Frontiers in research metrics and analytics最新文献

{"title":"Topic analysis on publications and patents toward fully automated translational science benefits model impact extraction.","authors":"Tejaswini Manjunath, Eline Appelmans, Sinem Balta, Dominick DiMercurio, Claudia Avalos, Karen Stark","doi":"10.3389/frma.2025.1596687","DOIUrl":"10.3389/frma.2025.1596687","url":null,"abstract":"<p><strong>Background: </strong>The Clinical and Translational Science Award (CTSA) program, funded by the National Center for Advancing Translational Sciences (NCATS), has supported over 65 hubs, generating 118,490 publications from 2006 to 2021. Measuring the impact of these outputs remains challenging, as traditional bibliometric methods fail to capture patents, policy contributions, and clinical implementation. The Translational Science Benefits Model (TSBM) provides a structured framework for assessing clinical, community, economic, and policy benefits, but its manual application is resource-intensive. Advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) offer a scalable solution for automating benefit extraction from large research datasets.</p><p><strong>Objective: </strong>This study presents an NLP-driven pipeline that automates the extraction of TSBM benefits from research outputs using Latent Dirichlet Allocation (LDA) topic modeling to enable efficient, scalable, and reproducible impact analysis. The application of NLP allows the discovery of topics and benefits to emerge from the very large corpus of CTSA documents without requiring directed searches or preconceived benefits for data mining.</p><p><strong>Methods: </strong>We applied LDA topic modeling to publications, patents, and grants and mapped the topics to TSBM benefits using subject matter expert (SME) validation. Impact visualizations, including heatmaps and t-SNE plots, highlighted benefit distributions across the corpus and CTSA hubs.</p><p><strong>Results: </strong>Spanning CTSA hub grants awarded from 2006 to 2023, our analysis corpus comprised 1,296 projects, 127,958 publications and 352 patents. Applying our NLP-driven pipeline to deduplicated data, we found that clinical and community benefits were the most frequently extracted benefits from publications and projects, reflecting the patient-centered and community-driven nature of CTSA research. Economic and policy benefits were less frequently identified, prompting the inclusion of patent data to better capture commercialization impacts. The Publications LDA Model proved the most effective for benefit extraction for publications and projects. All patents were automatically tagged as economic benefits, given their intrinsic focus on commercialization and in accordance with TSBM guidelines.</p><p><strong>Conclusion: </strong>Automated NLP-driven benefit extraction enabled a data-driven approach to applying the TSBM at the scale of the entire CTSA program outputs.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1596687"},"PeriodicalIF":1.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research funding challenges in Brazil: researchers' perceptions from a public institution of professional education.","authors":"Cristhian Chagas Ribeiro, Woska Pires da Costa, Marcos de Moraes Sousa, Priscilla Rayanne E Silva, Vicente Miñana-Signes, Matias Noll","doi":"10.3389/frma.2025.1553928","DOIUrl":"10.3389/frma.2025.1553928","url":null,"abstract":"<p><strong>Introduction: </strong>In a global landscape characterized by intense competition and stringent funding criteria, researchers face the dual challenges of limited resources and high demand for innovation-a challenge that Brazil is no exception to. This study aimed to explore the perceptions, barriers, and challenges faced by researchers during the project submission process for approval by funding agencies, with a focus on schools within the Federal Network of Professional, Scientific, and Technological Education Institutions.</p><p><strong>Methods: </strong>A quantitative cross-sectional approach was used to examine the characteristics of researchers at a Brazilian institution in 2023. The sample comprised eighty three researchers who completed an online questionnaire containing eighty three questions on demographic characteristics, factors associated with project submission and approval, and reasons for non-submission or non-approval. The data were analyzed using descriptive statistics, including the Kolmogorov-Smirnov, Pearson's chi-square, and Mann-Whitney <i>U</i>-tests, followed by <i>post hoc</i> analysis and Yates' correction. Logistic regression was applied using the backward elimination method, and significant parameters (<i>p</i> < 0.20) free from multicollinearity were selected.</p><p><strong>Results: </strong>This study revealed that most researchers were men (61.4%) with doctoral degrees (91.6%), highlighted the critical role of proposal clarity and relevance in the project evaluation process. Gender (<i>p</i> = 0.011) and academic level (<i>p</i> = 0.025) were significant factors influencing project submission rates, with Brazilian National Council for Scientific and Technological Development (<i>CNPq</i>) fellows and researchers involved in graduate programs submitting more projects. The participants identified \"search for funding\" and \"desire to expand research impact\" as their primary motivations while citing \"complex funding calls\" and \"funding limitations\" as major barriers. Additionally, age and the number of children were found to affect project approval (<i>p</i> ≤ 0.018), with \"proposal clarity\" and \"researchers' experience\" having been critical factors for submission approval (<i>p</i> ≤ 0.03).</p><p><strong>Conclusion: </strong>The study results highlighted a gender disparity, with lower participation among women, and identified key factors influencing project submission, including the search for funding, curriculum development, and structural challenges. Additionally, the findings suggest the adoption of gender-sensitive and early-career grant criteria, targeted support for underrepresented researchers, and flexible mechanisms for those with caregiving responsibilities. These findings underscore the importance of public policies and institutional strategies in promoting equitable and inclusive funding opportunities.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1553928"},"PeriodicalIF":1.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics and challenges of technology transfer in Colombia: a systematic literature review.","authors":"Alejandro Valencia-Arias, Karla Cristina Bonilla Restrepo, Eliana Villa-Enciso, Jackeline Valencia, Juan Camilo Rua Hernandez, Diana Marleny Ramírez-Ramírez","doi":"10.3389/frma.2025.1628141","DOIUrl":"10.3389/frma.2025.1628141","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1628141"},"PeriodicalIF":1.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The online survey in qualitative research: can AI act as a probing tool?","authors":"Ryan Thomas Williams, Ewan Ingleby","doi":"10.3389/frma.2025.1519008","DOIUrl":"10.3389/frma.2025.1519008","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1519008"},"PeriodicalIF":1.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Evaluating supervision and research leadership in promoting responsible research.","authors":"Tamarinde Haven, Dena Plemmons, De-Ming Chau","doi":"10.3389/frma.2025.1670586","DOIUrl":"https://doi.org/10.3389/frma.2025.1670586","url":null,"abstract":"","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1670586"},"PeriodicalIF":1.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for clustering for self-reported outcomes in the design and analysis of population-based surveys: A case study of estimation of prevalence of epilepsy in Nairobi, Kenya.","authors":"Daniel M Mwanga, Isaac C Kipchirchir, George O Muhua, Charles R Newton, Damazo T Kadengye","doi":"10.3389/frma.2025.1583476","DOIUrl":"10.3389/frma.2025.1583476","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1583476"},"PeriodicalIF":1.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12433999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct domains: a call for nuance in the categorization and evaluation of \"Arts and Humanities\" disciplines.","authors":"William E Savage, Richard Wheeler, Anthony J Olejniczak","doi":"10.3389/frma.2025.1661966","DOIUrl":"10.3389/frma.2025.1661966","url":null,"abstract":"","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1661966"},"PeriodicalIF":1.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12426151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of the cyberinfrastructure production function model to R1 institutions.","authors":"Preston M Smith, Jill Gemmill, David Y Hancock, Brian W O'Shea, Winona Snapp-Childs, James Wilgenbusch","doi":"10.3389/frma.2025.1449996","DOIUrl":"10.3389/frma.2025.1449996","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1449996"},"PeriodicalIF":1.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12408599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do women and men differ in research collaborations based on authorship positions? The Spanish case.","authors":"Fernanda Morillo, Manuel Escabias, Zaida Chinchilla-Rodríguez","doi":"10.3389/frma.2025.1631931","DOIUrl":"10.3389/frma.2025.1631931","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1631931"},"PeriodicalIF":1.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of knowledge management practices on librarians' competency in artificial intelligence in tertiary institutions of Lokoja, Kogi State, Nigeria.","authors":"Sherif Kunle Yusuf, Abdullateef B Oshinaike, Ismail O Suleiman, Yinka Martins Omoniyi","doi":"10.3389/frma.2025.1623278","DOIUrl":"10.3389/frma.2025.1623278","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>R</i> = 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).</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"10 ","pages":"1623278"},"PeriodicalIF":1.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12375557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}