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Bibliometric analysis is increasingly used to evaluate and compare research performance across geographical regions. However, the problem of missing information from author addresses has not attracted sufficient attention from scholars and practitioners. This study probes the missing data problem in the three core journal citation databases of Web of Science (WoS). Our findings reveal that from 1900 to 2015 over one-fifth of the publications indexed in WoS have completely missing information from the address field. The magnitude of the problem varies greatly among time periods, citation databases, document types, and publishing languages. The problem is especially serious for research in the sciences and social sciences published before the early 1970s and remains significant for recent publications in the arts and humanities. Further examinations suggest that many records with completely missing address information do not represent scholarly research. Full-text scanning of a random sample reveals that about 40% of the missing address articles have some address information that is not indexed in WoS. This study also finds that the problem of partially missing address information for U.S. research has diminished dramatically since 1998. The paper ends by providing some discussion and tentative remedies. 相似文献
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《Journal of Informetrics》2020,14(1):100999
The current study has two objectives. First, we explore the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network. Second, we use the constructed network to examine whether there are outstanding features of relations between drugs and side effects. We extracted drug and side effect names from 169,766 PubMed abstracts published between 2010 to 2014 and constructed author–entity pair bipartite networks after ambiguous author names were processed. We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked biological drug and side effect pairs. To investigate the relationship between a particular drug and a side effect, we compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER, a human expert-curated database. The results of this study indicate that our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests. Further, our approach also identified the unique characteristics of the relation of biomedical entities obtained using an author–entity pair bipartite network. 相似文献
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《Journal of Informetrics》2020,14(4):101066
Author keywords for scientific literature are terms selected and created by authors. Although most studies have focused on how to apply author keywords to represent their research interests, little is known about the process of how authors select keywords. To fill this research gap, this study presents a pilot study on author keyword selection behavior. Our empirical results show that the average percentages of author keywords appearing in titles, abstracts, and both titles and abstracts are 31%, 52.1%, and 56.7%, respectively. Meanwhile, we find that keywords also appear in references and high-frequency keywords. The proportions of author-selected keywords appearing in the references and high-frequency keywords are 41.6% and 56.1%, respectively. In addition, keywords of papers written by core authors (productive authors) are found to appear less frequently in titles and abstracts in their papers than that of others, and appear more frequently in references and high-frequency keywords. The percentages of keywords appearing in titles and abstracts in scientific papers are negatively correlated with citation counts of papers. In contrast, the percentages of author keywords appearing in high-frequency keywords are positively associated with citation counts of papers. 相似文献
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《Journal of Informetrics》2019,13(2):695-707
Twitter accounts have already been used in many scientometric studies, but the meaningfulness of the data for societal impact measurements in research evaluation has been questioned. Earlier research focused on social media counts and neglected the interactive nature of the data. We explore a new network approach based on Twitter data in which we compare author keywords to hashtags as indicators of topics. We analyze the topics of tweeted publications and compare them with the topics of all publications (tweeted and not tweeted). Our exploratory study is based on a comprehensive publication set of climate change research. We are interested in whether Twitter data are able to reveal topics of public discussions which can be separated from research-focused topics. We find that the most tweeted topics regarding climate change research focus on the consequences of climate change for humans. Twitter users are interested in climate change publications which forecast effects of a changing climate on the environment and to adaptation, mitigation and management issues rather than in the methodology of climate-change research and causes of climate change. Our results indicate that publications using scientific jargon are less likely to be tweeted than publications using more general keywords. Twitter networks seem to be able to visualize public discussions about specific topics. 相似文献
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《Journal of Informetrics》2022,16(4):101350
This study explores the impact of different collaboration modes on the cited frequency of publications. Though several studies have obtained some research results, most of them exploit association or regression-based methods, which may not lead to causal conclusions. To overcome the above challenges, we use the Propensity Score Matching (PSM) method to analyze and compare the citation frequencies resulting from four groups of collaboration models: international versus domestic, international multilateral versus international bilateral, domestic inter-organizational versus domestic intra-organizational, and domestic multi-author versus domestic single-author. More specifically, we conduct this analysis by exploring the publications with three computer science subfields from the Web of Science (WoS) database. The experimental results show that international collaboration, especially international multilateral collaboration, has a significant role in increasing the frequency of citations to scientific publications, showing that internationalization and collaboration are critical factors in the growth of the impact of the papers. Among national co-publications, collaborative publications within national organizations receive a higher citation impact. Multi-author collaborations significantly increase citation frequency compared to single-author publications. Our heterogeneity analysis across the different subfields of the computer science domain finds that the treatment effects for the three subfields differ modestly and mostly significant from the whole sample. Moreover, besides the implications for developing research policy and scientist collaboration, our study can capture the causal effect between author collaboration patterns and citation frequency to reveal their causal effects. 相似文献