Frontiers in research metrics and analytics最新文献

Introduction: Technology transfer in Colombia plays a critical role in promoting innovation and regional development. However, its implementation continues to face significant institutional, economic, and cultural challenges that hinder its effectiveness and sustainability.

Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. The databases Scopus and Web of Science were used to identify peer-reviewed articles that addressed knowledge and technology transfer within the Colombian context.

Results: The analysis revealed multiple barriers: institutional fragmentation, inadequate investment in R&D, lack of incentives for collaboration, and weak trust between academia and industry. Additionally, organizational rigidity and the limited adaptation of international transfer models to local contexts were identified as factors that reduce the effectiveness of these processes.

Discussion: The findings highlight the need to acknowledge regional particularities, foster inclusive governance, and promote horizontal relationships among stakeholders. Effective international strategies, such as open innovation, co-creation models, and hybrid technology transfer offices, could be adapted to the Colombian context. Strengthening institutional capacities, increasing R&D funding, and aligning innovation with social and environmental objectives are imperative to build a more robust and inclusive innovation ecosystem.

Surveys are commonly associated with quantitative methods, yet there is growing recognition of their potential to yield qualitative insights into complex social phenomena. However, the effectiveness of open-ended survey questions is often limited by issues such as respondent fatigue and low-quality responses. To address these limitations, researchers are increasingly exploring the use of artificial intelligence (AI) to support dynamic survey design, probing questions, and participant engagement. This article explores the role of qualitative surveys in social science research, by considering their alignment with qualitative paradigms. The content assesses how AI-powered features, such as machine learning and chatbot-driven interfaces, can enhance data collection through adaptive questioning. The article also discusses key challenges related to data quality, participant inclusivity, and ethical considerations. Particular attention is given to the concept of "felt anonymity" in online surveys, which can encourage candid disclosures on sensitive topics and broaden participation across diverse populations. When designed with ethical and methodological care, qualitative surveys can thus serve as powerful tools for accessing underrepresented perspectives. By integrating AI into qualitative survey design, researchers can enhance both the richness and reach of their data. This article argues that AI-powered qualitative surveys, especially those capable of dynamic probing, offer a promising hybrid approach, bridging the scalability of surveys with the responsiveness of interviews, and calls for further empirical study of their ethical and epistemological implications.

Population-based surveys are common for estimation of important public health metrics such as prevalence. Often, survey data tend to have a hierarchical structure where households are clustered within villages or sites and interviewers are assigned specific locations to conduct the survey. Self-reported outcomes such as diagnosis of diseases like epilepsy present more complex structure, where interviewer or physician-related effects may bias the results. Standard estimation techniques that ignore clustering may lead to underestimated standard errors and overconfident inferences. In this paper, we examine these effects for estimation of prevalence of epilepsy in a two-stage population-based survey in Nairobi and we discuss how clustering can be taken into account in design and analysis of population-based prevalence studies. We used data from the Epilepsy Pathway Innovation in Africa project conducted in Nairobi and simulated attrition levels at 10% and 20% assuming missing at random (MAR) mechanism. Attrition was accounted for using sequential k-nearest neighbor method. We adjusted the expected prevalence based on clustering at multiple levels, such as site, interviewer and household using a random effects model. Intraclass correlation (ICC) > 0.1 indicated presence of substantial clustering. We report point estimates with 95% confidence interval (CI). Crude prevalence of epilepsy was 9.40 cases per 1,000 people (95% CI: 8.60-10.20). There was substantial clustering at household level (ICC = 0.397), interviewer level (ICC = 0.101) and site level (ICC = 0.070). Prevalence adjusted for clustering at household, interviewer and site was 9.15/1,000 (95% CI 7.11-11.20). Overall, not accounting for clustering was associated with underestimation of standard errors. Not accounting for attrition on the other hand led to underestimation of prevalence. Imputation of the missing data due to attrition mitigated the attrition bias under appropriate assumptions. Accounting for clustering, particularly household, interviewer and site levels, is critical for valid estimation of standard errors in population-based surveys. Rigorous training and pre-survey testing can minimize measurement error in self-reported outcomes. Attrition can lead to underestimation of prevalence if not properly addressed. Attrition bias can be minimized by conducting targeted mobilization of participants to improve response rates and using statistical methods such as multiple imputation or machine learning-based imputation methods to address it.

High-performance computing (HPC) is widely used in higher education for modeling, simulation, and AI applications. A critical piece of infrastructure with which to secure funding, attract and retain faculty, and teach students, supercomputers come with high capital and operating costs that must be considered against other competing priorities. This study applies the concepts of the production function model from economics with two thrusts: (1) to evaluate if previous research on building a model for quantifying the value of investment in research computing is generalizable to a wider set of universities, and (2) to define a model with which to capacity plan HPC investment, based on institutional production-inverting the production function. We show that the production function model does appear to generalize, showing positive institutional returns from the investment in computing resources and staff. We do, however, find that the relative relationships between model inputs and outputs vary across institutions, which can often be attributed to understandable institution-specific factors.

This study examines gender disparities in authorship and collaboration within the Spanish scientific workforce, focusing on international and industry co-authored publications. Drawing on a comprehensive dataset of over 165,000 publications and more than 170,000 identified authors affiliated with Spanish institutions, the analysis explores how gender interacts with authorship position, research field, career stage, and team size. The results reveal a consistent under-representation of women in both types of collaboration, particularly in key authorship roles (first, last, and corresponding author). While women are more active at early career stages, their visibility in leadership roles tends to diminish over time, especially as the number of co-authors increases. Field-specific patterns show that even in highly feminized disciplines, such as Biomedical & Health Sciences, women are less likely to appear in prominent authorship positions. These findings raise important concerns about current research assessment practices that rely heavily on byline position as a proxy for contribution or leadership. The study contributes to ongoing discussions on responsible metrics and proposes policy recommendations to promote more equitable evaluation systems that reflect the collaborative and diverse nature of research careers.

Introduction: The integration of Artificial Intelligence (AI) in library services has highlighted the need for librarians to acquire relevant competencies. Knowledge Management Practices (KMP) including knowledge acquisition, organization, sharing, and application play a pivotal role in enhancing librarians' AI capabilities. However, in Lokoja, Kogi State, Nigeria, librarians face challenges in adopting these practices effectively, resulting in skill gaps that affect academic service delivery.

Methods: This study employed a quantitative survey design and utilized census sampling to include all 28 professional librarians from three tertiary institutions in Lokoja. Data were collected through a structured questionnaire focusing on current KM practices, AI competencies, and associated challenges. Descriptive statistics and multiple regression analyses were performed using SPSS version 25, with a significance threshold set at 0.05.

Results: The findings revealed that knowledge sharing (100%), digital repositories (92%), and taxonomy development (82%) were the most commonly adopted KM practices. Regression analysis demonstrated a significant positive relationship (R = 0.78; R² = 0.61) between KM practices and librarians' AI competencies. Among the predictors, knowledge sharing had the strongest influence (β = 0.41). Key challenges identified include technical issues (mean = 3.00), lack of training (2.89), and insufficient managerial support (2.89).

Discussion: The results confirm that KMP significantly enhance librarians' competency in managing AI-generated information and supporting users. However, limited proficiency in technical AI domains such as machine learning and natural language processing indicates a need for specialized training. The study underscores the necessity of investing in infrastructure, continuous professional development, and strategic leadership support to maximize the benefits of KMP in AI integration.

Introduction: This research explores the practices of current research groups, where multidisciplinary groups stand out for their relevance in developing science. The objective is to comprehensively explore the practices and values of research groups through a three phase study.

Methods: In the first phase a systematic review was performed, in the second phase, a hermeneutic analysis focused on values, knowledge and the social elements to describe and delve into the development of scientific practices. The third phase, a discussion of these practices and interactions was proposed.

Results: The most relevant findings are the specific attitudes valued in social interactions, such as the relationship between cooperation and solidarity, economic values like the link between benefit and profitability, and research values like the connection between inclusion and coherence.

Discussion: These relationships help define the value networks that shape interactions, practices and relationships within scientific groups. This describes the dynamic of contemporary science to answer today's challenges.

The study aims to explore the motivations and challenges affecting the utilization of Academic Social Networks among Arab mass communication researchers. Identifying the impression management strategies and practices among them and highlighting the obstacles these researchers face. An online survey was applied, collecting a convenience sample consisting of 100 respondents from six Arab countries. Countries include Egypt, Saudi Arabia, the United Arab Emirates, Iraq, Palestine, and Morocco. The study Findings showed no statistically significant differences in the time spent on ASNs based on gender. However, the analysis indicated differences in service usage between males and females. The findings also revealed significant variations in impression management practices based on gender, which highlight concerns regarding gender equality among Arab researchers. The study's findings identified several obstacles that prevent Arab researchers in Mass Communication from fully utilizing academic social networks (ASNs). The respondents cited plagiarism as the most significant concern, with 39%. Findings highlight the need for further studies to explore these barriers. The study also recommends conducting more research studies to compare the influence of disciplines on ASN practices, investigate how the area of specialization impacts research practices by comparing theoretical and applied specializations, and examine how specialization affects the use of ASNs, allowing for a comparison of differences among researchers in the sciences, arts, and humanities.