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INTRODUCTION
In the modern academic and scientific innovation era, higher education institutions serve as hubs for knowledge creation and dissemination. Among this King Faisal University (KFU) has emerged as a significant contributor to global research output, particularly in the Gulf region. Established in 1975 in Al-Ahsa, Saudi Arabia, KFU is renowned for its dedication to academic excellence, community engagement, and scientific discovery. Over the years, the university has diversified its academic portfolio, encompassing fields such as medicine, agriculture, engineering, humanities, and natural science, thereby forecasting a vibrant research culture. Scientometric analysis provides a quantitative lens to evaluate and understand an institution's research productivity, impact, and collaboration patterns. It offers insights into the evolution of academic contributions, identifies key strengths, and highlights opportunities for further growth (Moussa and Lo, 2016). This study seeks to provide a comprehensive overview of King Faisal University’s research output, analysing various metrics such as publication trends, citation patterns, international collaborations, and disciplinary focus. The research landscape of King Faisal University’s publications spans a wide range of disciplines, particularly on sustainability, healthcare, environmental science, and advanced materials. This diversity underscores KFU’s alignment with Saudi Arabia’s Vision 2030, prioritizing innovation, education, and sustainable development as key pillars of national progress (Ibrahim et al., 2009). The scientometric study examines a dataset spanning 1989 to 2025, offering a longitudinal view of KFU’s research performance. Matrices such as the annual growth rate of publications, average citations per document, and international co-authorship provide valuable insights into the university’s academic impact and collaborative ethos. By analysing key indicators such as the most frequently cited journals, prolific research areas, and trends in collaborative research, this study aims to identify the driving forces behind KFU’s academic achievements and its role in the global research ecosystem. In addition to providing a snapshot of KFU’s research productivity, this analysis aims to serve as a strategic tool for policymakers, administrators, and researchers. It identifies areas of strength that can be further leveraged and gaps that may require targeted interventions. Furthermore, the study highlights the significance of international collaborations, as reflected in KFU’s robust percentage of Co-authored works with researchers worldwide.
Through this scientometric exploration, King Faisal University's research landscape: A Scientometric analysis seeks to celebrate the university's achievements, recognize its contributions to global knowledge, and inspire a roadmap for its future research endeavors (Albohnayh et al., 2024). The findings of this study not only underscore the institution's role as a leader in academic excellence and provide a framework for continuous improvement in its research landscape.
MATERIALS AND METHODS
For the present study, the King Faisal University Research publications were retrieved and downloaded from the Web of Science (https://clarivate.com) database from 1989 to 2025. A search strategy was formulated, where the advanced search with the keyword “King Faisal University” and the search output fined by period using data range tad and was further refined with existing provisions in Web of Science database to generate kinds of publications statistics presented in the study. Citations to publications were counted from data of their publication till 12 January 2025. The study analyzed the publications data across a series of raw and relative biblipmetric studies. Bibliometrics, often known as “scientometrics”, is a quantitative tool for measuring and mapping existing research in scientific areas. A bibliometric analysis is a study that aims to uncover the fundamental structure of a field’s research. It analyses research trends in depth, evaluates science as a productive knowledge system, and is trustworthy and an associated tool with an R package, was created in response to the perception that scientific analysis is difficult and unclear (Anon n.d.; Donthu et al., 2021; Rajendran et al., 2023). The Bibliometrix software allows researchers to import data from the Web of Science citation database, as well as perform bibliometric analysis. It also has data metrics for author and country collaboration analysis, word analysis, author productivity analysis, and author collaborations. Limited research has been conducted to explain the evolution of various scientific subjects around data analytics identify emerging trends, and assess the performance and influence of countries, regions, scholars, and research organisations in specific fields of science (Donthu et al., 2021; Liu, 2013; Rajendran et al., 2023) Conducted a study on data analytics using Bibliometrix R tool. The results are to provide policymakers, researchers, and people in agriculture with a comprehensive understanding of Agriculture Big data research for future research (Esfahani, Tavasoli, and Jabbarzadeh 2019) also conducted a bibliometric analysis to look into the current state and progress of scientific studies on the impact of social network users. In another bibliometric study (Dervis 2019), total publications, total citations, and citations per paper are among the performance measures calculated.
Objectives
The present study examines the King Faisal University research output from 1989 to 2025, with the following objectives: (i) To study the annual average growth rate, citations impact, and collaboration features of King Faisal University publications, (ii) To study the most productive and impactful authors, (iii) To study the most relevance sources, Most collaborative organizations and country collaborations. (iv) To study the Trend topics, three filed analysis, Broadford law, and Occurrence of most frequent keywords
RESULTS
The dataset spans 36 years (1989-2025), documenting 10,482 research outputs from 2,472 sources with an annual growth rate of 0.91%. The average document age is 7.06 years, with 13.83 citations per document, indicating a moderate impact. A total of 24,996 authors contributed, with 73.22% of publications involving international collaborations and an average of 5.85 co-authors per document. Single-authored papers are relatively rare, comprising 10.8% of the total. The predominant document type of articles (9,060), followed by reviews (717), letters (131), and meeting abstracts (122). The dataset includes 395,857 references and extensive thematic coverage, with 18,647 keywords plus entries and 26,121 authors' keywords. Despite its richness, only 35 retracted publications were noted, emphasizing overall research reliability. This analysis highlights a collaborative and globally connected research ecosystem with impactful and diverse contributions.
Scientific overview
The provided graph illustrates the annual scientific production of King Faisal University over a specified period, showcasing the number of articles published each year. The graph shows that the university's research output remained relatively low and stable until approximately 2015. Following this period, a noticeable upward trend began, culminating in a sharp and significant increase in publication volume between 2017 and 2022. This peak suggests a period of substantial growth in research activity, potentially driven by increased funding, strategic initiatives, or faculty expansion. However, there is a stark decline in publication output observed post-2023, indicating possible challenges such as funding cuts, policy changes, or other institutional constraints. King Faisal University experienced remarkable growth in scientific research output in recent years, followed by a sudden downturn that warrants further investigation to understand the underlying causes scient Figure 1.
Average Citations per Year
Figure 2 tracks the average citations per article over time, providing insights into the research impact. There is a clear upward trend in average citations from the late 1980s to a peak around 2018-2019, signifying increased research relevance or influence during that period. Post-2019, there is a noticeable decline in average citations, possibly reflecting a shift in research trends, reduced article longevity, or delays in citation accumulation for newer articles. graph shows a historical growth in research impact, peaking recently but followed by a decline, highlighting potential shifts in scholarly focus or citation dynamics.
Distribution and Collaboration Relationship of country/regions
Figure 3 shows a VOSviewer network visualization map illustrating international collaboration among various countries, likely in a specific research domain. Saudi Arabia is the most prominent node, indicating a high level of collaboration and influence with other countries. Other significant nodes include Egypt, India, and Pakistan, showing regional collaboration. The network is divided into color-coded clusters that likely represent regional or thematic collaborations. Green Cluster: Includes countries like Saudi Arabia, India, UAE, and South Africa, suggesting strong regional partnerships. Red Cluster: Countries such as the USA, Canada, and European nations like Germany and Italy, possibly representing Western collaborations. Blue Cluster: Contains Pakistan and China, potentially indicating collaborations within Asia. Yellow Cluster: Focused around Egypt, suggesting specific connections with neighbouring regions or thematic collaborations. The thickness of the lines represents the strength of collaboration. King Faisal University has strong collaborative links with other Middle Eastern institutions such as Cairo University and Alexandria University. International collaborations, while present (e.g., with Imperial College London), have relatively thinner lines, indicating moderate collaboration strength. The thickness of the lines between countries represents the strength of collaboration. Saudi Arabia has numerous strong connections, especially with Egypt, India, and Pakistan. Countries like the USA, England, and Germany have multiple moderate connections across clusters. The network indicates global research collaborations, but with stronger ties within specific regions, such as the Middle East and South Asia.
Top Ten Most Productive Most Impactful Authors
Table 1 show that Nair AB stands out as the most impactful author, with the highest ACP (21.53) and total citations (4,047), alongside a high h-index (34). Abd El-Lateef HM is ranked second, with slightly fewer total citations (3,394) and a lower ACP (16.97) but an equally high h-index (34). Amin MN, Venugopala KN, and Khan K rank highly for impact, with h-index values close to 30 and ACP values above 16, suggesting strong influence within their fields. Alam MW is the least impactful among the top authors, with the lowest ACP (9.01) and h-h-index (20), despite being a productive contributor
| Top Ten Most Productive Authors | ||||||
|---|---|---|---|---|---|---|
| Sl. No. | Authors | Record Count | % of 10,482 | TC | ACP | h Index |
| 1 | Abd El-Lateef HM | 200 | 1.908 | 3,394 | 16.97 | 34 |
| 2 | Nair AB | 188 | 1.794 | 4,047 | 21.53 | 34 |
| 3 | Kandeel M | 174 | 1.66 | 2,088 | 12 | 23 |
| 4 | Venugopala KN | 166 | 1.584 | 2,791 | 16.81 | 29 |
| 5 | Amin MN | 158 | 1.507 | 2,810 | 17.78 | 30 |
| 6 | Khalaf MM | 148 | 1.412 | 1,758 | 11.88 | 24 |
| 7 | Sreeharsha N | 142 | 1.355 | 2,241 | 15.78 | 25 |
| 8 | Alam MW | 141 | 1.345 | 1,279 | 9.01 | 20 |
| 9 | Khan K | 139 | 1.326 | 2,678 | 19.27 | 29 |
| 10 | Kumar S | 138 | 1.317 | 2,318 | 16.8 | 25 |
| Top Ten Most Impactful Authors | ||||||
| 1 | Nair AB | 188 | 1.794 | 4,047 | 21.53 | 34 |
| 2 | Abd El-lateef HM | 200 | 1.908 | 3,394 | 16.97 | 34 |
| 3 | Amin MN | 158 | 1.507 | 2,810 | 17.78 | 30 |
| 4 | Venugopala KN | 166 | 1.584 | 2,791 | 16.81 | 29 |
| 5 | Khan K | 139 | 1.326 | 2,678 | 19.27 | 29 |
| 6 | Kumar S | 138 | 1.317 | 2,318 | 16.8 | 25 |
| 7 | Sreeharsha N | 142 | 1.355 | 2,241 | 15.78 | 25 |
| 8 | Kandeel M | 174 | 1.66 | 2,088 | 12 | 23 |
| 9 | Khalaf MM | 148 | 1.412 | 1,758 | 11.88 | 24 |
| 10 | Alam MW | 141 | 1.345 | 1,279 | 9.01 | 20 |
Nair AB is the overall leader in terms of impact due to a combination of high citations per publication, total citations, and an impressive h-index. Abd El-lateef HM is the most productive author, but slightly trails behind Nair in terms of impact. Authors like Amin MN, Venugopala KN, and Khan K strike a balance between productivity and impact, with high rankings in both lists. Lower ACP and h-index values for authors like Alam MW and Khalaf MM suggest their work may be less cited on average compared to their peers. This data emphasizes the importance of balancing productivity (the number of publications) with impact (citations, ACP, and h-index) to measure an author's influence in their research field.
Analysis of Most Relevant Sources
Figure 4 illustrates the publication titles and their corresponding output counts for King Faisal University, reflecting the diversity and volume of research contributions. The top journals include Sustainability (315 articles), Saudi Medical Journal (238) articles), and Molecules (1 48 articles), indicating strong output in sustainability, medicine, and chemistry. Specialized journals like the Journal of Camel Practice and Research (128 articles) highlight the university's focus on rare areas relevant to the region, such as veterinary and animal sciences. Other significant contributions are visible in fields like engineering (IEEE Access, Applied Sciences Basel), mathematics (Mathematics, AIMS Mathematics), and multidisciplinary sciences (Scientific Reports, PLOS ONE). Additionally, Publications in pharmaceuticals (Saudi Pharmaceutical Journal, Pharmaceuticals, Pharmaceutics) and environmental research demonstrate a robust engagement with healthcare and sustainability challenges. The wide distribution of publications across high-impact journals in diverse fields underscores King Faisal University's commitment to multidisciplinary research, regional priorities, and global scientific advancement.
Analysis of Most Productive Authors
This VOSviewer map (Figure 5) illustrates the collaborative network of authors in a specific research domain. Here's an analysis of the visualization: Kaliyadan, Feroze is the most prominent and central node, indicating they have significant collaborative influence and are highly connected with other authors The map shows a single interconnected network with overlapping clusters. The colors (yellow to blue) represent a timeline or the intensity of collaboration during a specific period, likely 2022. Tovani-Palone, Marcos Roberto and Menezes, Ritesh G. are also prominent nodes with a strong collaborative relationship with Kaliyadan, Feroze. The thickness of the lines indicates the strength of collaboration between authors. Kaliyadan, Feroze has thick connections with several authors, including Tovani-Palone and Menezes, suggesting frequent co-authorship. Other authors, such as Padubidri, Jagadish Rao and Nakdad, Ali H., have multiple connections, but their links are thinner compared to the central nodes. (Color Gradient): The gradient from blue to yellow reflects collaboration activity over time. Authors like Kallyadan, Feroze have recent collaborations (closer to yellow), while others, such as Menezes, Ritesh G., have older collaborations (closer to blue).
Peripheral Authors: Authors such as Waheed, Yasir and Fischer, Florian are on the periphery of the network, suggesting limited collaboration or involvement in fewer projects with the central group.
Analysis and data visualization of Affiliations
This Figure 6 VOSviewer network visualization map represents collaborative relationships among universities and research institutions. Here's an analysis of the organizational collaboration, King Faisal University is the most prominent and central node, indicating it is the leading institution in terms of collaboration within thisnetwork. It has extensive connections with other universities, particularly in the Middle East. The map is divided into color-coded clusters representing groups of universities with close collaborative ties: Green Cluster: Includes Middle Eastern universities like Cairo University, Alexandria University, Ain Shams University, and Mansoura University, suggesting regional collaborations, Blue Cluster: Includes King Abdulaziz University, King Saud University, and Prince Sattam Bin Abdulaziz University, forming a network of collaborations within Saudi Arabia. Red Cluster: Features international universities such as Imperial College London, University of Toronto, and Yonsei University, indicating a global outreach of collaborations. “The thickness of the lines represents the strength of collaboration. King Faisal University has strong collaborative links with other Middle Eastern institutions such as Cairo University and Alexandria University International collaborations, while present (e.g., with Imperial College London), have relatively thinner lines, indicating moderate collaboration strength. Institutions like Jazan University and Xi'an Jiao Tong University are on the periphery of the network, suggesting fewer or more specific collaborations. The map emphasizes a strong regional collaboration among Middle Eastern institutions, with additional, albeit less extensive, connections to universities in Europe, North America, and Asia. The network showcases King Faisal University's central role in facilitating collaborations, primarily within the Middle East, and to a lesser extent, internationally. It highlights the university's regional leadership in research partnerships, with strong ties to Egyptian universities and moderate global connections. Further context, such as the field of study could provide deeper insights into these collaborations.
The Trend topics analysis
Figure 7 illustrates the evolution of research topics over time, represented by terms on the vertical axis and their frequency on the horizontal timeline. The size of the bubbles reflects term frequency, highlighting the prominence of specific topics.
Emerging Trends: Terms like "nanoparticles," "oxidative stress," and “heat transfer" gain prominence in recent years, suggesting a growing focusing these fields.
Steady Growth: Some topics, such as "diagnosis" and "risk factors," show consistent research activity over time, indicating sustained interest.
Legacy Topics: Older topics, such as "plasma concentrations" and "interferon gamma," appear early but diminish over time, possibly reflecting shifting priorities.
Diverse Fields: The chart spans interdisciplinary domains, from biology (e.g. "antibodies," "diseases") to materials science (e.g. "copper," "transport properties") and public health (e.g. "Pregnancy," "childhood asthma").
The Three field analysis
Figure 8 shows that a three_field plot linking Cited References (CR). Authors (AU), and Keywords/Topics (DE) in research. Here's a brief analysis: Cited References (CR): Foundational works, such as Perdew JP (1996) and Becke AD (1993), are highly cited, suggesting their significance in the research context. These references span physics, chemistry, and biochemistry, indicating interdisciplinary contributions. Authors (AU): Key authors, such as Khan K. Amin MN, and El-Beltagi HM, bridge foundational works with recent topics. These authors are likely influential figures contributing to emerging fields. Keywords/Topics (DE): Popular research topics include nanoparticles, machine learning. antioxidants, DFT (Density Functional Theory), and antimicrobial applications. Emerging topics like COVID-19, artificial intelligence, and oxidative stress reflects current scientific priorities. Figure 7 demonstrates the interplay between classic literature, leading researchers, and contemporary research themes. It highlights the ongoing evolution of research from theoretical foundations to applications in areas like health, nanotechnology, and computational methods. This visualization effectively captures the flow of influence across these three interconnected dimensions.
Core Sources by Bradford's Law
Figure 9 visualizes Bradford's Law, which categorizes journals or sources into "core" sources and peripheral ones based on their productivity (number of articles). The "Core Sources" section, shaded in grey, highlights journals that contribute the majority of articles in the dataset, indicating their significance in the field. As the rank of sources increases (moving right), the number of articles contributed by each source decreases significantly, showing a long-tail distribution. Key journals, such as Sustainability, Saudi Medical Journal and Molecules, appear to be highly productive and fall within the core group. Core Sources by Bradford's Law identifies a few highly productive journals as key contributors to the field.
Conceptual structural analysis
Figure 10 indicates a conceptual structure map generated using Multiple Correspondence Analysis (MCA). It visually represents the relationships among keywords or concepts.
Dimensional Axes (Dim 1 and Dim 2): Dim 1 (49.93%) and Dim 2 (16.45%) represent the variance explained by the first two dimensions. Dim 1 typically captures the primary contrast or clustering of concepts, while Dim 2 captures secondary variations. Clusters of Concepts: The red-shaded area groups related concepts. Concepts near each other are more closely related. Distinct regions within the map show thematic groupings or focus areas in the data. Key Themes Identified: On the left, terms like "inflammation," "oxidative stress," "apoptosis," and "activation" indicate a focus on biological mechanisms, possibly related to cellular or molecular biology. In the center, terms like "antioxidant," "toxicity," "extract," and "mechanisms" suggest research on natural compounds or biomedical properties. On the right, terms like "nanoparticles," "performance," "mechanical properties, "and "optimization" point toward materials science or engineering research.
Hierarchical clustering analysis
Figure 11 presents dendrogram, a tree-like diagram that represents hierarchical clustering. Clusters and Grouping: The dendrogram shows the hierarchical grouping of keywords or data points. Terms that are closely linked are joined at a lower height, indicating a stronger similarity or closer relationship. Main Branches: The data is divided into several main branches, each representing a distinct cluster of related terms or concepts. The left branch primarily includes terms related to inflammation, oxidative stress, and biological processes. The middle and right branches include terms tied to material properties, nanoparticle behaviour, and optimization. Interpretation of Heights: The height of the vertical lines (linkage distance) shows how dissimilar clusters are. Larger distances at higher levels indicate clusters with less similarity being joined together. This dendrogram can be used to identify key thematic areas in the dataset, providing insight into the structure of the data or research focus areas. This hierarchical clustering helps visualize the relationships and similarity levels among terms, revealing distinct thematic domains and their interconnections.
Analysis of Occurrence of most frequent keywords
Figure 12 presents the word cloud visualizes frequently mentioned terms in a research context. Prominent terms like "performance," "nanoparticles," "oxidative stress," "growth," and "impact" stand out, indicating their significance in recent studies. Other terms like "model," "expression," "behaviour," "design," and "in vitro" highlight themes related to experimental setups, biological studies, and material design. The varying font sizes reflect the frequency of usage with larger words being more dominant topics.
The Word Cloud captures frequently occurring terms with prominence assigned to larger words: Dominant Keywords: Performance, nanoparticles, and oxidative stress are prominently featured, aligning with material science and biochemistry themes. Growth, model, and in vitro further emphasize biological research and modelling efforts. Supplementary Themes: Terms like antibacterial, expression, and derivatives suggest strong interests in applied sciences and healthcare-related research. Behavior, management, and design hint at interdisciplinary work spanning systems, optimization, and human behaviour studies.
Figure 13 highlights co-occurrence relationships between keywords. Keywords are clustered into groups based on their thematic relevance: Red Cluster: Focused on Saudi Arabia, machine learning, model, and terms related to healthcare and sustainability such as diagnosis, infection, and knowledge. This indicates research outputs tied to local and global healthcare challenges, as well as advancements in machine learning. Blue Cluster. Dominated by terms like performance, nanoparticles, adsorption, and removal. This signifies research strengths in material science, particularly in nanotechnology applications and environmental remediation. Green Cluster: Keywords like oxidative stress, growth, in vitro, and expression suggest a focus on biological sciences, biochemistry, and toxicology. Yellow Cluster: Highlighting antibacterial, derivatives, and biological evaluation, reflecting contributions in medicinal chemistry and pharmaceutical development. Purple and Cyan Clusters: Themes around mechanical properties, fluid, and transport, suggesting research related to physics and engineering disciplines.
DISCUSSION
The scientometric analysis of KFU’s research output demonstrates a dynamic evaluation in its academic contributions. The longitudinal dataset, spanning 36 years, showcases an increase in research activity, with a significant surge in publications post-2015. This period of growth coincides with institutional initiatives and enhanced funding opportunities aimed at fostering challenges such as funding limitations or shifts in research priorities that require further investigation (Chester et al., 2017; Kappi, M; Chaman Sab, et al., 2020; Kappi, M., and Biradar, 2021; M, Kappi, and Ahmed, 2021; Yazdani et al., 2015). International collaboration emerges as a cornerstone of KFU’s research strategy, with 73.22% of publications co-authored with global partners. This highlights the University’s commitment to addressing global challenges in Egypt, India, and Pakistan, as well as moderate collaborations with Western and Asian Universities. Despite this, there is room to strengthen global outreach further by fostering deeper partnerships with impact institutions (Agre et al., 2019; Ahmed KK, 2023; Aria and Cuccurullo, 2017; Chaman Sab, Kappi, and Mueen Ahmed, 2022; Kappi, M Chaman Sab, and Biradar, 2020; KAPPI, S., and BIRADAR, 2021; Rajendran et al., 2023; Shettar and Hadagali, 2020). Disciplinary analysis indicates a diversified research portfolio with significant contributions to sustainability, healthcare, and materials science, Journals such as Sustainability and Saudi Medical Journal serve as primary publication venues, reflecting KFU’s emphasis on addressing regional and global challenges. Emerging trends in topics like nanoparticles, oxidative stress, and artificial intelligence signify KFU’s adaptability to evolving scientific priorities (Albohnayh et al., 2024; Falagas et al., 2008; Gagolewski, 2011; Hackler and Saxton, 2007; Ibrahim et al., 2009). The study underscores the role of impactful researchers, with authors such as Nair AB and Abd El-Lateef HM leading in productivity and citations. Their contributions exemplify a balance between quality and quantity, serving as benchmarks for academic excellence. However, the lower citation metrics for some authors suggest a need for targeted support to enhance research visibility and impact.
CONCLUSION
The scientometric analysis affirms King Faisal University’s significant role in advancing global knowledge, particularly in sustainability and healthcare. Its alignment with Saudi Arabia’s Vision 2023 reinforces its status as a leader in academic excellence and innovation. The findings provide actionable insights for enhancing research productivity, strengthening international collaborations and addressing challenges such as the recent decline in output. Moving forward, KFU should prioritize strategic investments in emerging research areas, bolster global partnerships, and adopt policies to ensure sustained growth in adamic output. Additionally, initiatives to support underperforming researchers and increase research visibility through open-access platforms and high-impact collaborations are critical. By addressing these priorities, KFU can continue to shape its legacy as a hub for scientific innovation ang lobal collaboration.