Singly authored papers contribute the most to scientists’ impact

Utilizing citation data for 100,000 most-cited scientists in the Scopus database, this paper investigated how citations received by an author in different authorship affect his/her academic impact differently. Using a linear regression model as an estimation, it shows that the citations received as the single author of a paper elevates the academic impact the most, followed by that as the first (but not single) author, last author, and middle author. Differences also emerged when we probed into different research fields separately as in some fields citations in the four types of authorship do not differ a lot, and also in some fields, the last-authored citations could 'outweigh' the first-authored ones

Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact

Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science

Tracking self-citations in academic publishing

Citation metrics have value because they aim to make scientific assessment a level playing field, but urgent transparency-based adjustments are necessary to ensure that measurements yield the most accurate picture of impact and excellence. One problematic area is the handling of self-citations, which are either excluded or inappropriately accounted for when using bibliometric indicators for research evaluation. Here, in favor of openly tracking self-citations we report on self-referencing behavior among various academic disciplines as captured by the curated Clarivate Analytics Web of Science database. Specifically, we examined the behavior of 385,616 authors grouped into 15 subject areas like Biology, Chemistry, Science and Technology, Engineering, and Physics. These authors have published 3,240,973 papers that have accumulated 90,806,462 citations, roughly five percent of which are self-citations. Up until now, very little is known about the buildup of self-citations at the author-level and in field-specific contexts. Our view is that hiding self-citation data is indefensible and needlessly confuses any attempts to understand the bibliometric impact of one's work. Instead we urge academics to embrace visibility of citation data in a community of peers, which relies on nuance and openness rather than curated scorekeeping.Peer reviewe

Stochastic modeling of scientific impact

Recent research has found that select scientists have a disproportional share of highly cited papers. Researchers reasoned that this could not have happened if success in science was random and introduced a hidden parameter Q, or talent, to explain this finding. So, the talented high-Q scientists have many high impact papers. Here I show that an upgrade of an old random citation copying model could also explain this finding. In the new model the probability of citation copying is not the same for all papers but is proportional to the logarithm of the total number of citations to all papers of its author. Numerical simulations of the model give results similar to the empirical findings of the Q-factor article.Comment: Accepted for publication by Europhysics Letter

USE OF THE LINK RANKING METHOD TO EVALUATE SCIENTIFIC ACTIVITIES OF SCIENTIFIC SPACE SUBJECTS

A modification of the PageRank method based on link ranking is proposed to evaluate the research results of subjects of the scientific space, taking into account selfcitation. The method of reducing the influence of self-citation on the final evaluation of the results of research activity of subjects of the scientific space is described. The evaluation of the results of research is calculated using the modified PR-q method, taking into account self-citation as a solution of a system of linear algebraic equations, matrix of which consists of coefficients determined by the number of citations of publications of one scientist in the publications of another scientist. The described method can be used for the task of evaluating the activity of the components of the scientific space: scientists, higher education institutions and their structural units. For the task of evaluating the research activity of subjects of the scientific space, a method based on link ranking (PageRank method for web pages) and taking into account the selfcitation of scientists is proposed. The latter allows for an adequate assessment, taking into account the abuses associated with the authors’ excessive self-citation. The essence of the constructed method lies in the construction of a system of linear algebraic equations, whose coefficients of the matrix reflect the citations of some scientists by others in the citation network of scientific publications. The value of the coefficients of the matrix of such a system of linear algebraic equations is subject to certain restrictions, which allow to reduce the influence of the factor of excessive self-citation of the author on his overall assessment of research activity. The described method can be used to calculate the complex evaluation of the components of the scientific space: the scientist, the institution of higher education and its separate structural units. Evaluating research results provides an opportunity to verify the relevance of the research process to the goals identified at the planning stage and, if necessary, to adjust the progress of those studies. Also, the calculation of research evaluations of the components (objects and entities) of the scientific space is a powerful tool for managing research projects

The 100,000 most influential scientists rank : the underrepresentation of Brazilian women in academia

Despite the progress observed in recent years, women are still underrepresented in science worldwide, especially at top positions. Many factors contribute to women progressively leaving academia at different stages of their career, including motherhood, harassment and conscious and unconscious discrimination. Implicit bias plays a major negative role in recognition, promotions and career advancement of female scientists. Recently, a rank of the most influential scientists in the world was created based on several metrics, including the number of published papers and citations. Here, we analyzed the representation of Brazilian scientists in this rank, focusing on gender. Female Brazilian scientists are greatly underrepresented in the rank (11% in the Top 100,000; 18% in the Top 2%). Possible reasons for this observed scenario are related to the metrics used to rank scientists, which reproduce and amplify the well-known implicit bias in peer-review and citations. Male scientists have more self-citation than female scientists and positions in the rank varied when self-citations were included, suggesting that self-citation by male scientists increases their visibility. Discussions on the repercussions of such ranks are pivotal to avoid deepening the gender gap in science

Self-citation and corruption: cross-sectional, cross-country study

# Background Self-citation appears to be widely prevalent. However, the structural drivers of self-citation are poorly understood. # Methods Data for this study were obtained from a recently published study of Scopus data aggregated across all authors with \>5 publications, across all scientific fields, which yielded aggregate, country-level data on the mean co-author self-citation rate for the period 1960-2018. These data were merged with 2018 data from Transparency International on corruption, and additional data extracted from the World Development Indicators. The country-level association between the self-citation rate and the corruption index was estimated using multivariable linear regression. # Results Across 178 countries, the correlation between the mean self-citation rate and the corruption index was -0.52, 95% confidence interval, CI=-0.62 to -0.41. Among the 49 countries in the lowest quartile of the corruption index, the mean self-citation rate was 0.24 (standard deviation, SD=0.06). Among the 44 countries in the highest quartile of the corruption index, the mean self-citation rate was 0.21 (SD=0.05). In a weighted linear regression model with robust estimates of variance, the corruption index had a statistically significant association with the mean self-citation rate (2nd quartile compared with 1st quartile: *b*=-0.08 (95% CI=-0.17 to -0.01); 3rd quartile: *b*=-0.11 (95% CI=-0.19 to -0.02); 4th quartile: *b*=-0.10 (95% CI=-0.19 to -0.01; N=165). The implied effect size was large in magnitude and robust to potential confounding by unmeasured covariates. # Conclusions In this cross-sectional, cross-country analysis, there was a strong correlation between a country’s overall level of corruption and the mean self-citation rate. The estimated association was statistically significant, large in magnitude, and unlikely to be explained away by unmeasured confounding. Better understanding of how corruption norms evolve is likely to be critical in addressing the problem of extreme self-citation and other forms of citation manipulation