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Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granularity and readability. Each partition is labeled using an R(p, q) notation, where p and q indicate the position along each axis. Two perspectives are supported: the absolute mode, based on raw values (e.g., “very short, narrow”), and the relative mode, based on min–max normalization (e.g., “short relative to population”). I propose a set of transformation metrics—density, net flow, relative change ratio, and redistribution index—to quantify how data structures change between modes. The framework is demonstrated using both the Iris dataset and a subset of the airquality dataset, showing how RCF captures clustering behavior, reveals outlier effects, and exposes normalization-induced redistributions. Full article

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body of literature in this domain necessitates consolidation to provide a comprehensive understanding of their applications in multidimensional impact assessment. This study bridges this gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, and advanced topic modelling using BERTopic, to analyse a curated corpus of 1608 scholarly articles. Guided by three core research questions, this study investigates how ML and AI enhance research impact evaluation, identifies dominant methodologies, and outlines future research directions. The findings underscore the transformative potential of ML and AI to augment traditional bibliometric indicators by uncovering latent patterns in collaboration networks, institutional influence, and knowledge dissemination. In particular, the scalability and semantic depth of BERTopic in thematic extraction, combined with the visualisation capabilities of tools such as CiteSpace and VOSviewer, provide novel insights into the dynamic interplay of scholarly contributions across dimensions. Theoretically, this research extends the scientometric discourse by integrating advanced computational techniques and reconfiguring established paradigms for assessing research contributions. Practically, it provides actionable insights for researchers, institutions, and policymakers, enabling enhanced strategic decision-making and visibility of impactful research. By proposing a robust, data-driven framework, this study lays the groundwork for holistic and equitable research impact evaluation, addressing its academic, societal, and economic dimensions. Full article

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape. Full article

The agri-food sector is expanding, driven by growing global demand. At the same time, it faces the challenge of increasing its efficiency and adopting sustainable practices. This study aimed to map scientific production in this field, identifying trends, emerging themes, critical gaps, and future directions for research. A bibliometric analysis was conducted with 5141 papers published between 1977 and 2024, extracted from the Scopus and Web of Science databases. We applied keyword co-occurrence analysis, thematic analysis, thematic evolution, and three-field graphs using the metrics betweenness centrality, closeness centrality, and PageRank. The results revealed a significant growth in publications in the agri-food sector, especially after 2012, emphasizing the high centrality and relevance of themes such as sustainability, agri-food, and agriculture. Topics such as bioactive compounds, blockchain, and traceability were identified as areas of growing interest, and the circular economy stood out as an emerging topic. Italy, Spain, and France lead in scientific production and international collaboration. The most prominent journals were Sustainability, the Journal of Cleaner Production, and Agriculture and Human Values. Research in the sector is expanding, focusing on sustainability, the circular economy, and bioactive compounds. International collaborations and high-impact journals are pillars for advances in the sector. Full article

Background: The literature has addressed the negative impact of poor neonatal conditions (NCs) across regions. This has drawn attention to the need to improve NCs, particularly in Africa. NCs research can make an important contribution. However, there is no study dedicated to this topic in Africa. A bibliometric analysis of NCs research can assist scientists in planning ongoing and new NCs research and support those involved in developing and implementing strategies to combat poor NCs. Methods: This study used discipline-specific terms to identify articles on NCs published between 2000 and 2019 and indexed in the Web of Science Core Collection (WoS) with at least one African author. A bibliometric analysis was applied to determine the volume, visibility, topics, and collaboration activities related to NCs research. Results: The results show that knowledge on NCs increased between 2000 and 2019; NCs research is concentrated in a few African countries (Egypt, South Africa, Nigeria, Tanzania, and Kenya), and its visibility is below the world average. In general, maternal mortality is the most researched topic and collaborative activities are frequent, mainly international research collaboration (IRC), with the United States of America (USA) and the United Kingdom (UK) being the main partners (they participated in 57% and 28% of all articles with IRC). The collaboration networks are fragile as 43–67% of all links represent one article in 20 years. Conclusions: Ongoing and new NCs research in Africa should consider the main African players and their partners as important sources of knowledge. There is a need to implement strategies to increase NC knowledge in other African countries, expand and strengthen collaboration networks, and diversify the sources of knowledge. Full article

The digital transformation of museums has elevated their websites from mere informational tools to dynamic platforms that foster cultural engagement, inclusivity, and preservation. This study evaluates the performance of 234 museum websites worldwide, focusing on critical dimensions such as accessibility, usability, SEO, and speed. By employing a comprehensive diagnostic framework of evaluation metrics, the research reveals disparities between mobile and desktop versions, highlights regional variations, and identifies key performance drivers. Generally, desktop sites outperform their mobile counterparts, underscoring the necessity for tailored optimization strategies that strike a balance between fast-loading, visually stable mobile pages and content-rich desktop experiences. A key contribution of this study is the development of an easy-to-adopt and inclusive evaluation framework that unites fragmented approaches, enabling museums of all sizes to enhance their digital presence. Furthermore, the research provides actionable insights for administrators, particularly those in resource-constrained institutions, through a cost-free, user-friendly toolkit that simplifies technical metrics and promotes internal staff capacity building in digital analytics. Ultimately, the findings help empower museums to bridge digital performance gaps while ensuring they continue to function as vibrant cultural hubs in a rapidly changing digital landscape. Full article

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. Full article

As science has expanded in size and recognition as the dominant driver of innovation and economic growth, the need for studies to analyze and characterize the contributions made by research in each field of knowledge and related research areas from a critical standpoint has become increasingly prominent in international journals [...] Full article

This study investigates the diversity and evolution of research topics within the dental sciences from 1994 to 2023, using Topic modeling and Shannon’s entropy as a measure of research diversity. We analyzed a dataset of 412,036 scientific articles across six dental disciplines: Orthodontics, Prosthodontics, Periodontics, Implant Dentistry, Oral Surgery, and Restorative Dentistry. This research relies on BERTopic to identify distinct topics within each field. The study revealed significant shifts in research focus over time, with some disciplines exhibiting robust growth in article numbers, such as Periodontics and Prosthodontics. However, despite the overall increase in publications, the number of topics per discipline varied, with Restorative Dentistry increasing at a faster rate and exceeding 50 topics over the last 15 years. We observed an increasing diversification of research efforts in disciplines such as Restorative Dentistry, with entropy levels consistently above 2 and progressively increasing. In contrast, fields such as Prosthodontics, despite high publication output, maintained a more specialized research focus, reflected in entropy levels remaining below 1.5. Oral Surgery showed a steep increase in research diversification until 2000, after which it stabilized. Taken together, our findings describe the dynamic nature of dental research and highlight the balance shifts in research focus across several key areas of Dentistry. Full article

Systematic reviews are a powerful tool to summarize the existing evidence in medical literature. However, identifying relevant articles is difficult, and this typically involves structured searches with keyword-based strategies, followed by the painstaking manual selection of relevant evidence. A.I. may help investigators, for example, through topic modeling, i.e., algorithms that can understand the content of a text. We applied BERTopic, a transformer-based topic-modeling algorithm, to two datasets consisting of 6137 and 5309 articles, respectively, used in recently published systematic reviews on peri-implantitis and bone regeneration. We extracted the title of each article, encoded it into embeddings, and input it into BERTopic, which then rapidly identified 14 and 22 topic clusters, respectively, and it automatically created labels describing the content of these groups based on their semantics. For both datasets, BERTopic uncovered a variable number of articles unrelated to the query, which accounted for up to 30% of the dataset—achieving a sensitivity of up to 0.79 and a specificity of at least 0.99. These articles could have been discarded from the screening, reducing the workload of investigators. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature. Full article

As the number of publications is quickly growing in any area of science, the need to efficiently find relevant information amidst a large number of similarly themed articles becomes very important. Semantic searching through text documents has the potential to overcome the limits of keyword-based searches, especially since the introduction of attention-based transformers, which can capture contextual nuances of meaning in single words, sentences, or whole documents. The deployment of these computational tools has been made simpler and accessible to investigators in every field of research thanks to a growing number of dedicated libraries, but knowledge of how meaning representation strategies work is crucial to making the most out of these instruments. The present work aims at introducing the technical evolution of the meaning representation systems, from vectors to embeddings and transformers tailored to life science investigators with no previous knowledge of natural language processing. Full article