 million papers covered by the Web of Science and published in 2012, normalized citation rates are compared between tweeted and non-tweeted articles published in the same journal. The results indicate that tweeted papers, published in the same year and in the same journal received around 30% higher citation rates than papers which were not diffused on Twitter. This type of citation advantage appeared across disciplines and countries of authors, but varied in extent. [ABSTRACT FROM AUTHOR]

The present study investigates the disciplinarity of papers describing scientific software (i.e., "software papers") as the first step to gain a deeper understanding of how science is operated in its material and practice bases. The preliminary analysis mapped 768 software papers in the Journal of Statistical Software and about 57,000 papers that cited them to the five knowledge domains in the CWTS classification system and traced how these papers are cited within and across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant prior work and causes researchers to spend a considerable amount of time building up their literature list. Citations play a significant role in discovering relevant literature. However, not all citations are created equal. A majority of the citations that a paper receives are for providing contextual, and background information to the citing papers and are not central to the theme of those papers. However, some papers are pivotal to the citing paper and inspire or stem up the research in the citing paper. Hence the nature of citation the former receives from the latter is significant. In this work in progress paper, we discuss our preliminary idea towards establishing a lineage for a given research via identifying significant citations. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for at least a certain category of papers. The distal goal of this work is to identify the real impact of research work or a facility beyond direct citation counts. [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]

The data paper is a new type of scientific publication aiming to describe data objects per se and bridge the gaps between scientific writing and data‐driven scientific practice. Despite its importance, we are still largely in the dark as to the narrative structure of this emerging genre of scientific writing. In this poster, we presented and discussed some preliminary empirical evidence of the narrative structure of data papers and the distribution of cited sources in relation to this structure, based on a manual examination of 40 data papers from the journal of Scientific Data. Our findings suggest that even though data papers have a different structure from research papers, their paper sections bear distinct narrative functions as illustrated by the pattern of citation distribution. Moreover, some of these sections have similar functions to their counterparts in research papers. The implications of our initial findings and the next step of our project were explained in conclusion. [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]

ABSTRACT Citation is an important measurement in science which provides valuable clues to the historical development and trend forecast of science. However, the evolution of citation structure remains poorly understood despite long-period and frequent use of citation counts for assessment purposes within scientific community. We observe citation structure from a cascade perspective and construct a large and long-range citation graph based on real data. Two metrics are used to quantify the structural virality and cascade size. Preliminary results show that (1) age plays an important role for the evolution of citation cascades; (2) average depth tends to be influenced by both lifespan and the whole volume of scientific literature and grows much slower than cascade size; (3) there exists a stable positive relationship between average depth and cascade size, and it seems that an ultimate status exists where most papers eventually have large values for both metrics. [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]

Researchers in academia and industry face a deluge of data in our digital world. In this paper, we investigate a novel problem, query-centric scientific topic evolution. Using heterogeneous graph mining techniques we construct a topic evolution tree (TET) from massive collections of scientific publications, enabling students and researchers to explore the foundation of research topics outside their specialization. Prior research has focused mainly on citation relationships; in this study we employed multiple types of relationships, including authorship, citation, publishing venue, and the contributions authors, papers, and venues have made to a specific topic. We examine multiple restricted meta-paths in constructing a TET covering topics from the MeSH vocabulary for biomedical research. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a 'rough co-citation', which is a measure of relationship to expand co-citation networks so as to include new relevant documents. A rough co-citation relationship is a linkage between a pair of documents which are cited by two other documents in a similar citation context. The linkage strength of a rough co-citation relationship may be weaker than the original co-citation relationship, because a rough co-citation relationship is determined by citations in two separate documents. Rough co-citation linkages, however, may yield new relevant documents that are not identified by the original co-citation linkages. For example, the rough co-citation can identify relevant documents that are published after the citing document of the original co-citation becomes public. This study conducted IR experiments to evaluate the search performances of retrieval methods using the co-citation networks expanded by the rough co-citation relationships. Specifically, the random walk with restart, which is one of the latest graph search algorithms, is applied to the expanded and original co-citation networks. Scores of the normalized discounted cumulative gain (nDCG@K) are then compared. The results indicate that the search performance of the method using the expanded network outperforms a baseline method using the original network. [ABSTRACT FROM AUTHOR]

Treating all citations in a reference list equally is one of the common practices of citation analysis that have long been criticized. The increasing availability of digital full-text documents now makes it feasible to weigh citations in various ways, e.g., by their frequency or location in the text. In this paper we report preliminary results of a comparison of author citation weighing schemes based on in-text citation frequency, and the citation impact patterns that emerge from them in the field of library and information science. [ABSTRACT FROM AUTHOR]

Publications represent the most tangible products of research and scholarly communication. Growing pressures placed on researchers to increase their publication counts may encourage them to publish shorter papers, so that more publications may be generated from the same project and program. Is this the case? Furthermore, do shorter papers garner the same impact as longer papers as measured by the citations received? This research examines the relationship between article length and citations received by articles published in three prominent top biomedical research journals over the lifetime of the journals. The study reveals that instead of getting shorter, articles in these journals are getting longer over time. Longer articles in these journals, on average, attract more citations, and are therefore considered more impactful. [ABSTRACT FROM AUTHOR]

This ongoing study employs citation context analysis to examine the citation functions in social sciences and humanities (SS&H) disciplines. The analyses focus on the uses of organic/perfunctory citations and confirmative/ negational citations. In this poster, we will present the preliminary findings from the completed analysis on Chinese history research papers. Chinese history authors used a large amount of perfunctory citations. The percentage of negational citations was also high, and many papers have negated a previous research. It has seriously challenged the citation analysis assumptions that citations are of equal contribution and that citation means author endorsement of previous literatures. We will continue to complete the analyses for the other chosen SS&H disciplines, and we encourage other researchers to extend the analysis to other subject areas and in other countries or languages so as to better understand researchers' citation behavior and to better assess the applicability of citation analysis in different domains. [ABSTRACT FROM AUTHOR]

This study is to investigate how citing behavior for a scientific article changes over time. A highly cited article is chosen to collect citation content from its 902 citing articles. Natural language processing and content analysis are adopted to encode citation content and complete the statistics of citation behavior. Citation content analysis indexes including citation mention, length, and location, are used to describe the changes of citation behavior over time. The experimental results show that there is correlation between citing time with most of the indexes and that citation mention and citation length are declining while the number of the citation co-occurrences is increasing. [ABSTRACT FROM AUTHOR]

This study analyzes the relation between author affiliation reputation and uncitedness. We use 2015 U.S. News Best Global University Subject rankings as representative of affiliation reputation and collect uncited papers from 24 journals in six subjects from WoS. Preliminary correlation analysis results indicate that: (1) there is a significant correlation between affiliation reputation and uncitedness. (2) their relation is negatively and weakly correlated. And secondary data collection and correlation analysis proved that relation between affiliation reputation and uncitedness vary in different subjects. The findings of this study would attract scientists' attention to avoid Matthew Effect and focus more on research papers rather than reputation when citing references. [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]

This bibliometric pilot study presents a modified assessment taxonomy devised originally by Leeder, Markey, and Yakel (2012) and demonstrates how to apply it to assess the resources used in the construction of student academic research papers. Besides discussing this assessment tool, we present empirical data of its application, the result of a pilot study conducted at a medium sized Midwestern American university, based on the analysis of a sample of papers written for a freshman level English composition class. Our findings, among many, demonstrate that just one in 700 academic resources cited in these papers comes from a resource like Wikipedia, and that 52% of resources cited in student papers fall into the five taxonomic category combinations. Besides being used for assessment, this taxonomy may reveal models of information usage in future research. [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]

ABSTRACT A major challenge to formal scientific communication is the retraction of published works. This study includes a detailed analysis of retracted articles in biomedical literature, including categorization of the reasons for retraction. The examination covers the years 2010 through 2014. Analysis also includes citations to articles retracted between 2001 and 2005. The totality of the investigation is couched within the context of communication in the biomedical sciences and, to a lesser extent, of the formulation of theories of citation. [ABSTRACT FROM AUTHOR]

This paper explores how the patterns of collaboration change throughout a scientist's career, especially their collaboration with so‐called "giants"—well‐known elite scholars with very high scientific impact. We divide scientist's careers into five stages based on the number of years since obtaining their PhD, and for each stage, we calculate a number of indicators, such as publication count, normalized citation count, count of collaborations, count of collaborations with "giants," and percentage of uninterrupted and continuous presence. Our analysis shows that although collaborating with "giants" may benefit a young scholar, they are more likely to achieve greater scientific success if they first work independently and only collaborate with "giants" later in their career. Meanwhile, high‐impact authors tend to work with "giants" early in their career, but not in the later stages, which forces us to reconsider the established notions of when is best to collaborate with elite scholars. Moreover, we find that high‐impact authors tend to have fewer co‐authors per article, regardless of the stage of their careers, indicating that collaboration is important, but the size of collaborations also matters. [ABSTRACT FROM AUTHOR]

Taking international LIS journals as an example, combining 8 traditional citation indicators and 10 altmetrics indicators, we use the method of multi‐indicator fusion to construct a comprehensive evaluation model of journal impact based on traditional citation indicators and altmetrics indicators. Through normalization processing, correlation analysis, reliability analysis, validity analysis, factor analysis, the paper evaluates the academic impact (FA), societal impact (FS) of LIS journals respectively. We get the following conclusion: (1) We get the 2‐dimensional evaluation results and according to LIS journals impacts, we divide the roles into four categories: "Prestige journals", "Star journals", "Common journals" and "Expert journals". (2) It is found that 8 traditional citation indicators based on JCR, present strong positive correlations, and the indicators are highly consistent overall and internally. (3) There are moderate or high positive correlations among 10 altmetrics based on Altmetrics Explorer, and there are also significant consistency among the indicators as a whole and internally. (4) The correlation coefficient between FA and FS is 0.566, with a moderate positive correlation. It indicates that the evaluation of LIS journals' societal impact based on altmetrics indicators has a useful supplement to the evaluation of academic impact based on citation. [ABSTRACT FROM AUTHOR]

Although open access (OA) to research outputs has been proven to improve research readership, citation, and impact, the uptake of OA in some disciplines has remained low. In this paper, we investigated and compared OA publishing practices of early career and mid‐career researchers in the Humanities, Arts, and Social Sciences (HASS) disciplines in Canada. The descriptive survey design with the use of online questionnaire was employed. Participants were drawn from a group of 15 public research universities via their openly available emails on university websites. Survey data was analyzed with descriptive and inferential statistics. Findings show that in the last three years, 74.1% of mid‐career researchers have published in OA journals, compared to 63.1% of early career researchers. However, OA publishing of monographs (21.3%) and conference proceedings (29.9%), as well as the frequency and extent OA publishing remains low among all participants. ANOVA results (F [2, 218] = 3.683, p =.027, 휂2 =.033) showed that 3.3% of the variance in researchers' OA publishing frequency can be attributed to their disciplines. Overall, OA publishing among researchers in the HASS disciplines is still low. Hence, there is a need to identify factors that facilitate or hinder HASS researchers' OA publishing. [ABSTRACT FROM AUTHOR]

Interdisciplinary studies require integration of principles, theories and practices from multiple disciplines. What type of domain knowledge will be applied more in such researches? Since scientific paradigms differ in fields, this paper takes computer science (CS) as a case and investigates utilization patterns of CS knowledge in articles from PLOS One with high interdisciplinary degree (IDD) by citation content analysis. Since scholars usually organize and indicate used knowledge in methodology, we study articles which cite CS articles in this section. After extracting citation content from citing articles, we identify the cited knowledge and analyse the type of cited knowledge. Experimental results show that: In the PLOS One articles which cited the CS knowledge, algorithm is the most used knowledge type and the most used object is random forests. Our study can be applied to understand how articles with high IDD use the knowledge from other disciplines in the future. [ABSTRACT FROM AUTHOR]

We investigate the diffusion characteristics of two groups of scientific works with different levels of novelty to see how scientific novelty interacts with citation patterns. Preliminary results show that: (1) novel ideas have long‐lasting impact, but the recognition process lags behind; (2) most diffusion metrics can't differentiate the two groups under a similar macro environment; (3) citation take‐off, average clustering coefficient, and connectivity can distinguish works with different levels of novelty. The latter two characteristics—average clustering coefficient and connectivity of citing literature networks—show particular potential for identifying innovative scientific works at an early stage. [ABSTRACT FROM AUTHOR]

ABSTRACT In this poster, we describe partial findings from a larger study that explores the influences of three different citation bases (reference entries, in-text citations, and essential citations) on the results of citation analysis in social sciences and humanities. Various citation characteristics such as document type, document language, self-citation rate, cited half life, and subject distributions of cited works will systematically compared to understand whether the selection of citation base did have an influence on the results. The completed analyses of the sociology discipline will be presented in this poster. [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]

Automated citation sentiment analysis is a newly emerged research topic inspired by traditional citation context analysis in scientometrics and applied linguistics. The main goals of current citation sentiment analysis are to develop new tools to model scientific literature and provide authoring support for researchers in tasks like literature review. In terms of developing authoring support tools, however, current studies have not taken into consideration the behavioral patterns of researchers' literature review practice, leaving user need assessment as a missing piece in current interdisciplinary research effort. This paper analyzed biomedical researchers' need by reviewing their publications on using manual citation sentiment analysis for detecting citation bias, and discussed the differences between biomedical researchers' approach and current automated citation sentiment analysis model. These differences are expected to inform the modeling of automated citation sentiment 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]

Patents contain reference citations. However, unlike journal papers, patents may become invalid if they are not maintained or have expired. This study determines whether invalid patents are still cited. We collected telecommunications patents as a sample to compare the citations of valid and invalid patents. The results indicated that approximately 50% of cited patents were invalid. Furthermore, the citations per patent of invalid patents were fewer than those of valid ones. Nevertheless, the citations per patent of invalid patents were reduced 5.21, and nearly 5% of the patents were cited only after they became invalid. [ABSTRACT FROM AUTHOR]

Over the last 40 years, several studies have attempted to better understand the function of citations in scholarly communication. While the normative theory claims that researchers acknowledge the intellectual influence to their peers through citations, constructive theory suggests that authors cite to persuade their readers. This study explores this topic by analyzing citations found in ancient Chinese literature and finds that most ancient Chinese authors cite to convince their readers. [ABSTRACT FROM AUTHOR]

This study investigates how to measure subject relationship based on bibliographic coupling strength. Since the 1960s, researchers use citation analysis methods to discover the relationship between different works and authors. However, how to apply citation‐based methods in measuring the relationships between various subjects remains unknown. We propose a novel method to measure relationships between subjects based on bibliographic coupling strengths. Our dataset is composed of 7,692 articles published in 10 core information science journals from 2008 to 2017. The result shows that our method provides another viewpoint of exploring the development of science. Furthermore, our method can identify the core works in different subjects and help to judge how similar different subjects are. [ABSTRACT FROM AUTHOR]

Citation analysis, in addition to peer review, is one of the popular methods to assess faculty research performance. In some disciplines, the validity of citation counts for evaluating research has been proven by many studies. However, it should be used cautiously when different disciplines are concerned, because citation practices may vary across disciplines. Although there have been studies that have examined the different citation practices across disciplines, relatively little research has investigated the citation rates of subject areas within a single discipline. This study explores the citation rates of subject areas in library and information science (LIS) in Korea. The preliminary results of this study will provide the rationale for further work, analyzing the causes of different citation patterns in a discipline. Ultimately, the study could provide useful implications for understanding and using the citation counts in assessing research performance. [ABSTRACT FROM AUTHOR]

It is well known that collaborative papers tend to receive more citations than solo-authored papers. Here we try to identify the subtle factors of this collaborative effect by analyzing metadata and citation counts for co-authored papers in the biomedical domain, after accounting for attributes known to be strong predictors of citation count. Article-level metadata were gathered from 98,000 PubMed article records categorized with the term breast neoplasm, a topic offering longevity and relevance across biomedical subdisciplines, and yielding a relatively large sample size. Open access citation data was obtained from PubMed Central (PMC). Author-level attributes were encoded from disambiguated author name data in PubMed and appended as article-level attributes of collaborations. A logistic regression model was built to assess the relative weights of these factors as influences on citation counts. As expected, the journal and language of the paper were the strongest predictors. The significance of the number of authors diminished after accounting for other attributes. Some of the more subtle predictors included the group's highest hindex, which was positively correlated, while the diversity of author h-indices, minimum professional age, and author's total unique collaborators were negatively correlated. These observations indicate that smaller collaborations composed of early superstars - young, rapidly successful researchers with relatively high and similar h-indices - may be at least as influential in biomedical research as larger collaborations with different demographics. While minimum h-index was important, the first author's h-index was insignificant, underscoring the importance of the middle authors' publishing history. The gender diversity outcomes suggest that mixed groups may be ideal, and further research in this area is indicated. [ABSTRACT FROM AUTHOR]

When quantifying the level of interdisciplinarity for scientific research, most established indicators employ a three‐element diversity framework, namely variety, balance, and disparity, each of which captures a distinct but insufficient element. Among three, disparity, i.e. how different (dissimilar) the categories within a system are, is the most challenging one due to its calculation cost and conceptual ambiguity. The discriminative power for disparity is found to be weakened in more fine‐grained science classification schemes. To address this issue, this paper proposes a new method for quantifying disparity by applying Node2vec on the discipline citation network and retrieving distance between disciplines using embeddings vectors. Compared to cosine‐based dissimilarity for disparity, our proposed method exhibited broader distribution and less skewness for disparity values, which could potentially lead to higher discriminative power of interdisciplinarity. A case study for Linguistics is also conducted to show the capability of detecting variations in disparity of the proposed method. [ABSTRACT FROM AUTHOR]

Citation analyses are used to investigate the influence of architect Christopher Alexander over the period of 1964- 2014. Alexander's work has been so influential that he has been cited by nearly 2000 source items in the Web of Science Core Collection, by Thomson Reuters. Alexander's work has had a profound interdisciplinary reach. Citation analyses reveal that scholars in computing disciplines have cited Alexander's work more than scholars in any other discipline including architecture. Alexander's most cited work, A Pattern Language, is one of two volumes that Alexander refers to as an indivisible whole. These analyses reveal very different citation patterns for the two volumes of this indivisible whole. Although the difference is measurable, in terms of number of citations, we cannot say why this is so. However, the results of these analyses raise several questions including why we, as a community, have for the most part cited only a piece of this indivisible whole. The citation patterns support that this series has been largely reduced to one of its parts, when possibly the whole is greater than the sum of its parts or one of its parts. In this paper I propose that we might begin to see the potential benefits of pattern languages in computing disciplines when we take holistic approach, considering and applying the theory as described in all of the volumes in the series. [ABSTRACT FROM AUTHOR]

Citations and library holdings are two important metrics for assessing book impact. Traditional researches about correlations between these two metrics are mainly based on books' citations and holding numbers. More detailed information about books' holding (i.e. their holding regions) can be used in book impact assessment. In this paper, we will investigate the relationships between citations and more comprehensive features of library holdings, namely holding numbers, holding regions and holding distributions. We obtain citations of 2, 356 Chinese books from CSSCI and the corresponding library holding data from OCLC. Then, we conduct correlation analysis between citations and the three features of library holdings respectively. Results show that there are general weak correlations between the two metrics in all disciplines, while correlations in Linguistics, Ethnology and Archeology are stronger. [ABSTRACT FROM AUTHOR]

The citation relationship between scientific publications has been successfully used for bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relationships from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we propose a novel method to automatically label the massive citations in the scientific repository with different roles, a.k.a. citation role labeling. Unlike earlier studies, we employ pairwise features (similarity between citing and cited paper) and global features (citing and cited paper proximity on the heterogeneous graph), in addition to local features (information extracted solely from the citing paper, e.g. citation textual context). Evaluation result shows pairwise and global features, if properly used, can be very helpful to enhance the citation role labeling performance, especially when full-text data is not readily available. [ABSTRACT FROM AUTHOR]

As a scientific field, scientific mapping offers a set of standardized methods and tools that can be consistently adopted by researchers in different knowledge domains to answer their own research questions. This study examined scientific articles that applied science mapping tools (SMTs) to analyze scientific domains and the citations of these application articles. To understand the roles of these application articles in scholarly communication, we analyzed them and their citations by classifying them into library and information science (LIS) and other fields (non‐LIS) in terms of both publication venues and analyzed domains. Through our preliminary study, we found that science mapping, a topic that is deeply situated in the LIS field, has gained increasing attention from various non‐LIS scientific fields over the past few years. The application articles within and outside of the LIS fields played different roles in advancing the application of science mapping. [ABSTRACT FROM AUTHOR]

Scientific retractions occur for a multitude of reasons. A growing body of research has studied the phenomenon of retraction through systematic analyses of the characteristics of retracted articles and their associated citations. In our study, we focus on the characteristics of articles that cite retracted articles, and the changes in citation dynamics pre‐ and post‐retraction. We leverage descriptive statistics and ego‐network methods to examine 4,871 retracted articles and their citations before and after retraction. Our retracted articles data was obtained from PubMed, Scopus, and Retraction Watch and their citing articles from Scopus. Our findings indicate a stark decrease in post‐retraction citations and that most of these citations came from countries different from the retracted article's country of publication. Citation context analyses of a subset of retracted articles also reveal that post‐retraction citations came from articles with disciplinary and geographical boundaries different from that of the retracted article. [ABSTRACT FROM AUTHOR]

Despite the emergence of e-learning trends, the author cocitation analysis (ACA) method has not yet been used to analyze e-learning extensively. This study applies ACA to analyze the collected literature on e-learning from 1981- 2015, with the goal of identifying the top intellectual areas in three time periods - 1981-1991, 1992-2002, and 2003-2014 - to provide a reference framework for future researchers. Therefore, the objective of this paper is to construct an intellectual framework in the field of e-learning. [ABSTRACT FROM AUTHOR]

In 2007, the Association for Information Systems created a basket of eight journals that claim to bound the discipline. A review of the literature found 41 studies discussing the impact of the basket on scholarly publishing. While the basket of eight has had a positive impact on the impact factors for Information Systems, an increase that has not appeared in the LIS journal set, a review of citations finds a steady increase in the number of basket of eight journals being cited within the basket of eight. This paper discusses the impact the basket of eight has had on cited references. [ABSTRACT FROM AUTHOR]

ABSTRACT Discovery of disciplinary fields, their relationships, and key contributors is an important research field of modern scientometrics. In this contribution, we study the problem of disciplinary/interdisciplinary interpretation of scientific content in the context of higher-order citation graphs. In doing so, we introduce the general approach for representing scientific publishers (conferences, journals) and their relationships by means of multi-dimensional tensor models. Consequently, tensor decomposition methods allow us to identify scientific fields in terms of key authorities, hubs and topical context expressed by keywords and/or fields of study. The proposed strategy of tensor importance sampling allows for efficient and effective analysis of large-scale publication databases in state-of-the art cloud computing infrastructures. Consequently, we present results of our large-scale evaluations for the Microsoft Academic Graph dataset with various tensor modeling strategies, in direct comparison with state of the art content mining and graph mining methods common to the scientometric domain. The comprehensive comparative analysis shows that tensor-based interpretations show considerable agreement with established content mining methods and graph-based authority ranking. At the same time, tensor based analysis allows for discovery of novel inter-disciplinary and trans-disciplinary fields, which are focused on subjects of investigation rather than on established methodologies and strongly connected research communities. In this sense, our approach complements existing mining methods for scientific content, and allows for novel insights into research collaboration patterns. [ABSTRACT FROM AUTHOR]

Citation in academic articles is an important expression of knowledge flow between different disciplines. Analysis of citation content of references from different disciplines can help scholars to understand the role of various disciplines in knowledge flow. This study identifies the discipline of references in academic articles, and then explores the distribution pattern, including cited frequency and cited position (different type of section), of citation content of these references from different disciplines. Finally, we conclude that distribution patterns of different disciplines are different in knowledge flow. Although most references come from natural science, the humanities and social sciences still play an important role in the introduction and background. Basic disciplines like Mathematics are the main providers of methods, and some disciplines relevant to the research topic, like Nursing and Medicine in PLOS, provide more support for the results part of articles. [ABSTRACT FROM AUTHOR]

Discovering authoritative links between publications and the datasets that they use can be a labor‐intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to research datasets, which complements the work of data librarians. We first describe the components of the pipeline and then apply it to expand an authoritative bibliography linking thousands of social science studies to the data‐related publications in which they are used. The pipeline increases recall for literature to review for inclusion in data‐related collections of publications and makes it possible to detect informal data references at scale. We contribute (1) a novel Named Entity Recognition (NER) model that reliably detects informal data references and (2) a dataset connecting items from social science literature with datasets they reference. Together, these contributions enable future work on data reference, data citation networks, and data reuse. [ABSTRACT FROM AUTHOR]

ABSTRACT Team formation becomes important when scientific collaborations become common and important to scholars. This study initially tries to understand how research team size may influence the impact of a study measured by number of citations. Thus, publications in the biology domain are specifically investigated by correlation analysis between team size and scientific impact. The weak correlation suggests that larger team size can positively increase the impact of scientific publications on average; however, it cannot literally bring more highly cited articles. [ABSTRACT FROM AUTHOR]

This study uncovered the gap between scientific and technological contributions and scientific and technological influence by tracking scientists who received two national awards that recognized their scientific and technological achievements. The results show that scientific and technological contributions can be made by the same intellectual ideas, indicating the complex relationship between science and technology. Only a few scientists had received a remarkable number of citations of their scientific publications and patents over decades. The contribution–influence gap indicates that quantifying the scientific and technological contributions of some scientists is difficult. [ABSTRACT FROM AUTHOR]