Analyzing collaboration and impact: A bibliometric review of four highly published authors’ research profiles on collaborative maps

Abstract

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors’ IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

Features:

• 1.Introduction of rating scale for research profiles (RSRP) used for bibliometric analysis.
• 2.Use of collaborative maps and bibliometric indicators for author analysis.
• 3.Findings emphasize collaboration role and individual contributions in scholarly impact.

1. Introduction

A researcher profile is a detailed summary showcasing a researcher academic background, accomplishments, and contributions to their field.^[1] It often contains details about their education, research focus, published works, collaborations, awarded grants, and affiliations with professional bodies.^[2] This profile with publications acts as a concise overview of a researcher career, highlighting their expertise, specialty areas, and influence in the scholarly world.^[3] Such profiles are crucial for networking, fostering collaborations, assessing academic contributions, and building a researcher reputation.^[4] While impact beam plots (IBPs)^[5,6] have been introduced to academia as a method for profiling researchers, a comprehensive analysis of highly published authors’ research profiles and the grade of their research achievements (RAs) through collaborative maps has not been conducted.

1.1. Research gaps and questions

The introduction of IBPs in the Web of Science^[6] author records has facilitated the display of authors’ research trajectories, yet there remains potential for enhancing these tools. Enhancements could include more streamlined, user-friendly interfaces that offer direct links and simplified options, enabling researchers to showcase their work without overloading readers with extraneous information.^[5,7–9] The goal is to make the process straightforward and accessible for researchers wishing to efficiently disseminate their publications. Despite these advancements, since its 2021 inception,^[10] the IBP feature has seen limited research specifically aimed at improving it through the use of collaborative maps.

In the realm of bibliometrics (a discipline that employs quantitative analysis and statistics to study publications like books and articles), various mapping techniques are employed to uncover relationships and patterns within scientific research.^[11] Among these, thematic and collaborative maps are prevalent tools in bibliometrics for visualizing data.

A thematic map^[12,13] concentrates on how different research topics within the literature are interconnected and how these relationships evolve over time, utilizing bibliometric data for visualization. Conversely, a collaborative map^[14,15] illustrates the patterns of collaboration among authors, institutions, or countries within a particular scientific domain or across multiple fields over time. Both types of maps serve as potent instruments for revealing the structure, dynamics, and evolution of scientific inquiry, offering visual insights that can assist stakeholders such as researchers, policymakers, and funding bodies in making well-informed choices regarding research focus, collaborations, and funding. However, the application of collaborative maps to evaluate RAs (e.g., grades A, B, or C) for individual authors based on their research profiles has not yet been explored.

1.2. Rating RAs through collaborative maps

A collaborative map integrates 4 key dimensions—motor, niche, emerging or declining trends, and co-word analysis—spread across 4 quadrants.^[12–15] These dimensions together provide a comprehensive framework for analyzing scientific collaboration, with each quadrant offering unique insights into the nature and dynamics of research networks.

Collaborative maps, as a potent blend of analytical and visual tools in bibliometrics, facilitate a deep dive into the fabric of scientific collaboration. They unlock valuable perspectives for shaping research strategies, informing policy development, and guiding the allocation of resources. Considering this, collaborative maps are seen as promising tools for assigning RAs to authors, grading them from A to F based on their proximity to the map centroid along the y-axis—the higher an author position and lower the centroid, the stronger their collaboration grade. Authors’ research profiles (instead of traditional publication list on website) will be used to evaluate their RAs using collaborative maps and complement IBPs.

1.3. Ideas from thematic maps

Thematic maps^[12,13] are instrumental in identifying how different research themes are interconnected, how they evolve over time, and how they are distributed across various scientific fields. It can be drawn in R^[16,17] with topic clustering, trend analysis, and research gaps identification. These maps can take various forms, such as network diagrams, where nodes represent topics and edges indicate relationships, or density maps, where areas of high activity are highlighted. It is essential to integrate grade evaluations (called the rating scale for research profiles, RSRP) into collaborative maps,^[14,15] similar to thematic maps based on article authorship, so that RAs can be better understood. Several prolific authors are targeted, exemplified, and illustrated in this study.

1.4. Study aims

This study introduces the RSRP to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification.

2. Methods

2.1. Data sources

Four authors (from Taiwan: Chih-Ping Chen, Kuo-Chuan Hung, Chin-Hsiao Tseng and South Korea: Sung-Ho Jang) were selected as examples. Their publications were downloaded from Web of Science core collection (WoSCC); see Supplemental Digital Contents 1, http://links.lww.com/MD/N18 and 2, http://links.lww.com/MD/N19. Their research employed collaborative maps to analyze the publication patterns of authors, focusing on co-authorship patterns and the impact of their scholarly work with RA grades.

All data deposited in Digital Contents 1 and 2 are publicly released on WoS without participant identification information. Ethical approval was thus waived.

2.2. Three parts in this research

The study consists of 3 parts, including an overview of the overall contribution of the 4 authors, a pair-comparison of each author research profile, and IBPs showing publications in each author research profile, along with radar plots,^[18] network diagrams,^[19] collaborative maps,^[20] thematic maps,^[21] and beam plots.^[5,6]

2.2.1. Overall contribution of the 4 authors

Four-quadrant radar plot was designed (e.g., top 10 countries, institutes, departments, and authors were dispersed in quadrants I, II, III, and IV, respectively). Bubbles were sized by the CJAL score.^[18]

$${\displaystyle \begin{array}{l}{\displaystyle CJAscore={\displaystyle \underset{i=1}{{\displaystyle \sum ^n}}}{C}_i\times J_i\times A_i,}\end{array}}$$ (1)

$${\displaystyle \begin{array}{l}{\displaystyle CJALscore={\displaystyle \underset{i=1}{{\displaystyle \sum ^n}}}{C}_i\times J_i\times A_i\times L-inde{x}_i,}\end{array}}$$ (2)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{L}\text{-}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}=\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}(\mathrm{l}\mathrm{o}\mathrm{g}(\frac{Citation}{A_n\times Age}+1),0),>=1}\end{array}}$$ (3)

The CJAL in Equation 2 is computed by the CJA score^[22] in Equation 1 via adding additional L-index^[23] in Equation 3.

Here, 3 factors are considered in the CJA scores for a published article: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A. A publication final score is calculated by multiplying each of these 3 aspects (Eq. 1). CJA scores original research articles higher than other types of manuscripts; co-first authors score higher than other collaborators; for the journal quality assessment, they use the JIF or SCI/SSCI journal rankings for SCI/SSCI-indexed papers.^[22] SCI/SSCI journal rankings are based on JIF in each research domain; therefore, domain-specific journal rankings are usually not significantly different from those based on JIF.^[22]

In the 4-quadrant radar plot, bubbles were sized by the CJAL score and colored by the perspective (i.e., countries, institutes, departments, and authors, respectively), where absolute advantage coefficients (AACs) were computed through Equations 4 to 5.^[24,25]

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{A}\mathrm{A}\mathrm{C}=\left({\mathrm{R}}_{12}/{\mathrm{R}}_{23}\right)/(1+\left({\mathrm{R}}_{12}/{\mathrm{R}}_{23}\right)),}\end{array}}$$ (4)

$${\displaystyle \begin{array}{l}{\displaystyle {\mathrm{R}}_{12}=\mathrm{A}1/\mathrm{A}2,}\end{array}}$$ (5)

$${\displaystyle \begin{array}{l}{\displaystyle {\mathrm{R}}_{23}=\mathrm{A}2/\mathrm{A}3,}\end{array}}$$ (6)

where the AAC ratio is determined by the 3 consecutive numbers of values (e.g., the member number in each cluster in descending order denoted by A1, A2, and A3 in Eqs. 4 and 5). The ACC ranged from 0 to 1.0, representing the strength of dominance for the top member when compared to the next 2 members. Through the computation of AAC, the dominance strength in a variable (e.g., country) can be measured and judged by the effect size, with criteria of <0.5, between 0.5 and 0.7, and not <0.7 as the small, medium, and large effect size, respectively.^[25]

2.2.2. Pair-comparisons of each author research profile

In this study, the follower-leading clustering algorithm (FLCA)^[5,26–28] was utilized for cluster analysis, despite the availability of 8 widely recognized clustering methods in R,^[17] including Affinity Propagation, Betweenness, Fast Greedy, Leading Eigenvector, Louvain, Optimal, Infomap, and Spinglass.^[29] Different from cluster analyses in CiteSpace^[30] or VOSviewer^[31] in author collaboration and keyword cooccurrences, FLCA contains only primary connection between vertices. The way to conduct the FLCA in R^[17] is provided.^[32]

When the user selects which graph to draw and inputs the data in the appropriate format, the platform generates R code.^[17] Simply pasting the R code into R will create the desired graph. If adjustments are needed for font size and color, they can be easily made by modifying the parameters (see video tutorial^[28]).

RAs were categorized into 6 levels, ranging from A to F, determined by their locations along the y-axis and the map central point using RSRP. An author grade for collaboration becomes higher as their position on the y-axis increases and the central point position decreases, as illustrated in Figure 1. The central point, or centroid, is identified as the average location for all map elements, calculated as the mean of their × and y coordinates. A grade F signifies that the targeted author is not visible on the map, due to the inclusion criteria focusing solely on the first and corresponding authors. In this situation, known as scenario F, authors who are neither the first nor the corresponding, typically referred to as middle authors, are acknowledged in the article byline.

Figure 1.

Rating scale from A to F for research profiles on collaborative maps (note. only 1^st and corresponding authors were considered in Levels 1 and 2).

In these 6 levels, there were 2 types of RAs: Leader ABC (top 1 strongly, moderately, or slightly differs from centroid in Level 1) and Follower 123 (targeted author publication behind that of others in Level 2).

2.2.3. Impact beam plot

The IBP^[5,6] can be drawn with an appropriate data format. The details are presented in the video tutorial.^[29] The 3 metrics of h-/x-indices,^[33,34] and density index (DL)^[5] were compared for the 4 prolific authors.

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}=\mathrm{m}\mathrm{i}\mathrm{n}\left(\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r},\mathrm{d}\mathrm{f}\right)÷\mathrm{d}\mathrm{f},}\end{array}}$$ (7)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{d}\mathrm{f}=\mathrm{C}\mathrm{E}1,}\end{array}}$$ (8)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\left(\mathrm{D}\mathrm{I}\right)=\mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}÷\mathrm{C}\mathrm{N},}\end{array}}$$ (9)

where, CN = cluster number and CE = count of elements. If every element is interconnected with the rest (meaning the model is fully integrated and the Clustering Number, CN, equals 1), then the discipline index (DI) is set to 1.0. Consider a network comprising 20 elements, indicating a degree of freedom (df) of 19, which corresponds to the density. In this case, each element is connected with every other element, resulting in a DI of 1.0 (calculated as 1 divided by 1). Consequently, an increase in the number of clusters within the network leads to a decrease in the DI value. The way to draw IBP is provided.^[35]

The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors’ IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author).

2.3. Drawing software and packages

Through the cluster analysis of author collaborations, the collaboration patterns among authors can be observed. The highest weighted centrality of degree (WCD)^[34] in each cluster is designated as the representative of that cluster. The targeted author in the research profile typically has the highest WCD (i.e., publication as 1st and corresponding authors).

The top 20 authors with the most publications are selected in each network, and 2 visual graphs (i.e., collaborative map and network plot) were generated using the FLCA algorithm.^[5,26–28] The R platform generates code in R language (version 4.2.1).^[17] RStudio software (version 1.3.959) is suggested. The platform system generates R code^[36] developed by Coding Statistics Service (www.raschonline.com).

3. Results

3.1. Overall perspective

Figure 2 illustrates a comparative analysis of the research contributions made by 4 entities. It highlights that the foremost contributors by publications include researchers from Taiwan, Yeungnam University in South Korea, specifically from the department of obstetrics and gynecology, with particular emphasis on the work of Dr Chih-Ping Chen. In the area of AACs, Dr Chih-Ping Chen stands out significantly, showing a pronounced superiority over the next 2 competitors, marked by a substantial RA of 0.75 based on CJAL score.^[18]

Figure 2.

Research achievements in comparison of these 4 authors’ publications.

Figure 3 showcases the main journals, research themes, and international collaborations associated with the 4 authors, highlighting: publications from Taiwan J. Obstet. Gynecol., Sci Rep, Medicine (Baltimore), and Exp. Diabetes Res.; key research areas including PRENATAL DIAGNOSIS, META-ANALYSIS, STROKE, and UPDATED META-ANALYSIS; collaborative efforts between the US and India with Taiwanese authors, specifically for the works of Chen, Hung, Jang, and Tseng.

Figure 3.

Primary journals, themes, and country-based collaborations for these 4 authors.

3.2. Pair-comparisons of each author research profile

The collaborative maps on the left side of Figures 4 and 5 reveal that each researcher profile has achieved a grade A, signifying their status as Leaders who are distinctly positioned away from the centroid in Level 1 of Figure 1. However, Dr Tseng, situated in quadrant II, differs from those in quadrant I, highlighting a lower frequency of collaborative efforts with his peers, as exemplified by his publication of 114 sole-authored articles.^[37,38] In contrast, the network of Dr Jang has the highest DL (=0.47), indicating a closer relation with his peers.

Figure 4.

Collaborative maps for authors Hung and Chen from Taiwan.

Figure 5.

Collaborative maps for authors Jang from South Korea and Tseng from Taiwan.

3.3. IBPs replacing traditional publications list

Figures 6 to 9 display the IBPs for the 4 authors with publications. Their respective h-indexes and x-indexes, as noted in references^[33] and,^[34] are as follows: for Chen, Hung, Jang, and Tseng, the h-indexes are 53, 51, 59, and 38, and the x-indexes are 75.7, 51.62, 83.19, and 41.42. Additionally, their Discipline Indexes, referenced in,^[5] stand at 0.67, 0.11, 3.41, and 2.65, respectively. These values serve to compare the authors in terms of the frequency of self-citations in their manuscripts, as outlined in.^[5] For instance, Dr Tseng with a higher DI have a lot of sole-authored articles.^[37,38]

Figure 6.

IBP for Dr Chen. IBPs = impact beam plots.

Figure 9.

IBP for Dr Tseng. IBPs = impact beam plots.

Figure 7.

IBP for Dr Hung. IBPs = impact beam plots.

Figure 8.

IBP for Dr Jang. IBPs = impact beam plots.

In IBP, the symbols A and B stand for the most cited articles in Web of Science and in research profiles, respectively. Upon clicking on any of the links,^[40–43] the article abstract will appear on the web instantly.

3.4. Online dashboards shown on Google Maps

Figures 6 to 9 feature dashboards linking to all the articles discussed. Readers interested in exploring articles relevant to each IBP are invited to scan the QR code provided and select the bubble corresponding to the article of interest for further reading.

4. Discussions

4.1. Principal findings

The analysis revealed that each of the 4 research profiles attained a grade A status, with their centroids positioned in the third quadrant, reflective of a significant scholarly impact. The h-indexes of the authors were identified as 38, 51, 53, and 59, in that order. Distinctly, Dr Tseng from Taiwan exhibited a unique pattern, having a considerable volume of solo-authored works situated in the second quadrant. This contrasts with the other 3 authors, who showed a stronger inclination toward collaborative efforts, as indicated by their placement in the first quadrant.

Accordingly, the hypothesis that each of the research profiles would attain a grade A has been verified.

4.2. Additional information

A researcher profile acts as a concise overview of a researcher professional journey, offering a glimpse into their areas of expertise, specialization, and their influence on the scholarly community.^[3] Such profiles are widely utilized for networking, identifying opportunities for collaboration, assessing academic contributions, and affirming a researcher standing and reputation within the field.^[4] Despite their utility, there is a gap in identifying and analyzing manuscripts that may predominantly be classified by their research level.

While visual IBPs^[5,6] have been introduced to pinpoint research profiles and publications of authors, as of now, there has been no implementation of a collaborative map for ranking individual research levels, as depicted in Figure 1. Although titles such as professor, associate professor, and assistant professor are frequently used to denote academic ranks in educational contexts, there has been no prior instance where the 2 visual tools of IBP and collaborative maps were integrated for classifying individual research levels. Through these 2 visuals, one can discern the range of publications within the IBP, while RA classification can be achieved using the collaborative map in 2 groups of Leader ABC and Follower 123.

Analyzing Figures 4 and 5, it is evident that the focal author is intended to be positioned at the very top, while their colleagues are situated at the very bottom, as determined by the map centroid. Initially, for an author listed in the middle of the article byline, their research status is pinpointed in panel F of Figure 1, attributed to the absence of primary authorship roles (either as first or corresponding authors). Over time, as the author RA intensifies, leading to changes and developments in their research domain, their status progresses upwards from level D to A. This signifies the evolution into an independent and seasoned researcher. Horizontally, the extent of collaborative work is measured by the interactions between first authors and corresponding authors. For instance, Figure 5 illustrates the case of solo-author publications by Dr Tseng, where he is predominantly located in quadrant II, diverging from the common placement in quadrant I.

4.3. Implications and possible changes

This article analysis leads to key insights and suggested improvements in bibliometric research:

1. Development of better visualization tools: There a call for more dynamic, user-friendly interfaces for showcasing researcher profiles, emphasizing the need for tools that clearly display publications, collaborations, and metrics.

2. Combining collaborative maps with IBPs: To fill a current gap, integrating collaborative maps with IBPs could offer deeper insights into research impact and collaboration trends, influencing future research directions and policy.

3. Refining research achievement ratings: Using collaborative maps for grading RAs introduces a new evaluation metric. This could be further developed to account for factors like collaboration diversity and interdisciplinary research.

4. Strategies for career and collaboration: The move toward collaborative research suggests strategies for researchers to expand networks and engage in interdisciplinary projects, aiming to boost visibility and impact.

5. Influence on policy and funding: Thematic and collaborative maps can help inform decisions on research priorities, grant allocations, and promoting collaborations.

6. Acknowledging middle authors: Highlighting the need to recognize middle authors’ contributions, suggesting a shift toward more inclusive metrics that value all contributors.

The study suggests that embracing these tools and strategies can significantly impact academic evaluation, policy-making, and the broader research community approach to collaboration and impact assessment.

4.4. Limitations and suggestions

The generalizability of the findings in this study is constrained by the limited examples of the 4 authors’ research profiles presented. Further research is needed to verify the applicability of the collaborative maps to authors across different disciplines.

To enhance the interpretability of collaborative maps, it is important to provide readers with additional information. This includes coloring the bubbles based on clusters, sizing them according to WCD,^[34] and providing direct messages to collaborative efforts on the horizontal axis. These details were not explicitly described in the context but are crucial for improving the understanding of collaborative maps.

While the primary focus of this study is on clustering the top 20 authors using the FLCA algorithm,^[5,26–28] it is important to note that more authors are excluded from collaborative maps for simplifying the visuals. It is worth noting that the number of authors is not limited to 20, as demonstrated in this study.

The centroid in a collaborative map represents the average position of all points, indicating the central location based on collective inputs or data points in the map context. The definition of centroid, in comparison to the focal author position on the collaborative map, has not been elaborated upon; however, it plays a crucial role in classifying research levels as emphasized in this study.

While this study presents collaborative maps and IBPs, it may be worthwhile to explore whether other graphical representations can provide additional insights for readers in the evaluation or classification of individual RAs.

R is a useful tool for creating visual diagrams, and this study offers several R plots (e.g., network chart and collaborative maps on the R platform^[32]) using the FLCA.^[32] For future comparisons, other cluster analysis algorithms^[29] are recommended.

5. Conclusion

This study introduces the RSRP, revolutionizing bibliometric research by providing a framework to unveil complex academic research patterns and grade A research profiles of 4 prolific authors. It transcends traditional bibliometric analysis, enriching academic research understanding through the 2 visualizations of collaborative map and IBP.

We propose expanding RSRP application, integrating it with other tools, and conducting longitudinal studies for deeper insights. Our findings can aid policy and strategy, suggesting that embracing RSRP and collaborative maps will significantly advance bibliometrics, inspiring future research to explore academic collaborations further.

Acknowledgments

We thank Coding (www.raschonline.com) for the English language review of this manuscript and statistical analytics.

Author contributions

Conceptualization: Willy Chou.

Investigation: Julie Chi Chow.