Scientometrics最新文献

The rapid increase of Earth science data from remote sensing, models, and ground-based observations highlights an urgent need for effective data management practices. Data repositories track provenance and usage metrics which are crucial for ensuring data integrity and scientific reproducibility. Although the introduction of Digital Object Identifiers (DOIs) for datasets in the late 1990s has significantly aided in crediting creators and enhancing dataset discoverability (akin to traditional research citations), considerable challenges persist in establishing linkage of datasets used with scholarly documents. This study evaluates the citation coverage of datasets from NASA’s Earth Observing System Data and Information System (EOSDIS) across several major bibliographic sources ‒ namely Google Scholar (GS), Web of Science (WoS), Scopus, Crossref, and DataCite—which helps data managers in making informed decisions when selecting bibliographic sources. We provide a robust and comprehensive understanding of the citation landscape, crucial for advancing data management practices and advancing open science. Our study searched and analyzed temporal trends across the bibliographic sources for publications that cite approximately 11,000 DOIs associated with EOSDIS datasets, yielding 17,000 unique journal and conference articles, reports, and book records linked to 3,000 dataset DOIs. GS emerged as the most comprehensive source while Crossref lagged significantly behind the other major sources. Crossref’s record references revealed that the absence of dataset DOIs and shortcomings in the Crossref Event data interface likely contributed to its underperformance. Scopus initially outperformed WoS until 2020, after which WoS began to show superior performance. Overall, our study underscores the necessity of utilizing multiple bibliographic sources for citation analysis, particularly for exploring dataset-to-document connections.

Science and technology human resources are fundamental components for enhancing the efficiency of the national innovation system. This study aims to examine the co-evolutionary relationship between scientific collaboration and scientific mobility, explore the dynamic development process of collaboration and talent flow within the global science system, and offer insights for developing suitable policies related to scientific mobility and international collaboration. The study employs Scopus data from 1788 to 2020 to investigate the systematic co-evolution of scientific talent flow and scientific collaboration from a macro and long-term perspective. The findings indicate that: (1) The global scientific flow and collaboration networks are increasingly interconnected, with a rising prevalence of international mobility and intensified worldwide collaboration. (2) Both networks exhibit cluster structures that have evolved over time, with a shift towards more random network configurations, reflecting more extensive and frequent global scientific interactions. (3) The “Matthew Effect” is observed, highlighting an imbalance with a few dominant players and many minor participants, while advanced countries demonstrate greater alignment between collaboration and mobility networks than lagging ones. Policy implications include encouraging international research mobility, supporting cooperation within scientific clusters, and prioritizing connections with global research hubs while engaging with peripheral countries.

Policy citations are considered as one of the important indicators of the societal impact of research. Scientometrics is a field that, among other goals, focus on contributing to science policy, so the presence of scientometric researches in policy documents become important. Accordingly, this study aims to measure the impact of scientometric researches on policy by examining the mentions of scientometric articles in policy documents. The dataset used in this study includes 5525 scientometric articles indexed in Web of Science between 2013 and 2022. The Overton database were used to collect policy citations. The results showed that out of 5525 scientometric articles, 921 articles (16.67%) were cited at least once in policy documents. Additionally, older articles were cited more frequently than recent ones in policy documents. Scientometrics Journal ranked first in terms of the number of articles being cited in policy documents, while Research Policy and Research Evaluation Journals ranked first and second, respectively, in terms of coverage, density, and intensity. Subject analysis of the cited articles in policy documents showed that articles on national/international scholar collaborations, scholar productivity/scholar performance, and funding and sponsorship were cited more frequently in policy documents. Finally, Open Access articles were cited more frequently than non-open access articles in policy documents. However, there was not significant difference between policy citations of multi-authored and sing-authored articles. Overall, policy citations of scientometric articles were fair compared to other fields, and for greater impact of this field on policy, publishing open access, and greater attention to the topics identified in this study can be helpful.

The number of positive words in scientific papers has exhibited a notable upwards trend over the past few decades. However, there remains a gap in our comprehensive understanding of the relationship between positive words and research impact. In this study, we conduct a multifaceted exploration of the citation advantage associated with positive words based on social cognitive theory, examining its predictability, temporal evolution, and universality across journals of varying quality grades. Drawing from a corpus encompassing 124,144 papers published in the management field between 2001 and 2020, our regression results provide compelling evidence suggesting that positive words can serve as a significant predictor of the citation counts of academic papers, supporting the citation advantage of positive words. However, it is essential to recognize that over time, the citation advantage attributed to positive words is experiencing a conspicuous decline. The universality of the above phenomenon has been further verified in the analysis of journals of different quality. Our findings prompt a discussion regarding the need to pay more attention to the overuse and misuse of positive words, as well as practical considerations for enhancing scientific communication within the academic community.

A recent study published in Scientometrics used publications in Scopus and Web of Science Core Collection to exam the decades-long scientific collaboration between Cuba and China (Ronda-Pupo, Scientometrics 129:785–802, 2024). Ronda-Pupo’s finding of the significant growth of research collaboration between these two countries evidenced by the number of co-authored papers is different from our daily perception of the scientific collaboration between China and Cuba. By using the same data, we find the dominating role of multilateral co-authorship rather than bilateral or trilateral co-authorship in Cuba-China scientific collaboration. This important finding gives an alternative explanation of the increasing Cuba-China co-authored publications. Through the supplement of our exploration, readers can have a better understanding of the Cuba-China scientific collaboration.

This study comprehensively analyses gender representation and citation disparities in gender studies by examining the position of female scholars as first and corresponding authors. The research uncovers a pattern of gender-homogeneous co-authorship and investigates the geographical and economic disparities in academic contributions, scrutinising the impact of a country’s economic status on citation rates and open-access publications, particularly in relation to citation rates and open-access publications. The study uses a Logistics Regression and Zero-Inflated Negative Binomial Regression model to explore factors influencing open-access publication and citation rates. The study’s findings demonstrate the predominant presence of female scholars in gender-focused literature within social sciences, in contrast to their underrepresentation in STEM fields. The findings also reveal a tendency towards gender-homogenous collaborations and a significant concentration of scholarly output from the high-income regions, highlighting both geographic and economic disparities. The present study provides an analytical foundation for future studies on the global distribution of scholarly contributions and the complex interplay of various factors affecting academic publishing in gender studies.

Exploring a robust and universal appeal bibliometric indicator for assessing creativity is essential but challenging. The novelty measure of innovation proposed by Uzzi et al. (NoveltyU) has sparked considerable interest and debate. Thus, further validation and understanding of its portfolio form of novelty and scope of application are necessary. This paper delves into the calculation and application of the NoveltyU method to shed light on its effectiveness and scope. Analysis of the calculation process reveals that journal pairs with higher novelty often span independent fundamental areas, while those with lower novelty tend to focus on similar and applied fields. Utilizing collaboration patterns between institutions, as discussed in our prior study (Fan et al., Scientometrics 125:1179–1196, 2020), offers insight into the method’s performance in real-world contexts. Results consistently show higher mean NoveltyU values in MM pattern over time, affirming the method’s validity. Categorizing papers into high conventional, low conventional, low novel, and high novel categories unveils higher overlap degree of terms among different patterns in high novel papers. Moreover, leading terms in MM pattern exhibit specific information, while delay terms tend to be more general, and simultaneous terms are even more so. These findings offer valuable insights into identifying hot and frontier topics, bolstering the credibility and application potential of the NoveltyU method, aligning with the broader objective of establishing valid measures of innovativeness in research.

Many claims have been made in the past that Management and Organization Studies (MOS) is becoming increasingly fragmented, and that this fragmentation is causing it to drift into self-reference and irrelevance. Despite the weight of this claim, it has not yet been subjected to a systematic empirical test. This paper addresses this research gap using the tribalization approach and diachronic co-citation analyses. Based on 22,430 papers published in 14 MOS journals between 1980 and 2019, we calculate local and global centrality measures and the flow of cited articles between co-citation communities over time. In addition, we use a node-removal strategy to test whether only ritualized citations ensure MOS cohesion. Rather than tribalization, our results suggest a center–periphery structure. Furthermore, more peripheral papers are integrated into the central co-citation communities, but the lion's share of the flow of cited papers occurs over time to only a small number of large clusters. An increase of fragmentation and crowding-out of smaller clusters in MOS in seen in the polycentrically organized core 2014–2019.

As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers’ citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers.

Metaphors play a crucial role in facilitating the comprehension and analysis of knowledge. “Knowledge as energy” is a well-established metaphorical framework that provides unique benefits for comprehending the dissemination of knowledge and enabling its quantification. Nevertheless, empirical studies employing this framework are limited, especially in the area of the work–energy metaphor, which primarily remains theoretical. This paper proposes an application scheme for the work– energy metaphor in interdisciplinary citation analysis. In this scheme, disciplines are considered entities; various factors that drive the progress of a discipline are considered forces; energy is considered the knowledge produced or transferred in the citations. Building upon the work–energy theorem in physics, this study developed indicators reflecting citation quality and velocity to assess interdisciplinary research progression. An empirical investigation was carried out, utilizing these indicators to evaluate the influence of interdisciplinary citations on disciplines. In the experiments, we used Library and Information Science (LIS) from 2012 to 2021 as an example to analyze the impact of interdisciplinary citations from LIS on other disciplines over two time periods. The experiments demonstrated the feasibility of the work–energy metaphorical framework proposed in this paper. It was also found that Computer Science, Management, and Business experienced the highest impact from LIS interdisciplinary citations and exhibited steady growth over a 10-year period. Environmental Science has substantial potential for the future.