In today’s era of exponential scientific growth, understanding the vast landscape of academic research is more critical than ever. Researchers, institutions, and policymakers alike rely on robust methods to evaluate productivity, identify emerging trends, and make informed decisions about funding and collaborations. Bibliometric analysis (BA) has emerged as an indispensable tool for this purpose, offering quantitative and qualitative insights into the dynamics of scholarly communication. However, the true potential of bibliometrics is often untapped, limited by traditional approaches that fail to capture the full complexity of research performance and impact.
Two pivotal review articles, “Bibliometric analysis: A few suggestions” and its follow-up, “Bibliometric analysis: A few suggestions (Part Two),” provide a comprehensive roadmap for moving beyond superficial metrics to conduct more rigorous, nuanced, and meaningful analyses. Together, they argue for a shift from basic publication counts to a multifaceted evaluation that incorporates advanced indicators, deeper contextual understanding, and sophisticated analytical techniques.
Moving Beyond Basic Metrics: A Call for Depth
A common practice in many bibliometric studies is to present the number of publications per year as a primary indicator of research productivity. While this metric offers a glimpse into a field’s growth, it is an incomplete measure of influence. The first article strongly recommends supplementing this with per-year citation data and the total h-index of publications to provide a more informative perspective on research impact over time.
This call for depth extends to the evaluation of individual researchers. Traditionally, author performance has been judged by publication volume and the h-index. While valuable, these metrics have limitations; for instance, the h-index can be skewed by a single highly-cited paper and often disadvantages early-career researchers. To create a more holistic and fair assessment, researchers should consider a diverse array of alternative performance indicators. These include the g-index, which gives more weight to highly cited papers, and the m-index, which normalizes the h-index by the length of a researcher’s career. The consensus is clear: a multi-metric approach is essential to avoid the pitfalls of overemphasizing a single dimension of performance.
Building a Solid Foundation: Data and Tools
The quality of any bibliometric analysis hinges on the data it is built upon. The sources emphasize the importance of using multiple comprehensive databases, such as Scopus and Web of Science (WoS), to mitigate the limitations of any single source and ensure reliable results. A critical, and often overlooked, first step is the creation of a precise and comprehensive search string. Using Boolean operators, synonyms, and wildcard characters like the asterisk (*) ensures that data retrieval is both relevant and thorough, preventing gaps that could skew the analysis. The selection of search fields—whether title, abstract, keywords, or all sections—also significantly impacts the scope and precision of the results.
A variety of software tools are available to facilitate analysis and visualization, each with unique strengths. VOSviewer is popular for creating network maps of co-authorship and keyword co-occurrence, while CiteSpace specializes in identifying emerging trends and influential research fronts. Other tools like BibExcel and the R package Bibliometrix offer powerful functionalities for data processing and analysis. The choice of software should align with the specific research questions being addressed.
Advanced Techniques for Deeper Insights
The second article builds upon this foundation by introducing more advanced analytical techniques that can uncover the intellectual structure and evolution of a research field.
- Co-Word and Thematic Evolution Analysis: Traditional co-word analysis identifies thematic relationships by mapping keyword co-occurrence. To add a dynamic dimension, researchers can study thematic evolution by dividing the publication timeline into smaller intervals (e.g., 5 or 10 years) and analyzing how keyword clusters and their relationships change over time. This reveals how research topics emerge, grow, mature, and sometimes decline, offering a powerful lens on a field’s trajectory.
- Bibliographic Coupling and Co-Citation Analysis: These two methods map the intellectual connections between publications. Bibliographic coupling measures the similarity between two documents based on the references they share, which is useful for identifying contemporary research communities working on similar topics. In contrast, co-citation analysis measures how often two documents are cited together by other papers, helping to identify foundational or landmark studies that have shaped a field’s development.
- Three-Way Plots: To visualize complex relationships, three-way plots can map the interactions between three different entities, such as authors, universities, and countries. This technique provides a holistic view of collaboration networks, revealing influential research hubs and the global flow of knowledge.
A Comprehensive Framework for Future Research
A review of 36 published bibliometric studies revealed common gaps: an over-reliance on publication counts for author performance (85% of papers), inconsistent presentation of institutional and country data, and a lack of transparency regarding funding sources.
To address these shortcomings, a comprehensive bibliometric study should integrate multiple layers of analysis. It must start with a solid data foundation, present a rich array of performance indicators for authors, institutions, and countries, and employ advanced techniques to explore collaborative networks and thematic evolution. The ultimate goal is not just to present descriptive data, but to interpret it meaningfully, providing context and actionable insights that advance our understanding of the scientific landscape.
By adopting these rigorous and multifaceted approaches, the practice of bibliometric analysis can be elevated, transforming it from a simple counting exercise into a powerful tool for strategic insight and scholarly evaluation.
References
Hassan, W., & Duarte, A. E. (2024). Bibliometric analysis: A few suggestions. Current Problems in Cardiology, 49(8), 102640. https://doi.org/10.1016/j.cpcardiol.2024.102640
Abdelwahab, S. I., Taha, M. M. E., Farasani, A., Abdullah, S. M., Moshi, J. M., Alshahrani, A. F., Khamjan, N. A., Khired, Z. A., Assiri, A., Alqassmi, A. M., Alhusayni, A. M., Ibrahim, I. A. A., Alshahrani, S., & Hassan, W. (2025). Bibliometric analysis: A few suggestions (Part Two). Current Problems in Cardiology, 50(7), 102982. https://doi.org/10.1016/j.cpcardiol.2025.102982
Note 1: The key elements that both articles emphasize for consideration in bibliometric analyses are summarized below. These elements aim to ensure that the analyses are more comprehensive, rigorous, meaningful, and effective.
- Data Collection and Preparation Stage
Both articles stress that a robust analysis begins with a meticulous data collection process.
• Comprehensive Database Use: Analyses should not rely on a single database (e.g., Web of Science or Scopus). Since each database has its own strengths and weaknesses, using at least two major databases such as WoS and Scopus together makes the results more comprehensive and reliable.
• Precise Search Strategy (Search String): One of the most critical steps in accurate and complete data collection is constructing a precise and comprehensive search string. This string should fully cover the topic by including keywords, synonyms, and Boolean operators (AND, OR, NOT). The use of wildcard characters such as an asterisk (*) is also recommended to capture different variations of terms.
• Search Field Selection: Choosing the fields in which the search will be conducted (title, abstract, keywords, or full text) significantly affects the results. Researchers must consider the advantages and disadvantages of this choice (e.g., a broader search may bring irrelevant results).
• Defining Scope: Clearly specifying the time period, types of documents to be included (articles, reviews, conference papers, etc.), and language ensures that the research remains focused and consistent. - Diversification of Performance Indicators
The articles argue that relying on a single metric when evaluating the performance of researchers, institutions, and countries can be misleading, and that multiple indicators must be used.
• Author Performance: In addition to traditional metrics such as the number of publications and the h-index, a more holistic evaluation strongly recommends the use of alternative indicators such as the g-index (gives more weight to highly cited articles) and the m-index (normalizes the h-index by career duration). The second article provides a comprehensive list of 80 different author performance indicators on this subject.
• University and Country Performance: When evaluating the research performance of universities and countries, focusing only on the total number of publications or citations is insufficient. Including data such as international collaboration networks, comparisons with neighboring countries, and research funding in the analysis provides a richer context. Moreover, rather than just examining the top-ranked countries, tracking how a research topic spreads and evolves globally is more illuminating. - Going Beyond Superficial Analyses
It is emphasized that bibliometric analysis should not merely present descriptive data but should employ more in-depth and dynamic analytical techniques.
• Annual Production and Impact: While showing the annual number of publications is common, this alone is insufficient. Presenting this data together with metrics such as average citations per year and the cumulative h-index of publications provides a more meaningful perspective on the growth and impact of a field.
• Co-Word Analysis: Presenting maps showing the co-occurrence of keywords is valuable, but interpreting the clusters in these maps is often neglected. Cluster analysis reveals the main research themes and sub-disciplines within a field.
• Thematic Evolution: To make keyword analysis more dynamic, it is recommended to divide the research timeline into specific intervals (e.g., 5- or 10-year periods) and examine how keyword clusters change over time. This shows how research topics emerge, grow, or decline.
• Advanced Techniques: To enrich analyses, the following advanced techniques are recommended:
◦ Bibliographic Coupling: Measures similarity between documents with shared references, identifying current research communities.
◦ Co-citation Analysis: Examines how often two documents are cited together by other publications, revealing the intellectual foundations and landmark studies of a field.
◦ Three-way Plots: Visualize complex relationships between three different elements such as author, university, and country, providing a holistic view of global collaboration networks and knowledge flows.
In summary, both articles argue that bibliometric analyses should move away from superficial and one-dimensional measurements, adopting multi-layered, contextual, and dynamic methods. This approach is necessary for a more accurate understanding of scientific productivity and impact.
Note 2: To make the 80 bibliometric indicators more understandable, they can be grouped into five main categories:
- Productivity and Output Indicators
Measure the volume of research activity, such as total number of publications (NoP), role-specific publications (first, last, corresponding, single author), career span (first and last publication year, total active years). - Citation and Impact Indicators
Focus on scientific influence, including total citations, citations per paper (CPP), citations per year (CPY), share of self-citations, h-index without self-citations, and field-weighted citation impact for cross-disciplinary fairness. - Hybrid Indices (Productivity + Impact)
Combine quantity and impact in one metric. Examples include h-index (balanced measure), g-index (weights highly cited papers), m-index (adjusts for career length), and alternatives such as a-index, e-index, and others that refine specific aspects of h-index. - Collaboration and Network Indicators
Assess collaboration breadth and diversity, such as number of co-authors, international collaborations, and involvement with multiple institutions or countries. - Authorship Role and Contribution Indicators
Analyze the author’s share and roles in publications, e.g., proportion of first, corresponding, last, co-author, or single-author roles.
Interpretation and Importance
While using all 80 indicators in one analysis is impractical, their diversity prevents over-reliance on a single metric. Performance evaluation should be:
- Holistic: Combining productivity, impact, and collaboration measures.
- Contextual: Metrics chosen according to purpose (e.g., promotion, funding) and career stage (e.g., m-index and CPY are more relevant for early-career researchers).
- Cross-disciplinary: Considering disciplinary differences in citation practices; normalized measures like field-weighted citation impact are crucial.
Conclusion
These 80 indicators offer a rich toolbox for assessing researcher performance. The goal is not to identify a single “best” metric but to combine multiple indicators intelligently to create a nuanced and context-sensitive portrait of academic contribution.