Scientometrics最新文献

We find out which older publications (defined here as published before 1991) are nowadays, i.e. during the period [2013–2022], the most cited in leading bibliometric journals. It is found that Small’s article on co-citation analysis is the most-cited one in these journals, followed by Garfield’s “Citation Analysis as a Tool in Journal Evaluation” and Merton’s article on the Matthew Effect. As expected, also Price figures prominently among colleagues with older, highly-cited publications. Moreover, we performed the same exercise for citations received during the period [2003–2012] and compared the corresponding top twenty lists as a study in the obsolescence of a group of older publications.

This study reports on the yearly rate of journal self-citation (JSC) in sport sciences, how it changes over time, and its association with journal impact factor (JIF). Citations made by all 87 journals in “sport sciences” from 2013 to 2022 were extracted, as was their 2022 JIF. JSC rates were calculated using a Poisson distribution method. A mixed-effects negative binomial regression examined changes in yearly JSC rates over time. The association between average JSC rates and JIF were compared using a negative binomial regression. The median JSC rate was 6.3 self-citations per 100 citations. JSC rates are increasing in sport sciences by ~ 10% per year (incidence rate ratio [IRR] = 1.1, 95% CI 1.1–1.2; trivial effect). There was a significant negative association between JSC rate and JIF (IRR = 0.9, 95% CI 0.9, 1.0; trivial effect). Contrary to observations made in prior literature examining broader disciplines, the increasing JSC rate in sport sciences may be attributed to the growing maturity of this novel discipline. As sport-science topic areas become more established and appear in discipline specific journals, more JSCs may occur due to an increasing body of literature in these journals. The negative association between JSC rate and JIF may be due to specialized and less visible journals having a naturally lower JIF, as their impact is confined to a narrower field.

In general, the presence and performance of women in science have increased significantly in recent decades. However, gender-related differences persist and remain a global phenomenon. Women make a greater contribution to multidisciplinary research, which renders anti-doping research a compelling area for investigating the gendered aspects of academic research. The research design was based on the overall research aim to investigate whether gender in a specific field (ADS) has an effect on different aspects of research impact, including (1) the size of citation impact obtained by the research output, (2) the impact on the development of the knowledge base of ADS, expressed as the capacity of integrating knowledge from different research areas, and (3) the (expected) type of research impact targeting either societal or scientific developments (or both). We used a previously compiled dataset of 1341 scientific outputs. Using regression analysis, we explored the role of authors’ gender in citations and the effect of authorship features on scientific impact. We employed network analysis and developed a novel indicator (LinkScore) to quantify gendered authors’ knowledge integration capacity. We carried out a content analysis on a subsample of 210 outputs to explore gender differences in research goal orientation as related to gender patterns. Women’s representation has been considerably extended in the domain of ADS throughout the last two decades. On average, outputs with female corresponding authors yield a higher average citation score. Regarding women's knowledge integration roles, we can infer that no substantial gender differences can be detected. Dominantly female papers were overrepresented among publications classified as aimed at scientific progress, while the share of male-authored papers was higher in publications classified as aimed at societal progress. Although no significant gender difference was observed in knowledge integration roles, in anti-doping women appear to be more interdisciplinary than men.

In this study, the coverage and overlap of retracted publications, retraction notices and withdrawals are compared across seven significant scholarly databases, with the aim to check for discrepancies, pinpoint the causes of those discrepancies, and choose the best product to produce the most accurate picture of retracted literature. Seven scholarly databases were searched to obtain all the retracted publications, retraction notices and withdrawal from 2000. Only web search interfaces were used, excepting in OpenAlex and Scilit. The findings demonstrate that non-selective databases (Dimensions, OpenAlex, Scilit, and The Lens) index a greater amount of retracted literature than do databases that rely their indexation on venue selection (PubMed, Scopus, and WoS). The key factors explaining these discrepancies are the indexation of withdrawals and proceeding articles. Additionally, the high coverage of OpenAlex and Scilit could be explained by the inaccurate labeling of retracted documents in Scopus, Dimensions, and The Lens. 99% of the sample is jointly covered by OpenAlex, Scilit and WoS. The study suggests that research on retracted literature would require querying more than one source and that it should be advisable to accurately identify and label this literature in academic databases.

H-Index is a widely used metric for measuring scientific output. In this paper we showcase the weakness of this index as regards co-authorship. By ignoring the number of co-authors, each author gets the full credit of a joint work, something that is not fair for evaluation purposes. For this purpose we report the results of simulation scenarios that demonstrate the impact that co-authorship can have. To tackle this weakness, and achieve a more fair evaluation, we propose a few simple variations of H-index that consider the number of co-authors, as well as the active time period of a researcher. In particular we propose using HI/co and HI/(coy), two metrics that are simple to understand and compute, and thus they are convenient for decision making. The simulation shows that they can tackle well co-authorship. Subsequently we report measurements over real data of researchers coming from five universities (Cambridge, Crete, Harvard, Oxford and Ziauddin), as well as other datasets, that reveal big variations in the average number of co-authors. In total, we analyzed 526 authors, having in total more than 127 thousands publications, and 16.7 million citations. These measurements revealed big variations of the number of co-authors. Consequently, by including the number of co-authors in the measures for scientific output (e.g. through the proposed HI/co) we get rankings that differ significantly from the rankings obtained by citations, or by the plain H-Index. The normalized Kendall’s tau distance of these rankings ranged from 0.28 to 0.46, which is quite high.

Gender-Based differences in h-indices across fields, including psychology, social work, and the biomedical sciences have been reported. These differences are persistent across all faculty ranks, including assistant, associate, and full professors, but may be larger for early career and senior faculty. Even with these known biases, the h-index remains a widely used metric of the productivity and impact of research scientists and university faculty. Recently, several studies have drawn attention to the potential for a widening gender gap in academic metrics given the ways in which gendered roles, and thus research productivity, were inequitably impacted by the COVID-19 pandemic. We describe the association between gender and h-index among a sample of tenured faculty from epidemiology departments in Schools and Programs of Public Health. Gender explained 1.2% of the variance in h-indices; after adjustment for professional age, gender explained only 0.1% of the variance. There was also crossover interaction for professional age and gender, with women having lower h-indices in early career yet overtaking males later. If h-indices are utilized as metrics for promotion and tenure, or as criteria for appointments to leadership or other roles, gender bias will continue to limit early- and mid-career women’s inclusion and advancement.

Prevailing attention centers on the plight of female scientists in modern academia. However, female contributions and potential remain insufficiently recognized. To unravel this veil, we leverage large-scale cross-disciplinary datasets from SciSciNet to portray female participation over the past 20 years and quantify the female effect on research using bibliometric indicators. Female ratio is utilized to gauge gender composition within teams. Through successive modeling including mixed-effect and multivariate regressions, we disentangle the intricate effects of female presence and extent of female participation on research impact and dual innovation metrics. We find a steady rise in female-inclusive teams and per-team female ratios over time, with variations across disciplines and broad categories. We demonstrate an inverted U-shaped relationship between female ratio and citation counts—gender-balanced teams typically garner peak citations, while highly-cited vertices drift toward male-skewed teams in male-majority areas. Increasing female participation yields significant gains in innovation. In the upstream of knowledge flow, as captured by novelty (z-scores), female-skewed teams tend to combine more unconventional knowledge. For the downstream, as encapsulated through disruption, female-skewed teams’ innovation efforts have been recognized by follow-on citations. Notably, the female advantage in innovation becomes more evident in male-dominated fields and intensifies over time. Our study offers insights into the unique academic value and the tremendous scientific contributions of females, providing important visions for institutional and policy reforms.

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.