Abstract Equal-contribution authorship, in which two or more authors are designated as having contributed equally, is increasingly common in scientific publishing. Using approximately 480,000 tagged records from PubMed and PMC (2010-2024), we examine temporal trends, journal-level patterns, geographic distributions, and byline positions of equal-contributing authors. Results show a sharp rise after 2017, with both high-output mega-journals and smaller, discipline-specific journals contributing to the growth. Journal-level analysis indicates a median increase in the share of tagged articles from about 19% in 2015 to over 30% in 2024, with some journals exceeding 50%. Geographically, China accounts for the largest share (40.8% of fractionalized contributions), followed by the United States (15.2%) and Germany (5.2%). Normalizing to 2015 baselines, China shows a 13.1×; increase by 2024, while even the slowest-growing countries more than tripled their levels. Analysis of normalized byline positions shows that equal-contribution designations are concentrated near the first-author position, with fewer cases in middle or last positions. These findings document a broad shift toward shared first-author credit across journal sizes and regions within the biomedical literature and suggest that journals and evaluators may need to rely more on transparent contributorship information and to monitor the use of such labels over time.

Abstract Understanding who shares academic publications on Twitter is critical to interpreting altmetrics as signals of scholarly or societal impact. Prior studies have used diverse and often incompatible user classification schemes, making synthesis difficult. This study presents a systematic review and meta-analysis of 23 empirical studies (covering 79,014 Twitter users, over 20 million tweets, and more than 5 million tweeted publications) to estimate category-specific engagement across three metrics: user counts, tweets, and tweeted publications. We developed a harmonized categorization scheme encompassing 11 user types and applied both Random Effects Models (REM) and Beta-Binomial Hierarchical Models (BBHM) to estimate proportions, account for study-level variation, and model uncertainty. Across all indicators, individual users were the most active, comprising 66% of users, 55% of tweets, and 50% of tweeted publications. BBHM further enabled in-category vs. out-of-category comparisons and revealed engagement differences not detected by REM. T-tests on study-level means confirmed significant differences between academic individuals and other user types. Despite methodological heterogeneity, results consistently show that academic and non-academic individuals statistically equally dominate Twitter engagement with scholarly content. Our findings support the need for standardized user classification schemes and demonstrate the value of Bayesian modeling for synthesizing altmetric data in study variation and sparsity.

Abstract International research collaboration is essential for tackling global challenges, many of which disproportionately affect low- and middle-income countries (LMICs), while scientific capital remains concentrated in high-income countries (HICs). Drawing on CRediT contributorship data from 2018-2023 and past and present author affiliations, this study examines how labor is divided in teams between researchers from countries with different economic resources, offering new insights into the role of mobility in addressing structural disparities. Our findings reveal a pronounced divide: data collection is primarily conducted in LMICs, while data analysis and publication efforts are concentrated in HICs. Although often collaborative, conceptualization is more frequently led by scientists with past or present HIC ties, reinforcing ongoing debates about intellectual ownership. Mobile researchers with past and present ties to both HICs and LMICs emerge as key contributors to conceptualization and writing, leveraging their unique position to integrate awareness of LMIC challenges with scientific capital in HICs. By shedding light on how access to scientific capital shapes contributions within international teams, this study informs policy efforts to foster more equitable collaborations that align with the needs of LMICs, ensuring global research can effectively tackle pressing challenges.

Abstract Estimates of the frequency of research misconduct appear much higher in self-report surveys than would be expected from numbers of institutional investigations, but there is little hard data on this topic. U.K. universities produce annual statements on research integrity. The most recent statements were analyzed and compared with evidence of fabrication/falsification of data from reports on the PubPeer website. In 117 institutional statements with usable data, there were only 25 allegations of fabrication or falsification involving 13 universities. On PubPeer comments in 2023 for all publications that included a lead U.K. author there were 49 comments describing fabrication/falsification of data (mostly digitally manipulated images), and 28 with hallmarks of a paper mill. Only nine universities had more than one PubPeer entry compatible with fabrication/falsification, but four of these involved senior researchers with multiple problematic publications; in none of these cases had the institution upheld an allegation of misconduct. It is recommended that it should not be left to the employing institution to deal with allegations of serious research misconduct, that PubPeer could be used proactively in investigations of misconduct, and that research integrity reports should be made openly available to increase confidence in the process.

Abstract Interdisciplinary knowledge transfer is hindered by information overload and siloed reading and citation practices. Research paper recommender systems, which tend to overemphasize similarity and relevance, can perpetuate information silos due to the so-called “filter bubble” effect. In this work, we argue for the importance of offering novel and diverse research paper recommendations to scientists in order to reduce siloed reading and facilitate interdisciplinary knowledge transfer. We also identify important RP-Rec-Sys methodologies which serve this purpose. Specifically, we propose a novel framework for evaluating the novelty and diversity of research paper recommendations, drawing on methods from network analysis and natural language processing. Using this framework, we show that the choice of representational method within a larger research paper recommendation system can have a measurable impact on the nature of downstream recommendations, specifically on their novelty and diversity. We describe a paper embedding method that provides more distant and diverse research paper recommendations without sacrificing the relevance of those recommendations compared to other state-of-the-art baselines. By recommending relevant research to users that is distant and dissimilar from their own work, we present a viable method to facilitate interdisciplinary knowledge transfer using research paper recommender systems.

Abstract It has been suggested that polysemy in scientific language allows communities to engage in a common research program. However, polysemy might conflict with rules that require words to have a precise meaning. We conceptualize polysemy as a set of relationships between word senses and contexts of usage and we suggest that, to sustain interdisciplinarity, a word should display stable patterns of activation of senses in contexts, and accordingly be both precise and bridging across contexts. We analysed documents in Scopus including the word ‘argumentation’ in title, keywords or abstract. Our analysis displays the layered nature of polysemy, in that there is a set of distinct senses, that also related to specific contexts of usage, such as scientific disciplines, journals and communities; at the same time, we could identify a small set of shared senses that allows bridging between topics. Moreover, we provided evidence that senses are selectively combined within scientific publications and that a significant number of scholars is using the word in different senses in their scientific production. This suggests that the word ‘argumentation’ is able to foster the exchange of concepts and the cooperation of scholars across diverse scientific domains such as philosophy, political sciences and informatics.

Abstract We test the feasibility of incorporating broad social, political, and governance indicators with standard metrics as a way to enrich assessment of national research capacity. We factor analyze two sets of variables for 174 countries from 2012 to 2021, one being tradtional measures associated with national science and technology capacity, such as spending, and a second being broader social, political, and governance measures, such as academic freedom. As expected, two factors emerge, one for raw or “core” research capacity and the other indicating the wider governance context. Further analysis shows convergent validity within the two factors and divergent validity between them. The analysis also quantifies the contribution of each indicator to each factor. Nations rank differently for each factor and also when combined. Ranks vary as a function of the chosen aggregation method. As a test of the predictive validity of the capacity index, we find both factors to be associated with country-level field-weighted citation indices. Policymakers and analysts may find useful feedback from this approach to quantifying national research strength.

Abstract Scientific breakthroughs possess the transformative potential to reshape research trajectories and scientific paradigms. However, there is limited systematic evidence on how these breakthroughs influence the evolution of scientific knowledge. Building on the concepts of disruption and consolidation in science, we categorize forward-citing papers into two distinct categories: Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP). Analyzing the dynamic patterns of DCP and CCP in Nobel Prize–winning papers, we find that in the early postpublication phase, scientific breakthroughs generate more consolidating citations than disruptive citations. Additionally, CCP in this early phase demonstrate higher scientific impact. However, in the long-term phase, scientific breakthroughs generate more disruptive citations, with DCP often involving larger and more diverse teams. Linguistic analysis also uncovers nuanced differences between CCP and DCP. Furthermore, the dynamic patterns of knowledge flow in scientific breakthroughs differ significantly from control groups. Collectively, our results reveal that scientific breakthroughs initially consolidate knowledge before disrupting it in later phases, offering profound insights into the mechanisms driving scientific progress.

Abstract Historically Black Colleges and Universities (HBCUs) play a foundational role in promoting equality in U.S. higher education and society. Studying faculty transitions and research dynamics at HBCUs is crucial to understanding and addressing these institutions’ challenges, such as the brain drain and its relationships with faculty research practices. By tracking the affiliation changes of 139 professors and their research outcomes (consisting of 4,269 publications) and comparing them with a matched control group with similar backgrounds, we revealed a moving penalty for professors moving from Predominantly White Institutions (PWIs) to HBCUs, who experienced declines in research productivity and citation impact. In contrast, professors transitioning from HBCUs to PWIs benefited from the moving premium of increasing high-impact publications. Professors at HBCUs tend to increase their collaborations with PWIs before transitioning, while those moving to PWIs reduce their collaborations with HBCUs. Our findings highlight the ongoing challenges HBCUs face and underscore the need for comprehensive strategies to strengthen these institutions’ research functionality and ultimately their overall academic standing.

Abstract To provide valuable insights for shaping future funding policies, in this study, we offer a comprehensive panorama of the research funding across 171 SCI disciplines in the decade 2011–2020, based on more than 13 million scientific literature records from the Web of Science. The relationship between funding and research impact is also explored. To this end, we employ two indicators: the universality and multiplicity of funding, to indicate the funding level and six indicators to gauge the impact advantages of funding. Our findings reveal an upward trend in both the universality (increasing from 66.30% to 74.26%) and multiplicity (increasing from 2.82 to 3.26) of funding over the decade concerned. The allocation of funding varies across disciplines, with life sciences and earth sciences receiving the highest percentage of funding (78.31%) and medicine having the highest multiplicity of funding (3.07). Engineering and computer science have seen relatively rapid growth in terms of universality and multiplicity of funding. Funded articles have a greater impact than unfunded ones. And this impact strengthens as the number of funding grants increases. Through regression analysis, the citation advantage of funding is also proven at the article level, although the usage advantage is not significant.