Frontiers in research metrics and analytics最新文献

The increasing availability and accessibility of artificial intelligence have triggered a seismic transformation of the publishing value chain, presenting unprecedented opportunities and challenges for publishers. Artificial Intelligence (AI) has been integrated into the entire publishing value chain, streamlining the processes of content acquisition by authors and publishers, content and product development, as well as the marketing and distribution of products. However, the disruptive force of AI renders some publishing functions obsolete and transforms the production and distribution of materials, and subsequently, knowledge dissemination. Despite the acknowledged value and potential of integrating especially generative artificial intelligence in the publishing industry, concerns have been raised over integrity, copyright and intellectual property rights in AI-generated content, text and data mining. The impetus of this study emanates from a dearth of literature on the adoption, challenges and opportunities associated with the integration of AI in the production, dissemination and distribution of publications in the book sector in Africa. This paper explores the role of artificial intelligence in the production and distribution of teaching and learning materials by educational publishers. Informed by the disruptive innovation theory, this conceptual paper provides a review of the extant literature on the integration of artificial intelligence in the educational publishing value chain in Africa and its implications on creativity, integrity and intellectual property rights issues associated with AI-generated content. The paper will proffer recommendations.

Introduction: In Italy, legislation in Exercise and sports sciences (ESS) had significant progress, particularly in physical and sport education, as well as sport pedagogy for compulsory teaching of Physical education by master's degree' specialist teachers, for the new profession of kinesiologist and, first the reform of Italian Constitution about social and educational value of movement and sport. The aim of this study was to measure the specific scientific output in ESS and its impact on the scientific community.

Methods: Data on global scientific production related to the keywords "Physical education," "Sport education," and "Sport pedagogy" were extracted from the Scopus database, including total citations and h-index. Trends among Italian researchers were also examined. Data on the overall scientific output and specific trends of Italian researchers were extracted, along with total citations and h-index. The trends of total and relative metrics (citations and h-index) for the period 2020-2023 were evaluated using Spearman's correlation.

Results: Analysis of global scientific production on Scopus revealed 31 ESS faculty members (7 full professors, 15 associate professors and 9 researchers) for "Sport Education" keyword, 11 ESS faculty members (2 full professors, 5 associate professors, and 4 researcher) for "Sport Pedagogy," and 18 ESS faculty members (7 full professors, 7 associate professors and 4 researcher) for "Physical Education." Less than half of these researchers are directly framed with ESS. However, despite this minority representation, significant positive correlations emerged between total citations and total H-index for the period 2020-2023 (rs = 0.83), relative citations and relative H-index (rs = 0.61), relative citation and total H-index (rs = 0.32).

Conclusions: The positive correlation found between relative citations and the total h-index indicated these scholars had a greater impact with studies relevant to the identified keywords compared to other general topics. The analysis also highlighted the lack of international impact of Italian research in the educational and didactic aspect of ESS, particularly for "Physical Education."

Research on Fischer-Tropsch (FT) synthetic lubricant base stock products is expected to fill a global gap in high-quality lubricants. However, the lack of identifiable characteristic functional groups in their pure hydrocarbon compositions makes it difficult to accurately analyze their compositions using existing methods. In this study, we propose a method combining nuclear magnetic resonance (NMR) and high-temperature gas-phase analysis to achieve a simple and accurate calculation of the structural information of lubricant base oils. Four structural parameters of FT lubricant base oils-namely, the average carbon number (C^*), the number of branched chain nodes (B), the degree of branching (BI), and the structural index (BC^*)-were successfully calculated using a series of empirical equations. Subsequently, we correlated the molecular structure parameters of the oils with their density, viscosity, viscosity index, and condensation point. Effective fitting equations were developed and quantitatively verified. Studies have shown that the physicochemical properties of lubricant base oils can be related to the structural parameters BC ^* or BBC ^* . BBC ^* fits better, with an R ² value of up to 0.91 or more, except for the condensation point. Density correlates well with viscosity, with a calculation error of <5%. This method of calculating the structural information of lubricant base oils can be applied to the structural determination of many hydrocarbon base oil molecules, while the simulation equations can simultaneously be used as a reference for the structure-function relationship of distillate base oils.

Introduction: This study examines how Digital Community Centers (DCCs) contribute to the empowerment of indigenous Mayan women in Northern Huehuetenango, Guatemala. Although rural and indigenous communities remain largely excluded from digitalization, the DCC model aims to narrow the digital gap by providing internet access, basic computer training, and workshops on positive masculinities.

Methods: We employed a mixed-methods approach, including 10 semi-structured focus groups and 43 surveys. The survey assessed digital literacy and gender attitudes using the GNDR-4 and GEM scales.

Results: Findings show significant improvements in women's digital skills after a short training period. These gains enabled participants to reduce travel time for tasks such as processing government documents and to launch small-scale economic initiatives. Participants who attended the positive masculinity training-both men and women-reported more equitable attitudes toward women's leadership and decision-making.

Discussion: Despite these gains, participants stressed ongoing barriers-most notably limited infrastructure, constrained financial resources, and insufficient institutional support-that hamper the long-term viability of the DCCs. They also noted a need for more detailed and standardized training on gender topics to sustain changes in attitudes over time. In conclusion, DCCs offer a promising strategy for bridging the digital divide and facilitating women's socio-economic participation, but further research with larger samples and longer follow-up periods is warranted to confirm and expand upon these initial findings.

The analysis of multi-environment trials (MET) data in plant breeding and agricultural research is inherently challenging, with conventional ANOVA-based methods exhibiting limitations as the complexity of MET experiments grows. This study presents linear mixed model-based approaches for MET data analysis. Ten MET grain yield datasets from national variety trials in Ethiopia were used. Randomized complete block (RCB) design analysis, spatial analysis, and spatial+genotype-by-environment (G × E) analysis were compared under linear mixed model framework. Spatial analysis detected significant local, global, and extraneous spatial variations, with positive spatial correlations. For the spatial + G × E analysis, increasing the order of the factor analytic (FA) models improved the explanation of G × E variance, though the optimal FA model order was dataset-dependent. Integrating spatial variability through the spatial + G × E modeling approach substantially improved genetic parameter estimates and minimized residual variability. This improvement was particularly notable in larger datasets, where the number of trials and the size of each trial played a crucial role for presence of spatial variability and strong GxE effects. Additionally, the genetic correlation heat maps and dendrograms provided intuitive insights into trial relationships, revealing patterns of strong positive, negative, and weak correlations, as well as distinct trial clusters. The results clearly demonstrate that linear mixed model-based approaches, especially the spatial + G × E analysis excel in capturing complex spatial plot variation and G × E effects in MET data by effectively integrating spatial and FA models. These insights have important implications for improving the efficiency and accuracy of MET data analysis, which is crucial for improving genetic gain estimation in plant breeding and agricultural research, ultimately accelerating the delivery of high-performing crop varieties to farmers and consumers.

[This corrects the article DOI: 10.3389/frma.2025.1531758.].

Supervisors are considered to play a pivotal role in stimulating responsible conduct of research (RCR). Their position as supervisors of PhD candidates offers the opportunity to be good role models and show young researchers how to conduct research properly. In this contribution, we delineate what it means to "lead by example." We inquire how the concept of role modeling is currently applied in the context of supervision in general, and in RCR specifically, and present the perspective of empowerment as a fruitful approach to help determine what role modeling should focus on when aiming to foster a positive research culture.