(1) Understanding Bibliometrics and Its Evolution 

Bibliometrics has emerged as one of the most popular methods of research, offering to the researchers and scholars a systematic and structured method for the analysis of the published literature through the usage of statistical techniques and this method transcends through the various disciplines, enabling the researchers for the discovery of new insights into the various trends, productivity parameters, collaborative networks, etc. for ensuring a good academic impact. Bibliometrics is closely associated with ‘Scientometrics’ a data analysis subset which was designed for analysis of scientific data, metrics and indicators and has been in vogue since the early 1940s with the researcher, Paul Otlet, introducing the term for describing a new method for the measurement of the various aspects of research publications. A few decades after the introduction of the term, further refinement was done in the 1969 by the researcher Alan Pritchard, who evolved a more technical definition for the term, involving mathematical and statistical methods, based on a scientific analysis of the data, applied to published literature books and other print media. Bibliometrics has been recognized at the strategic level, due to the crucial role it plays in the analysis of data for the stakeholders, for use in decision making in various aspects and use cases ranging from research literature to policy formulation and Bibliometrics is being used for assessing the impact of new & existing researches and to benchmark institutions along with the identification of new research areas for the researchers. Bibliometrics is beneficial for researchers as not only it is efficient, but also provides a greater depth of insight into the field of study, which could explain the rapid rise in its popularity as a methodology of research across the domains of social sciences, medicine and engineering and the methodology is on the path of evolution, through the rise in the volume of the large scale databases, softwares and information from the traditional, reading intensive method to the data driven insights based method, making its place as a useful compass, for navigation through the vast landscape of research. 

(2) Exploring the different Branches, Benefits, and Global Participation 

Bibliometrics is a multifaceted field of research, with a diverse range of specialized branches, each branch catering to a specific analytical need. For example, ‘informatics’ deals with the analysis of bibliometric data – author affiliations, publication dates, citation count numbers, etc. ‘Scientometrics’ has narrowed it down further to scientific literature wherein the productivity of the authors and institutions are evaluated through the usage of metrics such as the h-index, citation ratios, etc. In the age of today, cybermetrics and webometrics have gained a prominent place in the realm of bibliometric analysis with the former studying internet based online content and the latter focussing specifically on the website content data analysis. Of late, altmetrics has emerged for the evaluation of the impact of research studies into the various platforms of social media, capturing metrics and dimensions of influence which could be overlooked by the traditional citational metrics data. Bibliometric study is extremely beneficial to researchers because of the relative ease through which it can be done as access to data is far easier today, than it was before due to the existence of vast research databases such as SCOPUS, Web of Science, Google Scholar, etc. and can be performed at a reasonable cost with basic data analysis & statistical tools such as the VOSviewer and RStudio. Thirdly, bibliometric analysis methods are suitable to today’s era of fast life, enabling researchers to examine the various trends of interest, saving time otherwise spent on study of complete articles and has made them very attractive in the context of research studies in a time-sensitive scenario, which could explain its popularity ins several countries in the world with the researchers in the countries leveraging this methodology for understanding the current research output in their respective countries in their areas of interest, the data which could be used for setting benchmark values in terms of the trends in research, research collaboration and institutional rankings, enabling further contribution to the bodies of knowledge in terms of the outputs of such studies.

(3) Strategic Planning—From Topic Selection to Title Crafting 

Selection of a good topic for bibliometric research study is crucial for researchers. Researchers can save time and resources if they carefully narrow down their topic of study, finalizing the topic that is truly of interest to them for the conduct of the study. This task is crucial as a good topic reflects both the interest and the expertise of the researchers while also addressing any emerging or unexplored theme of research. A good topic has to be a topic which is contemporary and relevant and can inspire future research studies, post publishing of the research. The topic needs to be useful and not redundant so that the researcher’s time and resources are utilized in a right way, so that through the analysis, the researcher will be able to identify new insights and angles, qualitatively adding to the body of literature. Once the topic is selected, the next step is to create the title. The title needs to be short and sharp, able to capture the core of the research within a sentence or a two in a way which commands attention from the reader, creating an impact in their minds. This is important as the choice of a title reflects the aim, scope and the methodology of the study in a concise way. Beyond the title, the researchers need to focus on the two crucial components following and concluding the title – the abstract and the conclusion as the former summarizes the research within 100 – 150 words, describing the purpose, objectives, methodology, findings and implications in a concise way and the latter concludes it, consolidating the findings and laying the road ahead for future research studies in the topic area. 

(4) Data Collection, Tools, and Visualization Techniques 

Being that bibliometric research is based on data, the researcher needs to be careful in ensuring that the data they are using is relevant. The relevancy of the data can be ensured with the careful selection of the keywords, necessary for the identification of the relevant research literature. Due to this, the choice of the keywords needs to be precise, inclusive of synonyms, variations and other related concepts. For example, if the researcher is conducting a bibliometric research study in online education, he/she needs to include keywords such as ‘e-learning’, ‘MOOC’, ‘virtual classrooms’ etc. Academic databases such as SCOPUS, Web of Science, Google Scholar, etc. are commonly used by researchers as the sources of data. Of late, AI based keyword generation software is being used as a qualitative refinery for the search terms and for identifying any trending topic of interest to the researchers. Post gathering of the data, frameworks like ‘PRISMA’ (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) are utilized for organizing the data through various stages such as identification, screening along with ensuring that the collected data is eligible and inclusive for the research.

For the data to be rendered in a form ‘useful’ to the researchers, it needs to be cleaned so that the final data set is at a usable quality level as desired by the researchers. For example, author name variants and non-consistent institutional affiliations often cause the data to be spoiled which can be cleaned using tools such as OpenRefine for ensuring that the final form of the dataset is coherent with the end purpose of the research. Further visualization can be done using tools like VOSviewer for mapping the density of the keywords, the networks of the co-authors and the patterns observed in the citations. The quality of the data can be subjected to further quality refining through tools such as Gephi, which can perform deep level network analysis, enabling the final results to be depicted in a graphical way. Researchers can use metrics such as the link strength data and cluster density data for identification of the thematic hotspots, enabling the discovery of gaps in the research. For example, a bibliometric study conducted on the topic of renewable energy revealed that ‘solar energy’ is a dominant cluster whereas ‘vertical wind turbine systems’ is not that prominent, in terms of the representation of the data, which can suggest a gap, creating an opportunity for research in this topic area. Through these techniques of visualization, the data can be utilized in a way which not only enhances the presentation of the research but also provides a rich, analytical perspective into the research.

(5) Best Practices, Limitations, and Future Directions 

While Bibliometric study as a research methodology has its own store of advantages, there are a few challenges in this method which necessitates a careful analysis of the limitations in this method of research. A key limitation is the free access to complete papers as majority of the good papers are not available free of cost for the researchers, creating a challenge for the researchers with a limited budget. One will need to collaborate with large research centres for solving this lacunae, which could provide for a reach-around to this limitation. Another challenge is the sheer size of data as hundreds of papers are published and indexed to the databases on a daily basis in the world, which could be solved by limiting the dataset count to a reasonable number, say 500 to 1000 papers, providing a robust base for the data analysis and interpretation work, through the means of network and trend analysis. A smaller dataset (100 – 200 papers) might be sufficient for the conduction of a scoping review but it might not be beneficial for the conduct of a conclusive bibliometric interpretation due to the shallow nature of the dataset. Another challenge is the existence of biases in the databases. For example, majority of the available research papers are published in English (language bias), or a good number of the papers discovered in the initial search might favour more towards the technical or engineering topics (as indexed in the IEEE) with less number of papers discovered pertaining to topics relating to the humanities (disciplinary bias). Other similar biases exist, making the effort of research a daunting one. It is to be noted that, compared to the other research methodologies, bibliometric analysis is far easier for the researchers which explains the rising interest in it and the integration of AI based tools, altmetrics and real-time analytics in this methodology of research is making the task easier, expanding the horizon for the conduct of a far more dynamic and socially relevant research evaluation studies. 

In conclusion, we would like to state that Bibliometrics as a research methodology is a methodology which is subjected to continuos evolution, in reflection of the changes and developments in the dynamic nature of scholarly communication. Through the right strategy, tools and critical thinking, researchers will be able to open the doors to quality research studies, with profound insights, enabling them to make a meaningful contribution to their respective fields and to conduct a successful, bibliometric research, the following best practices such as development of a 100% clarity regarding the research area in order to refine the area further down to the topic, followed by the selection of the right tools for data securation and visualization along with the adoption of a structured framework for the process of data screening and analysis. Due to the fact that no data stays the same, it will be beneficial for revisiting and updating one’s research, in relevance to the presence of the new data and tools so that the quality of the research will stay fresh and relevant.