Artículos

Rural teachers have the potential to be important agents of local development. To achieve this goal, they need to acquire high digital competence in order to effectively integrate technology into their pedagogical practices, thus enriching the learning experience of students and fostering their participation. Digital competence contributes to reducing the education gap between urban and rural areas, promoting educational equity and inclusion. High digital competence also enables rural teachers to address the specific challenges of their environment, such as cultural diversity, scarce resources, and low population density. Against this backdrop, this article presents a bibliometric review of the importance of digital competence in rural teachers in Spain, where the problem of rural depopulation, as in other regions of Europe, has been accentuated in recent years. The objective of the bibliometric review is both (i) to find the strengths and weaknesses that concern researchers in relation to the digital training of teachers in rural areas and (ii) to express them explicitly in order to contribute to propose solutions. The results reveal the growing academic and political attention being paid to this issue, highlighting the need for rural teachers to acquire digital skills to adapt to current educational demands. In addition, they point to the importance of developing specific policies and programs in Europe as well as providing training opportunities and ongoing support to ensure that teachers in rural contexts can acquire or strengthen their digital competence, thereby improving the quality of education in these areas.

The bibliometrix package in R programming language, which is frequently used in bibliometric analysis, was introduced in this research. The article aimed to illustrate the various analyses applied in a bibliometric study. For this purpose, articles containing the “item response theory” (IRT) or “item response modeling” or “item response model” terms in the abstract were searched in the Thomson Reuters Clarivate Analytics Web of Science (WoS at http://www.webofknowledge.com), and bibliometric data was downloaded. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) steps were followed in the study. Data from 3388 IRT-related articles on education and psychology, searched between 2001 and 2021, were used in the study. Data were analyzed with the bibliometrix package. Some of the stages in data analysis were shared with screenshots. As a result of data analysis through the real data set, the author’s keywords related to IRT were item response model, differential item functioning, psychometrics, assessment, measurement, reliability, validity, Rasch model, and measurement invariance. The countries with the highest number of citations in IRT studies were the USA, Canada, Netherlands, United Kingdom, and China, respectively. Turkey ranked 12th in IRT studies with 434 citations. It was thought that bibliometric analysis of articles related to IRT would shed light on researchers in the field of psychometrics.

Scientific research builds on previous studies and scientifically proven knowledge. Researchers must master the recent developments in the field when designing research to answer new questions. Today, the accessibility of research literature is abundant due to digitized publications, extensive coverage of citation indexes, and several literature databases. The means for conducting systematic literature reviews have greatly improved. In recent years, a lot of research with methods, such as systematic literature reviews, literature mapping, and visualizing studies, has been published. Despite the tools having been perfected, they still have limitations. Particularly, database selection for a literature search sets a bias, because the citation indexes differ in coverage according to the scientific domain. In this paper, bibliometric data from two citation indexes (Web of Science and Scopus) are combined for the purposes of bibliometric analyses in the context of inter-firm relationships. The combination process requires a lot of unifying and repairing of data in practice that is referred as data wrangling. This study describes the process steps and the lessons learned and considers the amount of effort required.

Co-authorship networks are widely used to evaluate the quality of scientific literature productions and collaborations between researchers and institutions. We identified a gap in the literature regarding the analysis of interactions between CNPq productivity fellows. To fill this gap, we used data science to characterize co-authorship networks to obtain a complementary overview of this critical public policy for promoting excellent research in Brazil. To this end, we collected 12,345 researchers’ Lattes CVs and analyzed approximately 400,000 publications. The results showed greater collaboration in the higher strata of grants (e.g., 1A and 1B). Other research findings of interest are related to regional discrepancies and gender equity. The study contributes to a better understanding of the social dynamics of productivity grant recipients, supporting the evaluation of this relevant research promotion policy.

The number of bibliometric studies published in the scientific literature has been increasing in recent years. Some authors publish more bibliometric studies than others. The aim of this study is to (i) identify authors who focus on bibliometric studies and their publication strategy based on these studies, and to (ii) determine whether the focus of the bibliometric studies can be considered a successful publication strategy. Bibliometric analysis, including citation analysis, was used to determine the results. The Scopus database was selected as the source of bibliometric data. A total of 100 authors who frequently publish bibliometric studies were identified. For almost half of them, bibliometric studies is considered the main or significant part of their publication portfolio. A relatively small group of authors widely publish bibliometric studies. The bibliometric indicators of these authors point out that the specialization of bibliometric studies is quite successful.

Objective: This study aimed to determine various article characteristics influencing the citations and altmetric scores using papers published in a year in four high-ranking surgery journals. Material and Methods: We included all papers (n= 819 articles) published between January 2015 to December 2015 in the Annals of Surgery, British Journal of Surgery, JAMA Surgery and Journal of American College of Surgeons. Article characteristics were manually extracted. We determined citation count using the Web of Science database and used univariate analysis and negative binomial regression to determine which article characteristics affect citations and altmetric scores. Results: Mean number of citations and altmetric score received by the article were 44.6 (0-475) and 19.2 (0-665) respectively. Majority of the articles contained at least one citation (98.3%) and altmetric score (98.2%). In regression analysis, citation count was significantly associated with the journal [Annals of Surgery (IRR= 1.93), JAMA surgery (IRR= 1.76)] and non-funded research (IRR= 0.83). The altmetric score was significantly associated with the country of the corresponding author (US) (IRR= 1.3), study subtopic, journal [JAMA surgery (IRR= 2.33)], non-funded (IRR= 0.74) and non-open-access publication (IRR= 0.44). Conclusion: Article metrics were found to be associated with specific study subtopics, country of the corresponding author, funding, open-access publication and the journal. These results might help editors, reviewers and authors to produce, review and publish more impactful studies. A similar study in the future may help to better understand the changing dynamics of academic publishing.

The purpose of the study is a critical analysis of the Russian practice of using the main indicators of sci entometrics in management activities at various levels. To achieve this goal, it was necessary to solve several problems, namely: to study the main scientometric parameters; determine the degree of accuracy and reliability of information indexed by the major bibliographic databases; to formulate a number of principles of management practice related to scientometrics; to identify and consider the shortcomings of management decisions recorded in regulatory documents; to develop recommendations aimed at resolving a number of problems related to the use of scientometric indicators. The research is based on both general scientific theoretical methods: induction and deduction, analysis and synthesis, systems approach, social modeling, comparative typological and comparative analytical methods, and practical methods: working with documents, analysis of printed and electronic sources of information, content analysis and bibliographic combination of documents. In the course of the work, it was revealed that scientometrics in its applied version still clearly lacks reliability both due to imperfections associated with both its main indicators in all databases and with the indexing of publications in them, as well as due to management miscalculations reflected in regulations. In particular, in the light of the current political events caused by the special military operation in Ukraine, and in connection with difficult access to Western bibliographic data bases, the problem of a radical reorganization of the Russian Science Citation Index (RSCI) and the transition to a new system of scientometric indicators clearly arises. The data and recommendations obtained as a result of the study will help management structures avoid obvious omissions and errors in planning and monitoring the scientific activities of university and academic research institute staff, as well as optimize scientometric reporting.

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.

In “low-cost” solutions, ensuring economic accessibility and democratizing the availability of emerging technologies stand as pivotal considerations. This study undertakes a systematic literature review of low-cost 3D mapping solutions. Leveraging SCOPUS as the primary database, a comprehensive bibliometric analysis encompassing 1380 publications was conducted, subsequently narrowing the focus to 87 recent publications for detailed review. This research endeavors to delineate the defining characteristics of low-cost systems, elucidate their principal applications and preferred platforms, assess accessibility level, gauge the extent of innovation in both hardware and software development, explore the contributions of Deep Learning and data fusion, evaluate the consideration of data quality, and examine the contemporary relevance of photogrammetry within low-cost context. The findings demonstrate that many authors subjectively use the term low-cost to highlight qualities of a technology, methodology or sensor, but challenges arise from data quality comparisons with high-cost systems.

The article presents the results of an information review of special libraries of higher education institutions, members of the network of educational libraries of the Ministry of Education and Science of Ukraine and the National Academy of Sciences of Ukraine regarding the use of bibliometric methods in their activities and the provision of bibliometric services presented on the library websites. A sample of fourteen special libraries of higher education institutions of Ukraine, which were included in the webometric ranking of the world’s universities in the version of Webometrics Ranking of World’s Universities January 2024, was formed. Quantitative monitoring of the sites of special libraries was carried out according to the following parameters of information services using bibliometric methods: bibliographic, organizational, methodical, scientific and information services. The content and thematic load was established for the main areas of information services and methods used in the work of libraries: organization of access to bibliographic and scientometric databases by means of citation and citation management; creation and adjustment of ORCID author profiles, development of methodological recommendations for their filling; organization of bibliometric data search on the topic of scientific research in the institutional repository; development of recommendations regarding the use of bibliometric resources and bibliometric indicators. It was established that the use of bibliometric services is expanding and deepening in the conditions of the progress of European integration of education and pedagogical science of Ukraine. Prospects for further scientific research in the researched direction are predicted.