Frontiers in research metrics and analytics最新文献

Those who lead research teams have myriad roles and responsibilities that are pivotal to both producing rigorous and responsible scientific work and creating a supportive research environment that cultivates this work. We begin by presenting a leadership, management, and mentoring (LMM) framework focused on three critical roles researchers must play that have direct impact on the scientific work, the work environment, and research team dynamics: the role of research leader, research manager, and research mentor. Research leadership involves fostering a healthy research culture by building relationships where team members feel respected and supported. Research management involves providing oversight and direction of day-to-day operations to ensure tasks are done effectively, rigorously, and responsibly. Research mentoring involves providing opportunities and support to team members so that they develop professionally and build their careers. While these three roles are distinct, there is overlap in the professional, interpersonal, and intrapersonal skills that underlie their effective performance, such as communication, active listening, emotion management, and self-reflection. We also draw attention to some of the challenges when performing LMM roles. A variety of sources and types of evaluation measures may be used to comprehensively assess the functioning of a research team and its leader(s). We illustrate key domains for measurement, example indicators of effectiveness in those domains, and examples of the types of measures that could be used for evaluation. We discuss how top-down evaluation, bottom-up evaluation, and self-evaluation methods could be employed for data collection and note that each of these methods has strengths and limitations. We recommend multiple sources and types of data but acknowledge that evaluation must be feasible and practical. We note best practices and key implementation considerations for each method of measurement. When combined, these three methods provide a robust approach for evaluating LMM. We conclude with a description of key considerations for supporting the evaluation and application of LMM in real-world settings at academic institutions. Such considerations include senior leadership buy-in and communication about LMM expectations and providing appropriate framing, time, support, and incentives for LMM. We also highlight institutional risk factors that may inadvertently undermine LMM goals.

Purpose: Supervision and leadership are regarded to have a major role in promoting responsible research. Various approaches to training for supervisors and leaders have been proposed. However, little is known about what works best, what kind of hurdles are faced in implementation and engagement, and what methods of assessing the effectiveness of training programs are available. Through exploring these points, this research aims to propose a roadmap to good practice for training supervisors and leadership.

Design: A virtual marketplace for exchanging current practices and approaches for training supervisors and leadership took place in March 2024. Twenty-two policy makers from thirteen European countries, supervisors and senior research leaders were selected to participate, using opportunistic and purposive sampling. Facilitated using the Gather platform, the marketplace commenced with a non-European keynote speaker on training supervisors and leadership. Three main questions were brought forward for discussion separately-What works well for successful implementation? What are the challenges? How do we assess effectiveness? After the keynote presentation, marketplace participants rotated in groups between three market stalls to share thoughts on good practices for training supervisors and leadership framed around the three questions. Moderators for each of the stalls recorded detailed field notes to inform the study findings.

Findings: During the exchange, mandatory training, especially when tailored to specific disciplines and conducted in small groups using a problem-based learning approach, was deemed effective. Awareness of power imbalances between early career researchers, supervisors, and leaders were to the fore. Critical challenges included a need for senior supervisors and leaders to participate and support research training. Also a need for systemic processes, tailored to specific local settings to avoid ad hoc implementation of policies, procedures and training. In assessing effectiveness there was an emphasis to share more research data and to utilize incidents of breaches of research integrity. The latter to be leveraged for learning purposes and transparency around the investigative process.

Originality: There are multiple facets to good practice for training supervisors and leadership, along with a multitude of practices, however there is little evidence of practices that work, challenges around implementation, and assessing effectiveness.

Scientifically well-established methods do not automatically get used in regulations. Even when there is an urgent need for regulatory relevant test methods, methods need to pass through a so-called standardization process. This involves following specific agreed processes, which define the timeline and requirements (e.g., validation, documentation, approval) before the method can be integrated in regulatory oriented standards or Test Guidelines from the Organization of Economic Cooperation and Development (OECD). The process is often seen as too complex or too resource (and time) consuming by the scientific community, which inhibits method developers from translating their scientific methods and protocols into standards or OECD Test Guidelines. Numerous incentives exist for scientists to be (more) active in the standardization process and allow regulation to keep up with new scientific developments. These include an increase in research impacts, an expansion and diversification of the international expert network, and an access to more fundings. This paper shows scientists how to reach such outcomes, by providing guidance on how to navigate successfully through the standards and OECD Test Guidelines development processes. Especially the requirements for method validation, which is a prerequisite in this process and common across the different standardization bodies. For further details and insights, readers are invited to consult the various freely available resources generated by the NanoHarmony EU project. These are compiled in the OECD Test Guideline Process Mentor (https://testguideline-development.org/). The active participation of scientists along the entire process toward standards and OECD Test Guidelines is key. Only then can their methods be expanded into a wider, regulatory application toward a safer world.

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.