Authorship is associated with scientific capital and prestige, and corresponding authorship is used in evaluation as a proxy for scientific status. However, there are no empirical analyses on the validity of the corresponding authorship metadata in bibliometric databases. This paper looks at differences in the corresponding authorship metadata in Web of Science (WoS) and Scopus to investigate how the relationship between author position and corresponding authors varies by discipline and country and analyzes changes in the position of corresponding authors over time. We find that both WoS and Scopus have accuracy issues when it comes to assigning corresponding authorship. Although the number of documents with a reprint author has increased over time in both databases, WoS indexed more of those papers than Scopus, and there are significant differences between the two databases in terms of who the corresponding author is. Although metadata is not complete in WoS, corresponding authors are normally first authors with a declining trend over time, favoring middle and last authors, especially in the Medical, Natural Sciences, and Engineering fields. These results reinforce the importance of considering how databases operationalize and index concepts such as corresponding authors, this being particularly important when they are used in research assessment.

Diversity in human capital is widely seen as critical to creating holistic and high-quality research, especially in areas that engage with diverse cultures, environments, and challenges. Quantification of diverse academic collaborations and their effect on research quality is lacking, especially at international scale and across different domains. Here, we present the first effort to measure the impact of geographic diversity in coauthorships on the citation of their papers across different academic domains. Our results unequivocally show that geographic coauthor diversity improves paper citation, but very long distance collaborations have variable impact. We also discover “well-trodden” collaboration circles that yield much less impact than similar travel distances. These relationships are observed to exist across different subject areas, but with varying strengths. These findings can help academics identify new opportunities from a diversity perspective, as well as inform funders on areas that require additional mobility support.

Measuring international research collaboration (IRC) is essential to various research assessment tasks but the effect of various measurement decisions, including which data sources to use, has not been thoroughly studied. To better understand the effect of data source choice on IRC measurement, we design and implement a data quality assessment framework specifically for bibliographic data by reviewing and selecting available dimensions and designing appropriate computable metrics, and then validate the framework by applying it to four popular sources of bibliographic data: Microsoft Academic Graph, Web of Science (WoS), Dimensions, and the ACM Digital Library. Successful validation of the framework suggests it is consistent with the popular conceptual framework of information quality proposed by Wang and Strong (1996) and adequately identifies the differences in quality in the sources examined. Application of the framework reveals that WoS has the highest overall quality among the sets considered; and that the differences in quality can be explained primarily by how the data sources are organized. Our study comprises a methodological contribution that enables researchers to apply this IRC measurement tool in their studies and makes an empirical contribution by further characterizing four popular sources of bibliographic data and their impact on IRC measurement.

We study the citation dynamics of the papers published in three scientific disciplines (Physics, Economics, and Mathematics) and four broad scientific categories (Medical, Natural, Social Sciences, and Arts & Humanities). We measure the uncitedness ratio, namely, the fraction of uncited papers in these data sets and its dependence on the time following publication. These measurements are compared with a model of citation dynamics that considers acquiring citations as an inhomogeneous Poisson process. The model captures the fraction of uncited papers in our collections fairly well, suggesting that uncitedness is an inevitable consequence of the Poisson statistics.

We analyzed a data set of scientific manuscripts that were submitted to various conferences in artificial intelligence. We performed a combination of semantic, lexical, and psycholinguistic analyses of the full text of the manuscripts and compared them with the outcome of the peer review process. We found that accepted manuscripts scored lower than rejected manuscripts on two indicators of readability, and that they also used more scientific and artificial intelligence jargon. We also found that accepted manuscripts were written with words that are less frequent, that are acquired at an older age, and that are more abstract than rejected manuscripts. The analysis of references included in the manuscripts revealed that the subset of accepted submissions were more likely to cite the same publications. This finding was echoed by pairwise comparisons of the word content of the manuscripts (i.e., an indicator of semantic similarity), which were more similar in the subset of accepted manuscripts. Finally, we predicted the peer review outcome of manuscripts with their word content, with words related to machine learning and neural networks positively related to acceptance, whereas words related to logic, symbolic processing, and knowledge-based systems negatively related to acceptance.

Contributorship statements were introduced by scholarly journals in the late 1990s to provide more details on the specific contributions made by authors to research papers. After more than a decade of idiosyncratic taxonomies by journals, a partnership between medical journals and standards organizations has led to the establishment, in 2015, of the Contributor Roles Taxonomy (CRediT), which provides a standardized set of 14 research contributions. Using the data from Public Library of Science (PLOS) journals over the 2017–2018 period ( N = 30,054 papers), this paper analyzes how research contributions are divided across research teams, focusing on the association between division of labor and number of authors, and authors’ position and specific contributions. It also assesses whether some contributions are more likely to be performed in conjunction with others and examines how the new taxonomy provides greater insight into the gendered nature of labor division. The paper concludes with a discussion of results with respect to current issues in research evaluation, science policy, and responsible research practices.

To promote research excellence, China’s government has been offering substantial financial support for a small group of selected universities through three national research programs (Project 211, Project 985, Double First Class). However, admission to these programs may not be completely merit based. Based on a statistical analysis of Chinese universities’ scientific activities, this paper shows that this institutionalized hierarchy is not supported by empirical data on research performance, which contributes to inequalities and inefficiencies in Chinese higher education. To build and maintain research capacity, China must support meritocracy across the research system.

Acknowledgements found in scholarly papers allow for credit attribution of nonauthor contributors. As such, they are associated with a different kind of recognition than authorship. While several studies have shown that social factors affect authorship and citation practices, few analyses have been performed on acknowledgements. Based on 878,250 acknowledgees mentioned in 291,167 papers published between 2015 and 2017, this study analyzes the gender and academic status of individuals named in the acknowledgements of scientific papers. Our results show that gender disparities generally found in authorship can be extended to acknowledgements, and that women are even more underrepresented in acknowledgements section than in authors’ lists. Our findings also show that women acknowledge proportionally more women than men do. Regarding academic status, our results show that acknowledgees who have already published tend to have a higher position in the academic hierarchy compared with all Web of Science (WoS) authors. Taken together, these findings suggest that acknowledgement practices might be associated with academic status and gender.

The publication of special issues constitute an important yet underinvestigated phenomenon of scholarly communication. In an attempt to draw attention to the proliferation of special issues, Priem (2006) suggested that their commissioning has an underestimated opportunity cost, given the relative scarcity of publication space: by distorting the “marketplace for ideas” through the commanding of preselected topical distributions, special issues undermine the total research output by “squeezing out” high-quality but topically unrelated articles. The present paper attempts to test this hypothesis by providing a topicality and research impact analysis of conference-based, monographic, and regular issues published between 2010 and 2015 inclusive and indexed in Clarivate Analytics’ Web of Science. The results show that the titles and abstracts of articles copublished are topically closer to each other than those copublished in regular issues, which suggests that their relative importance might influence the total topical distribution. However, disciplinary and overall comparison of relative citations for both special and regular issues shows that intraissue averages and variances in the former case are respectively higher and lower than in the regular issue context, which undermines not only the abovementioned hypothesis, but also the belief that editors often “fill up” special issues by accepting substandard papers.