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Article

Discrepancies in Mapping Sustainable Development Goal 3 (Good Health and Well-Being) Research: A Comparative Analysis of Scopus and Dimensions Databases

by
Raghu Raman
1,*,
Vinith Kumar Nair
1 and
Prema Nedungadi
2
1
Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India
2
Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(23), 16413; https://doi.org/10.3390/su152316413
Submission received: 21 August 2023 / Revised: 30 September 2023 / Accepted: 8 November 2023 / Published: 29 November 2023
(This article belongs to the Section Development Goals towards Sustainability)
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Abstract

:
Understanding the alignment and contributions of scientific research to the Sustainable Development Goals (SDGs) is essential for guiding global progress toward these critical targets. In this context, the study focuses on SDG 3 (Good Health and Well-Being), as it dominates the most researched SDG literature, thus providing a meaningful representation of the broader SDG research landscape. The comprehensive examination of the SDG 3-related research from 2018 to 2022 highlights significant discrepancies in publications mapped to SDG 3 between the two largest databases, Scopus and Dimensions. Despite previous studies showing Dimensions to have more coverage, the present analysis finds Scopus to report 124% more publications in this area. These discrepancies extend across various levels, including country, funder, institution, journal, and author, and have substantial implications for stakeholders relying on these data. Notably, contrasting cluster themes are discovered, with Dimensions revealing five integrative clusters and Scopus focusing on six specialized ones. This discrepancy can affect global research attention, funding allocation, institutional recognition, and SDG journal alignment. The findings emphasize the need for standardization and transparency in SDG mapping methodologies, especially as the 2030 target year approaches and progress on many 2030 SDG targets is lagging. It also highlights the importance of acknowledging and understanding these disparities at various levels of the research ecosystem. The study raises questions about similar discrepancies in other SDGs and necessitates a broader analysis that might include more databases and refine publication types. It serves as a cautionary reminder to the scientific community, policymakers, and other stakeholders about the importance of careful and comprehensive evaluation when mapping publications to SDGs.

1. Introduction

The adoption of the United Nations’ Sustainable Development Goals (SDGs) in September 2015 marked a critical evolution in international developmental priorities [1]. As a successor to the eight Millennium Development Goals (MDGs) culminating in 2015, the SDGs offer a comprehensive, international political agenda emphasizing collaborative action to attain peace, prosperity, and holistic well-being for all by 2030 [2]. A notable shift from the MDGs to the SDGs is the change in approach from vertical, addressing specific sectors, to a more horizontal methodology that casts a broader net over global challenges [3]. These 17 SDGs address pivotal global concerns, from promoting gender equality and ensuring good health and wellbeing to acting on climate change and fostering industry, innovation, and infrastructure [4].
In the academic domain, there is an ever-growing recognition of the role of research in advancing the attainment of these SDGs. Mapping research publications to the SDGs establishes academia’s commitment to these goals and charts out the knowledge landscape directly geared towards addressing global sustainability challenges [5]. Specific studies [6,7] have emphasized the intricate network of interconnectedness within the SDGs, shedding light on how academic endeavours span multiple goals.
Scopus and Dimensions databases have developed distinct mechanisms for filtering and mapping SDG research publications. In Scopus, the database employs a keyword-based approach to identify papers related to SDGs. On the other hand, Dimensions employs its proprietary machine-learning algorithms for classification and is generally considered more stringent. Comparative analyses of distinct SDG mapping initiatives reveal variations in how different initiatives map publications to respective SDGs [8,9,10]. Furthermore, initiatives like the Aurora Network Global [11] and the SDG Mapping endeavour by the University of Auckland [12] provide context to our exploration.
The implications of these methodological divergences are profound for academic rankings. For instance, institutions focusing on SDG3 (Good Health and Well-being) and SDG4 (Quality Education) may be variably represented depending on the database. This could affect funding opportunities, collaborative ventures, and policymaking, emphasizing the need for a standardized methodology for SDG research categorization.
Under SDG 3, “Ensure healthy lives and promote well-being for all ages” is the goal’s stated commitment [2]. While SDG 3 is devoted to good health and well-being, health contributes to almost all the other goals [13]. Due to the numerous health issues that have plagued the world in recent years, including the COVID-19 pandemic, SDG 3, which focuses on “good health and well-being,” has dominated the global discourse [14,15]. Given the centrality of SDG 3 and its evident prominence in academic research, our study embarks on an analysis of the SDG 3 mapped publications and their related citations from the two largest databases, Scopus and Dimensions. We also observe that a leading journal focused on the topic of sustainable development has most studies focused on SDG 3 [16].
Ranking universities based on their contributions to Sustainable Development Goals (SDG) research is becoming more common as addressing global challenges through academia gains recognition [17]. Discrepancies in publication and related citation counts could disadvantage SDG-focused universities by undercounting their contributions, distorting their commitment and societal impact in rankings. Misrepresentations might divert resources from other research areas, impacting their reputation and global standing due to distorted rankings and underrepresentation in SDG research contributions.
It is imperative to explore the methodologies behind databases like Scopus and Dimensions, as they directly impact the academic discourse surrounding SDGs. Based on the Elsevier SDG Mapping Initiative used by Scopus [18] and the Sustainable Development Goals filter by Dimensions [19], we aim to compare the publication counts and distribution across parameters such as countries, funding sources, institutions, journals, and authors. The methods and findings of this research also present an opportunity for extrapolation to studies concerning other SDGs, contributing to a broader understanding of research related to sustainable development goals.
The study uses a quantitative approach, and a bibliometric analysis is used to address the following research questions:
  • RQ1: How does the trend of SDG 3 mapped publications compare in Scopus and Dimensions?
  • RQ2: What is the pattern of disparities in SDG 3 mapped publications when studied at country, funder, institution, journal, and author level?
  • RQ3: Is there a significant difference between citation counts of the same paper between Scopus and Dimensions?
  • RQ4: Are there temporal trends in citation differences between Scopus and Dimensions?

2. Literature Review

Over the last two decades, scholars and practitioners have examined sustainable development from different perspectives. Study by Olawumi and Chan [20] looked at how the field of research has changed over time, from the Brundtland Commission report’s formulation of its principles to the more recent creation of models and sustainability indicators. They also reported that the United States, China, the United Kingdom, and Canada have substantially contributed to sustainability research. Another study [21] analysed 4593 research articles at the meta-level, explicitly referring to the SDGs. The study reveals that the Social Science and Life Sciences research fields produce the most SDG-related literature, with SDG 3 (“good health and well-being”) being the most prevalent SDG among the papers examined. It is also worth noting that the “Sustainability” journal continues to be a very big reference point for SDG research [16].
Higher education institutions (HEIs) are uniquely placed to play a leading role in supporting the attainment of Sustainable Development Goals [9,22]. Research and higher education institutions can contribute to achieving the SDGs through research and other engagement [23]. The institutions can assist in forming public policy for sustainable development and provide the knowledge foundation required for making decisions and creating options for sustainable development paths [5].
Studies have compared the two databases, Scopus and Dimensions [24,25], for journal coverage. In one of the studies, Kar and Harichandran [26], in their work on Green Marketing and Sustainable Consumption, conducted a search using specific search phrases and mapped research and review articles from Scopus and Web of Science (WoS) databases, respectively. They checked overlapping across the databases and found 46% overlapping across the two datasets. Christov et al. [27] studied the concept of Open Eco-innovation in the context of sustainability and mapped existing research using both Scopus and WoS. They also reported that exclusive use of WoS captures only 71% of the current literature on OEI, and exclusive use of Scopus captures only 79%. In their Energy Transition Bibliometric Research, Harichandan et al. [28] mapped peer-reviewed publications accessible in two databases, identifying patterns and gaps and suggesting areas for further research. They detected 49% overlapping across the two datasets. The study by Visser [29] also highlights that there is value both in the comprehensiveness and the selectivity offered by Scopus and that comprehensiveness and selectivity are no longer mutually exclusive. Generally, most quantitative bibliometric studies concentrate on analysing documents from one database; however, the coverage between different databases and mapping methodologies also shows variation in results [30,31].
Comparative analyses of SDG mapping initiatives reveal variations in how different initiatives map publications to respective SDGs [8,32]. Recently, there have been studies to map publications in a thematic area to SDGs using the SDG mapping initiatives—women entrepreneurship [33], business research [6], cyberbullying research [34], analytical hierarchical process [35], and water security [36].
Discrepancies in SDG-related research paper counts can significantly impact journal and university rankings that utilize SDGs as parameters. Over-crediting specific journals for SDG research could inflate their perceived impact. Journals might prioritize quantity over quality, shifting focus from vital research areas. Though efforts to rate or rank academic journals based on their contributions to the United Nations’ Sustainable Development Goals (SDGs) are still relatively nascent, some initiatives are being taken. Simon Linacre introduced the first SDG Impact Intensity journal rankings in a collaborative partnership between Saint Joseph’s University and Cabells Scholarly Analytics [37]. Utilizing data compiled from Cabells’ Journalytics database and applying AI-driven methodology for discerning the relevance to Sustainable Development Goals (SDGs), the SDG Impact Intensity model assigns a rating of up to three “SDG rings” to encapsulate the SDG pertinence of articles published within journals.
Evaluating universities based on their efforts to contribute to these goals can help highlight institutions actively working toward positive societal change [38]. Some well-known agencies and organizations that have started ranking universities based on their SDG research contributions include Times Higher Education (THE), which launched the “Impact Rankings” in 2019, which evaluates universities’ contributions to the SDGs incorporated the Elsevier SDG Mapping Initiative to rank universities’ contributions to various SDGs [39]. QS Quacquarelli Symonds also includes an SDG component in their World University Rankings, assessing universities’ commitment to and impact on the SDGs. Academic Ranking of World Universities (ARWU), often called the Shanghai Ranking, introduced an SDG-focused ranking in 2020. These rankings consider research publications, research funding, collaborations, courses and programs, innovation and impact, community engagement, awareness, and advocacy [40,41].
Bibliometric studies emphasize SDG 3 performed using different research databases like Scopus, WoS, Dimensions, and Google Scholar. Raman et al. [42] identified essential connections between COVID-19 studies and their implication for SDG 3. They reported strong connections with SDGs 3 and SDG 11, which are about making cities and human settlements inclusive, safe, resilient, and sustainable. Raji and Demehin [43] analysed the contributions of academia by using bibliographic mapping to examine scholarly publications on SDG 3. The study observed that while research on SDG 3 is increasing, a negative growth rate may not be because of a lack of innovation but due to unforeseeable events, such as the COVID-19 pandemic.

3. Methodology

In this study, the dataset covering the period 2018-22 examines SDG 3 (Good Health and Well-being), which presents the highest discrepancy in mapped publications. The dataset is organized based on country, funder, institution, journal, and author-based publications.
This study employs quantitative research. PRISMA-P protocol [10] followed bibliometric studies to evaluate SDG 3 publications from Scopus and Dimensions between 2018 and 2022 (Figure 1). A Boolean search approach is used in this study. There have been attempts to use Boolean queries that combine terms and phrases typical of the objectives and targets listed by the United Nations to discover scientific outputs relating to these SDGs automatically [8]. Each SDG-relevant publication collection was created using a bottom-up methodology, in which numerous sub-queries were initially created for each SDG objective and then aggregated at the SDG level. The initial step in the evaluation process was to look at the keywords from the Elsevier 2020 [32] and Aurora European Universities Alliance [44] questions.
Table 1 shows the top academic journal publishers [45] selected based on their coverage of at least 80% of total SDG 3-mapped publications in the Scopus database. It is important to note that OMICS International has been widely discredited for its predatory publishing practices [46]. This same list of publishers was subsequently employed to filter SDG 3-mapped publications within the Dimensions database. Searches were conducted on 13 July 2023. Keyword co-occurrence analysis [47] was performed using VOSviewer software (v1.6.19) on highly cited SDG 3 research publications (n = 19,976) from Scopus and Dimensions. In recent years, keyword co-occurrence networks have been exploited for knowledge mapping [48]. In this study also, knowledge mapping from Scopus and Dimensions is performed.

Search Strategy

We briefly describe the search strategies adopted by the two databases.
Scopus employs a multi-faceted approach for mapping research publications to Sustainable Development Goals (SDGs). Utilizing a sophisticated text-mining algorithm, it scans article titles, abstracts, and keywords against a predefined set of SDG-aligned keywords and phrases. To ensure accuracy, manual curation or expert review supplements the automated processes. Furthermore, the subject classification of the journal in which the paper is published is considered an additional metric to establish relevance to specific SDGs. Citation analysis is also integrated, offering a mechanism to gauge the paper’s impact within its respective SDG field. Overall, Scopus’ approach is more exclusive and stringent in its inclusion criteria for SDG mapping.
Dimensions uses a combination of natural language processing (NLP) and machine learning (ML) to map research publications to SDGs. The NLP system is used to extract key concepts and ideas from research publications. The ML system is then used to match these concepts and ideas to the relevant SDGs. The mapping process in Dimensions is predominantly automated with minimal manual intervention, which accelerates the process but could result in more false positives. In addition to journal articles, Dimensions includes a wider variety of publication types in its SDG mapping. Overall, Dimensions aims for inclusivity, mapping a wider range of papers to SDGs compared to more stringent databases like Scopus. To generate the training sets of research publications that were published from the beginning of 2010, approximately coinciding with the establishment of the UN Millennium Development Goals (MDGs), the predecessor to the UN SDGs.
Table 2 highlights the search queries executed on Scopus [32,47] and Dimensions [48] database.

4. Results and Discussion

Table 3 shows the total number of publications accessed from Scopus and Dimensions, filtered based on the largest publishers.

4.1. Publication Trends

The RQ1 was how the trend of SDG 3 mapped publications compare in Scopus and Dimensions. Figure 2 provides the answer to RQ1 and shows a year-by-year comparison of the total number of SDG-mapped publications mapped by two databases: Dimensions (n = 1,348,969) and Scopus (n = 3,021,627), from 2018 to 2022. It also presents the percentage difference between the two databases’ publication counts each year, showing that Scopus consistently maps more publications than Dimensions. On average, Scopus reports about 124% more publications than Dimensions over the period considered.
While the data effectively answer the research question, they also introduce additional questions and potential biases that warrant critical discussion.
Firstly, the considerable variation in publication counts between the two databases suggests the presence of methodological biases. Such variations can significantly impact the reliability of these databases as sources for academic insights into SDG 3 research. This issue calls for an inquiry into the mapping algorithms and protocols followed by each database. Secondly, the stark difference of 124% more publications in Scopus also necessitates an evaluation of what types of publications are being included or excluded in each database. Are these articles, reviews, conference papers, or other types of scholarly outputs? Different types of publications have different impacts and relevancies to SDG 3, which could introduce another layer of methodological bias.
The RQ2 on the pattern of disparities in SDG 3 mapped publications, when studied at country, funder, institution, journal, and author level, is discussed in Section 4.2–Section 4.6.

4.2. Prolific Countries

Figure 3 presents the total number of publications mapped to SDG 3 from the Scopus and Dimensions database based on countries. The “% Difference” column indicates where TPS exceeds TPD.
A data review reveals that Japan and South Korea exhibit the most significant percentage differences, at 260% and 238%, respectively, indicating a substantial discrepancy between TPS and TPD publications within these nations. The United States, despite having the most publications overall, shows a comparatively lower percentage difference of 99%. On the other end of the spectrum, countries like Australia and India show relatively smaller differences between TPS and TPD publications, at 114% and 124%, respectively. The pronounced discrepancies for Japan and South Korea raise questions about potential regional or institutional biases in database algorithms. Is the higher rate of mapping in Scopus indicative of a database bias favouring specific regions, or is it reflective of actual research output?

4.3. Top Funders

As seen in Figure 4, the National Institute of Health (United States) registers the most significant percentage difference, a staggering 2212%, indicating a substantial predominance of TPS over TPD publications. Similarly, the National Research Foundation of Korea shows a high difference of 304%. Conversely, the European Commission (Belgium) has a negative difference of −20%, indicating more TPD publications than TPS. Moreover, institutions like the National Heart, Lung, and Blood Institute (United States), the Medical Research Council (United States), and the National Center for Advancing Translational Sciences (United States) present relatively lower differences of 68%, 45%, and 28%, respectively, indicating a more balanced distribution between TPS and TPD publications.
The astronomical difference in the case of NIH requires an immediate critical evaluation. One line of inquiry would consider whether NIH-funded research follows specific guidelines that align better with Scopus’ indexing algorithms than with Dimensions’. A further query should examine the granularity of the research themes within SDG 3 funded by NIH, as this could influence database inclusion. Does the National Research Foundation of Korea have a specific focus within SDG 3 that aligns closely with Scopus’ inclusion criteria? Finally, what attributes of European Commission-funded research make it more prevalent in Dimensions? Is this reflective of a methodological bias in Dimensions toward European research or merely a broader array of research typologies in European output?
This data highlights the variations in the SDG 3-related research output across diverse funding institutions and countries, painting a complex picture of the global health research landscape. The disparity between the TPS and TPD publication counts for different funding agencies emphasizes the need for these funding agencies to consider multiple databases when evaluating their research impact related to SDG 3. Funding agencies should use their clout to put pressure on database providers to improve their indexing methods.

4.4. Productive Institutions

Observing the data in Figure 5, we can notice Inserm (France) exhibits the highest percentage difference, with an impressive 1150%, suggesting a considerably higher count of TPS publications than TPD. Similarly, Mayo Clinic (United States) and Karolinska Institutet (Sweden) show substantial percentage differences of 162% and 159%, respectively, reflecting a significant skew towards TPS publications.
Contrastingly, the University of Washington (United States) stands at the lower end with a modest 63% difference, indicating a more balanced distribution between TPS and TPD publication counts. Regarding absolute differences, Harvard University leads with a difference of approximately 24,196 publications, followed by the University of Toronto with a difference of about 21,743 publications.
These discrepancies underscore the significance of considering multiple databases to understand SDG 3-related research outputs by academic institutions worldwide comprehensively. Relying solely on one database to rank universities based on SDG research may lead to inaccurate and skewed results, potentially impacting the university’s reputation, funding prospects, and collaborative opportunities.

4.5. Top Journals

Figure 6 shows that The International Journal of Molecular Sciences has the highest percentage difference at 379%, indicating a significant predominance of TPS publications over TPD ones. Cancers and Frontiers In Neurology also show substantial percentage differences of 366%.
In contrast, Nature Communications and BMC Public Health show lower differences of 55% and 47%, suggesting a more balanced distribution of publications between TPS and TPD. Regarding absolute differences, PLOS ONE presents the most substantial difference, with approximately 23,386 more TPS publications than TPD, followed by The International Journal of Environmental Research and Public Health with a difference of around 20,083 publications.
The observed discrepancies raise questions about the relevance of these journals in the broader SDG 3 research landscape. Are these journals cited more frequently in one database versus another, and if so, what are the implications for the journal’s perceived influence or impact within the SDG 3 domain? Discrepancies due to various SDG mapping queries can misrepresent a journal’s reputation and perceived contribution to SDGs, influencing researchers’ publishing decisions, career progression, and funding prospects. Hence, researchers should consider multiple databases to evaluate their targeted journals comprehensively.

4.6. Influential Authors

Figure 7 compares the count of SDG 3-related publications for individual authors, as mapped in Scopus (TPS) and Dimensions (TPD) databases. The column ‘% Difference’ reveals significant discrepancies in these databases’ records. For instance, Sahebkar, A. has a high difference of 188%, with 883 publications recorded in Scopus versus 307 in Dimensions. Similarly, B. Larijani and P.I. Karakiewicz have 176% and 161% differences, respectively. On the other hand, authors like GYH Lip and C. Torp-Pedersen show comparatively lower discrepancies, with 65% and 54% differences. Notably, D.L. Bhatt shows a negative difference (−25%), indicating more Dimensions records than Scopus.
These disparities have implications for researchers’ perceived productivity and impact. They can affect their reputation, promotion, grant acquisition, and collaboration opportunities. Researchers must be aware of these differences, considering multiple databases when evaluating their publication record and influence in SDG 3 research.

4.7. Keyword Co-Occurrence Analysis

VOSviewer [45] has an advanced graphic presentation capability suitable for large-scale data and locates key points and hotspots in scientific research using density view [46]. The keywords co-occurrence network is instrumental in getting an overview of the assignment of keywords to clusters and how clusters of keywords are related.
Figure 8 and Figure 9 show the keyword’s co-occurrence network based on highly cited publications (n = 19,976) from Scopus and Dimensions. The colour coding of the clusters (see Figure 7 and Figure 8) and VOSviewer’s built-in function for viewing the list of keywords in each cluster were used to arrive at generalizations about the clusters to capture their overarching themes. Table 4 and Table 5 list the names of the themes derived based on the cluster keyword lists.
A comparative analysis of thematic clusters, as seen in Table 4 and Table 5, unveils distinct themes for the highly cited publications from each database.
Structural Differences: Table 4 has four thematic clusters based on the Dimensions database. Table 5 presents six clusters based on the Scopus database.
Cancer and Its Treatment: In Table 4, cancer-oriented keywords link with terms like “deep learning” and “machine learning,” hinting at the marriage of oncology with AI methodologies. Conversely, Table 5 hosts a more traditional take, grouping “cancer,” “chemotherapy,” and “biomarkers” in one cluster and segregating “machine learning” and “deep learning” into another, dedicated to technological advances.
Mental Health: Table 4 connects mental health concerns with overarching global events, including “pandemic” and “lockdown.” Table 5 provides a separate platform for mental health, clustering “depression”, “anxiety”, “stress”, and “loneliness” together, hinting at a deeper, standalone exploration of psychological well-being.
Chronic and Lifestyle Diseases: Both tables feature keywords like “diabetes”, “obesity”, and “hypertension.” However, their thematic placement differs. Table 4′s theme emphasizes preventive aspects, while Table 5 investigates causative factors, clubbing these terms with “oxidative stress” and “air pollution.”
Infectious Diseases: While Table 4 creates an integrated infectious disease theme, Table 5 carves out a niche for “COVID-19” and its related terms, demarcating it from other infectious terms like “influenza.”
Technological Innovations in Health: Table 4′s theme merges medical science with technology. In contrast, Table 5′s cluster around “machine learning” and “telehealth” indicates a broader applicability of technology in health sectors beyond just oncology.
In summary, Table 4, with its four clusters, opts for an integrative thematic approach, seeking intersections in research. In contrast, Table 5, with its six clusters, leans towards specialization, emphasizing distinct thematic buckets. Therefore, the choice between the two would depend on a researcher’s intent—to draw intersections or delve deep into specialized themes.

4.8. Citation Trends

RQ3 was to understand if there is a significant difference between citation counts of the same paper between Scopus and Dimensions. Analysis was performed on the highly cited papers (n = 19,976) from Scopus and Dimensions to understand the citation trends. The total number of common papers between Scopus and Dimensions was explored, and the mean of the common papers was calculated. A significant difference between citation counts of the same paper (n = 6548) between Scopus and Dimensions can be observed. The mean citation value of Scopus is 359.7, whereas that of Dimensions is higher, i.e., 434.5. This indicates higher citations for the same paper in Dimensions rather than Scopus. A similar observation was made by Harzing [24] in the study wherein it was observed that when compared to Scopus, Dimensions have similar or better coverage for both publications and citations.
To answer RQ4, we compute temporal trends in citation differences between Scopus and Dimensions for highly cited SDG 3 papers (n = 19,976) from 2018–2022, as seen in Figure 10. The citations of papers related to SDG 3 display a contrasting pattern. The Scopus database registers a greater overall citation count compared to Dimensions. The most significant variance in citation counts is noted in 2018, with Scopus showing a 72% higher count, followed by 2019 with a 68% difference. However, an exceptional scenario arose in 2020, where Dimensions exhibited a negative differential of −16% in total citations compared to Scopus, marking the only year when Dimensions surpassed Scopus in total citation percentage. Notably, 2020 also witnessed a remarkable surge in the citation count of SDG 3 publications within the Dimensions database, soaring by 160% compared to the previous year, 2019, most likely due to COVID-19 related publications.
Among the highly cited publications (n = 19,976), 13,428 do not appear in both databases. Table 6 displays the top ten highly-cited publications from each database that are not found in the other. The Dimensions database still predominantly features COVID-19-related studies, particularly those published in The Lancet, Cell, and Nature. These cover a range of subjects, including clinical features, epidemiology, genomic characterization, and molecular structure of the virus. Scopus’s top-cited papers include clinical guidelines for various health conditions and more specialized subjects such as extracellular vesicles and single-cell transcriptomics. The source titles vary notably, including Nature, The New England Journal of Medicine, and The European Heart Journal.

5. Conclusions

The study’s results explore the SDG 3-related research landscape between 2018 and 2022 from Scopus and Dimensions database. SDG 3 is taken as it dominates the most researched SDG literature [49]. The discrepancies in publications in SDG 3 were analysed. The study shows that Scopus consistently maps more publications than Dimensions. Scopus reports about 124% more publications mapped to SDG 3 than Dimensions, which contrasts with the findings that Dimensions have more article coverage than Scopus. The considerable discrepancy between Scopus and Dimensions in mapping publications to SDG 3 may have implications for policymakers who rely on these databases to inform decisions. If nations, funding bodies, or institutions are using this data to assess progress, monitor research contributions, or allocate resources, they might be presented with a skewed picture.
The pattern of disparities in SDG 3 mapped publications was studied at the country, funder, institution, journal, and author level. The discrepancy in the SDG 3 publication can be observed at the country level. The most significant percentage difference is seen in Japan and South Korea. The United States, which has the most publications, shows a comparatively lower percentage difference. Australia and India show relatively more minor differences between TPS and TPD publications. Disparities in SDG3 research output can make it challenging to identify and collaborate with researchers from other countries, lead to imbalances in power and influence, and make it challenging to ensure that the benefits of global collaborations are shared equitably.
For example, a researcher from a low-income country seeking to collaborate with peers from high-income countries on a climate change and human health study may encounter challenges. Due to lower research output on SDG3-related topics in low-income countries, finding collaborators could be difficult. Even if collaboration is established, high-income country researchers will likely exert more significant influence over decision-making and resource allocation.
A substantial predominance of TPS over TPD publications is seen in the publications of the National Institute of Health (United States) and the National Research Foundation of Korea. Other institutions like the National Heart, Lung, and Blood Institute (United States), the Medical Research Council (United States), and the National Center for Advancing Translational Sciences (United States) have relatively lower differences between TPS and TPD. Only the European Commission (Belgium) has more TPD publications than TPS. Harvard University and the University of Toronto lead the difference. For institutions, especially academic ones, such discrepancies can affect their perceived contribution to the SDGs. Rankings and recognitions that rely on publication counts could be unfairly tilted depending on the indexing database consulted. Funding bodies that prioritize SDG 3 might be led to believe there is either a surplus or a need for more research in the domain based on the indexing database they refer to. This can affect their decisions on where to allocate funds, potentially leading to overfunding certain areas or neglecting others.
Earlier studies have demonstrated that the Dimensions database has the most exhaustive journal coverage, with 82.22% more journals than Web of Science and 48.17% more than Scopus. A different scenario emerges in this study, and total publications in Scopus in SDG 3 are more than in Dimensions. The International Journal of Molecular Sciences has the highest percentage difference at the journal level, indicating a significant predominance of TPS publications over TPD ones. The other high TPS publications in TPD are Cancers and Frontiers in Neurology. Given the differences noted across journals, there is a potential opportunity for editorial boards and publishers to understand and possibly rectify the alignment of their publications with SDGs. They can play a pivotal role in ensuring that research that significantly contributes to SDGs is correctly recognized.
Discrepancies can be seen at the author level also. The findings stress the importance for researchers and stakeholders to be conscious of their choice of indexing database. It underlines the need for transparency in mapping methodologies and possible database standardization to ensure consistent data representation.
The different mapping techniques used by Scopus and Dimensions may be the reason for the discrepancies at the country, funding agency, journal, and author levels. The mapping of various initiatives’ publications to their respective SDGs varies, according to comparative evaluations of SDG mapping initiatives, as highlighted in the study by Jayablasingham [31]. So, depending upon which SDG mapping is chosen, the number of publications based on University, Country, or Author may go up or down, which is a grave concern.
Keyword co-occurrence analysis of data from Dimensions reveals four clusters with an integrative thematic approach, seeking intersections in research. At the same time, data analysis from Scopus highlights six clusters and leans towards specialization, emphasizing distinct thematic buckets. Temporal trends in citation differences between Scopus and Dimensions for SDG 3 publications indicate an edge for Dimensions.
In summary, while the aim to map research publications to the SDGs is useful, the discrepancies observed in SDG 3 mapping under the two largest abstract indexing databases underline the necessity for a more harmonized approach, especially as the 2030 target year for the SDGs approaches. The stark difference between Scopus and Dimensions begs the question of similar discrepancies in mapping other SDGs. Multiple SDG-related research initiatives may lead to inconsistencies in the rankings of journals, institutions, and funders. These discrepancies make meaningful comparisons difficult, emphasizing the necessity of consistent methods and cooperation. Stakeholders at every level, from individual researchers to global organizations, must be aware of these disparities and their implications.
The present study has certain limitations. Overall, it is important to be cautious when comparing the outputs from the two different strategies used by Scopus and Dimensions to map research publications to SDGs. There are a number of factors that can affect the results of such comparisons, including the different taxonomies used by the two tools, the different coverage of research publications, and the constantly evolving search strategies used by the two tools. The present study is constrained to Scopus and Dimensions database for the literature search. Though Scopus and Dimensions may have overlapping publications, many relevant articles outside these two databases need to be included in the future. A broader analysis might be required to ascertain the reliability of these databases in representing research contributions to all the SDGs. A more comprehensive study can be carried out by including more databases like Web of Science and Google Scholar. A more targeted investigation may be performed by examining specific papers that Scopus indexes rather than by Dimensions to understand better the discrepancies between Scopus and Dimensions in paper identification. Another limitation of the study is that all types of publications, such as notes, letters, reviews, and editorials, were included in the analysis. However, these types might not represent original research contributions. Despite all this, the authors believe that the scientific community, policymakers, and other stakeholders will be assisted in a better understanding of the mapping of publications to SDGs through this study.

Author Contributions

Conceptualization, R.R.; methodology, R.R.; software, V.K.N.; data curation, V.K.N.; writing—original draft preparation, R.R. and P.N.; writing—review and editing, R.R., V.K.N. and P.N.; visualization, R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sachs, J.D. From millennium development goals to sustainable development goals. Lancet 2012, 379, 2206–2211. [Google Scholar] [CrossRef] [PubMed]
  2. UN. Transforming Our World: The 2030 Agenda for Sustainable Development; Resolution Adopted by the General Assembly on 25 September 2015; UN: New York, NY, USA, 2015.
  3. de Jong, E.; Vijge, M.J. From Millennium to Sustainable Development Goals: Evolving discourses and their reflection in policy coherence for development. Earth Syst. Gov. 2021, 7, 100087. [Google Scholar] [CrossRef]
  4. Fonseca, L.; Carvalho, F. The reporting of SDGs by quality, environmental, and occupational health and safety-certified organizations. Sustainability 2019, 11, 5797. [Google Scholar] [CrossRef]
  5. Schmalzbauer, B.S. (Ed.) The Contribution of Science in Implementing the Sustainable Development Goals; German Committee Future Earth: Hamburg, Germany, 2016. [Google Scholar]
  6. Raman, R.; Nair, V.K.; Shivdas, A.; Bhukya, R.; Viswanathan, P.K.; Subramaniam, N.; Nedungadi, P. Mapping sustainability reporting research with the UN’s sustainable development goal. Heliyon 2023, 9, e18510. [Google Scholar] [CrossRef] [PubMed]
  7. Sreenivasan, A.; Ma, S.; Nedungadi, P.; Sreedharan, V.R.; Raman, R.R. Interpretive Structural Modeling: Research Trends, Linkages to Sustainable Development Goals, and Impact of COVID-19. Sustainability 2023, 15, 4195. [Google Scholar] [CrossRef]
  8. Armitage, C.S.; Lorenz, M.; Mikki, S. Mapping scholarly publications related to the Sustainable Development Goals: Do independent bibliometric approaches get the same results? Quant. Sci. Stud. 2020, 1, 1092–1108. [Google Scholar] [CrossRef]
  9. Balsiger, J.; Förster, R.; Mader, C.; Nagel, U.; Sironi, H.; Wilhelm, S.; Zimmermann, A.B. Transformative learning and education for sustainable development. GAIA-Ecol. Perspect. Sci. Soc. 2017, 26, 357–359. [Google Scholar] [CrossRef]
  10. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef]
  11. Github. (n.d.). Aurora-Network-Global’s SDG-Queries. Available online: https://github.com/Aurora-Network-Global/sdg-queries (accessed on 13 July 2023).
  12. Auckland’s SDG Mapping (n.d). University of Auckland’s SDG Mapping Initiative. Available online: https://www.sdgmapping.auckland.ac.nz/ (accessed on 13 July 2023).
  13. Mohammed, A.J.; Ghebreyesus, T.A. Healthy living, well-being and the sustainable development goals. Bull. World Health Organ. 2018, 96, 590. [Google Scholar] [CrossRef]
  14. Filho, W.L.; Brandli, L.L.; Salvia, A.L.; Rayman-Bacchus, L.; Platje, J. COVID-19 and the UN sustainable development goals: Threat to solidarity or an opportunity? Sustainability 2020, 12, 5343. [Google Scholar] [CrossRef]
  15. Bhatia, R.; Khetrapal, S. Impact of COVID-19 pandemic on health system & sustainable development goal 3. Indian J. Med. Res. 2020, 151, 395–399. [Google Scholar] [CrossRef]
  16. Ali, S.M.; Appolloni, A.; Cavallaro, F.; D’adamo, I.; Di Vaio, A.; Ferella, F.; Gastaldi, M.; Ikram, M.; Kumar, N.M.; Martin, M.A.; et al. Development Goals towards Sustainability. Sustainability 2023, 15, 9443. [Google Scholar] [CrossRef]
  17. Blasco, N.; Brusca, I.; Labrador, M. Drivers for universities’ contribution to the sustainable development goals: An analysis of spanish public universities. Sustainability 2020, 13, 89. [Google Scholar] [CrossRef]
  18. Roberge, G.; Kashnitsky, Y.; James, C. Elsevier 2022 Sustainable Development Goals (SDG) Mapping. Mendeley Data 2022, 1, 2022. [Google Scholar]
  19. Olawumi, T.O.; Chan, D.W. A scientometric review of global research on sustainability and sustainable development. J. Clean. Prod. 2018, 183, 231–250. [Google Scholar] [CrossRef]
  20. Meschede, C. The sustainable development goals in scientific literature: A bibliometric overview at the meta-level. Sustainability 2020, 12, 4461. [Google Scholar] [CrossRef]
  21. Tilbury, D. Higher education for sustainability: A global overview of commitment and progress. High. Educ. World 2011, 4, 18–28. [Google Scholar]
  22. Körfgen, A.; Förster, K.; Glatz, I.; Maier, S.; Becsi, B.; Meyer, A.; Kromp-Kolb, H.; Stötter, J. It’s a hit! Mapping austrian research contributions to the sustainable development goals. Sustainability 2018, 10, 3295. [Google Scholar] [CrossRef]
  23. Thelwall, M. Dimensions: A competitor to Scopus and the Web of Science? J. Inf. 2018, 12, 430–435. [Google Scholar] [CrossRef]
  24. Harzing, A.-W. Two new kids on the block: How do Crossref and Dimensions compare with Google Scholar, Microsoft Academic, Scopus and the Web of Science? Scientometrics 2019, 120, 341–349. [Google Scholar] [CrossRef]
  25. Kar, S.K.; Harichandan, S. Green marketing innovation and sustainable consumption: A bibliometric analysis. J. Clean. Prod. 2022, 361, 132290. [Google Scholar] [CrossRef]
  26. Chistov, V.; Aramburu, N.; Carrillo-Hermosilla, J. Open eco-innovation: A bibliometric review of emerging research. J. Clean. Prod. 2021, 311, 127627. [Google Scholar] [CrossRef]
  27. Harichandan, S.; Kar, S.K.; Bansal, R.; Mishra, S.K.; Balathanigaimani, M.S.; Dash, M. Energy transition research: A bibliometric mapping of current findings and direction for future research. Clean. Prod. Lett. 2022, 3, 100026. [Google Scholar] [CrossRef]
  28. Visser, M.; van Eck, N.J.; Waltman, L. Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. Quant. Sci. Stud. 2021, 2, 20–41. [Google Scholar] [CrossRef]
  29. Mongeon, P.; Paul-Hus, A. The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics 2016, 106, 213–228. [Google Scholar] [CrossRef]
  30. Gavel, Y.; Iselid, L. Web of Science and Scopus: A journal title overlap study. Online Inf. Rev. 2008, 32, 8–21. [Google Scholar] [CrossRef]
  31. Jayabalasingham, B.; Boverhof, R.; Agnew, K.; Klein, L. Identifying research supporting the United Nations Sustainable Development Goals. Mendeley Data 2019, 1. [Google Scholar] [CrossRef]
  32. Raman, R.; Subramaniam, N.; Nair, V.K.; Shivdas, A.; Achuthan, K.; Nedungadi, P. Women entrepreneurship and sustainable development: Bibliometric analysis and emerging research trends. Sustainability 2022, 14, 9160. [Google Scholar] [CrossRef]
  33. Achuthan, K.; Nair, V.K.; Kowalski, R.; Ramanathan, S.; Raman, R. Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput. Hum. Behav. 2023, 140, 107566. [Google Scholar] [CrossRef]
  34. Fonseca, L.M.; Domingues, J.P.; Dima, A.M. Mapping the sustainable development goals relationships. Sustainability 2020, 12, 3359. [Google Scholar] [CrossRef]
  35. Dibbern, T.A.; Rampasso, I.S.; Serafim, M.P.; Bertazzoli, R.; Filho, W.L.; Anholon, R. Bibliometric study on SDG 6: Analysing main content aspects by using Web of Science data from 2015 to 2021. Kybernetes 2022. [Google Scholar] [CrossRef]
  36. Steingard, D.; Balduccini, M.; Sinha, A. Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs). AI Soc. 2023, 38, 613–629. [Google Scholar] [CrossRef]
  37. Bautista-Puig, N.; Orduña-Malea, E.; Perez-Esparrells, C. Enhancing sustainable development goals or promoting universities? An analysis of the times higher education impact rankings. Int. J. Sustain. High. Educ. 2022, 23, 211–231. [Google Scholar] [CrossRef]
  38. Wang, R.-J.; Shih, Y.-H. What are universities pursuing? A review of the Quacquarelli Symonds world university rankings of Taiwanese universities (2021–2023). Front. Educ. 2023, 8, 1185817. [Google Scholar] [CrossRef]
  39. Park, J.; Savelyeva, T. An interpretive analysis of the 2030 sustainable development goals in Hong Kong public universities. Asia Pac. Educ. Rev. 2022, 23, 543–558. [Google Scholar] [CrossRef]
  40. Raman, R.; Vinuesa, R.; Nedungadi, P. Bibliometric analysis of SARS, MERS, and COVID-19 studies from India and connection to sustainable development goals. Sustainability 2021, 13, 7555. [Google Scholar] [CrossRef]
  41. Raji, S.A.; Demehin, M.O. “Long walk to 2030”: A bibliometric and systematic review of research trends on the UN sustainable development goal 3. Dialogues Health 2023, 2, 100132. [Google Scholar] [CrossRef]
  42. Schmidt, F.; Vanderfeesten, M. Evaluation on accuracy of mapping science to the United Nations’ Sustainable Development Goals (SDGs) of the Aurora SDG queries. Zenodo 2021. [Google Scholar] [CrossRef]
  43. Nishikawa-Pacher, A. Who are the 100 largest scientific publishers by journal count? A webscraping approach. J. Doc. 2022, 78, 450–463. [Google Scholar] [CrossRef]
  44. Siler, K.; Vincent-Lamarre, P.; Sugimoto, C.R.; Larivière, V. Predatory publishers’ latest scam: Bootlegged and rebranded papers. Nature 2021, 598, 563–565. [Google Scholar] [CrossRef]
  45. Van Eck, N.J.; Waltman, L. Visualizing bibliometric networks. In Measuring Scholarly Impact: Methods and Practice; Springer International Publishing: Cham, Switzerland, 2014; pp. 285–320. [Google Scholar] [CrossRef]
  46. Radhakrishnan, S.; Erbis, S.; Isaacs, J.A.; Kamarthi, S. Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE 2017, 12, e0185771. [Google Scholar] [CrossRef]
  47. Pakkan, S.; Sudhakar, C.; Tripathi, S.; Rao, M. A correlation study of sustainable development goal (SDG) interactions. Qual. Quant. 2023, 57, 1937–1956. [Google Scholar] [CrossRef] [PubMed]
  48. Wastl, J. 6.2 Expanding Dimensions: A New Source in the Bibliometrician’s Toolbox. In Handbook Bibliometrics; Walter de Gruyter GmbH & Co KG: Berlin, Germany, 2020; Volume 421. [Google Scholar]
  49. Sweileh, W.M. Bibliometric analysis of scientific publications on “sustainable development goals” with emphasis on “good health and well-being” goal (2015–2019). Glob. Health 2020, 16, 68. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. PRISMA Framework.
Figure 1. PRISMA Framework.
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Figure 2. SDG 3 Publication trends.
Figure 2. SDG 3 Publication trends.
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Figure 3. SDG 3 Publications based on Prolific Countries (n = 15).
Figure 3. SDG 3 Publications based on Prolific Countries (n = 15).
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Figure 4. SDG 3 Publications based on Top Funders (n = 15).
Figure 4. SDG 3 Publications based on Top Funders (n = 15).
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Figure 5. SDG 3 Publications based on Productive institutions (n = 15).
Figure 5. SDG 3 Publications based on Productive institutions (n = 15).
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Figure 6. SDG 3 Publications based on Top journals (n = 15).
Figure 6. SDG 3 Publications based on Top journals (n = 15).
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Figure 7. SDG 3 Publications based on Influential authors (n = 15).
Figure 7. SDG 3 Publications based on Influential authors (n = 15).
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Figure 8. Keywords co-occurrence network (Dimensions database).
Figure 8. Keywords co-occurrence network (Dimensions database).
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Figure 9. Keywords co-occurrence network (Scopus database).
Figure 9. Keywords co-occurrence network (Scopus database).
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Figure 10. Temporal trends in citation differences between Scopus and Dimensions.
Figure 10. Temporal trends in citation differences between Scopus and Dimensions.
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Table 1. Largest academic journal publishers (in alphabetical order).
Table 1. Largest academic journal publishers (in alphabetical order).
No.Publisher Name
1American Chemical Society
2British Medical Journal
3Bio Med Central
4Cambridge University Press
5De Gruyter
6Elsevier
7Emerald
8Frontiers
9Hindawi
10IGI Global
11IOP Publishing
12Inderscience Publishers
13Institute of Electrical and Electronics Engineers
14MDPI
15OMICS International
16Oxford University Press
17Public Library of Science
18SAGE Publications
19Science Publishing Group
20Springer Nature
21Taylor & Francis
22Wiley
23Wolters Kluwer
Table 2. Search queries executed on Scopus and Dimensions database.
Table 2. Search queries executed on Scopus and Dimensions database.
Search Query—Scopus (n = 3,021,627)
TITLE-ABS-KEY (((human AND (health* OR disease* OR illness* OR medicine* OR mortality)) OR {battered child syndrome} OR {cardiovascular disease} OR {cardiovascular diseases} OR {chagas} OR {child abuse} OR {child neglect} OR {child well-being index} OR {youth well-being index} OR {child wellbeing index} OR {youth wellbeing index} OR {water-borne disease} OR {water-borne diseases} OR {water borne disease} OR {water borne diseases} OR {tropical disease} OR {tropical diseases} OR {chronic respiratory disease} OR {chronic respiratory diseases} OR {infectious disease} OR {infectious diseases} OR {sexually-transmitted disease} OR {sexually transmitted disease} OR {sexually-transmitted diseases} OR {sexually transmitted diseases} OR {communicable disease} OR {communicable diseases} OR aids OR hiv OR {human immunodeficiency virus} OR tuberculosis OR malaria OR hepatitis OR polio* OR vaccin* OR cancer* OR diabet* OR {maternal mortality} OR {child mortality} OR {childbirth complications} OR {neonatal mortality} OR {neo-natal mortality} OR {premature mortality} OR {infant mortality} OR {quality adjusted life year} OR {maternal health} OR {preventable death} OR {preventable deaths} OR {tobacco control} OR {substance abuse} OR {drug abuse} OR {tobacco use} OR {alcohol use} OR {substance addiction} OR {drug addiction} OR {tobacco addiction} OR alcoholism OR suicid* OR {postnatal depression} OR {post-natal depression} OR {zika virus} OR dengue OR schistosomiasis OR {sleeping sickness} OR ebola OR {mental health} OR {mental disorder} OR {mental illness} OR {mental illnesses} OR {measles} OR {neglected disease} OR {neglected diseases} OR diarrhea OR diarrhoea OR cholera OR dysentery OR {typhoid fever} OR {traffic accident} OR {traffic accidents} OR {healthy lifestyle} OR {life expectancy} OR {life expectancies} OR {health policy} OR ({health system} AND (access OR accessible)) OR {health risk} OR {health risks} OR {inclusive health} OR obesity OR {social determinants of health} OR {psychological harm} OR {psychological wellbeing} OR {psychological well-being} OR {psychological well-being} OR {public health})) and (publisher(american chemical*) OR publisher(biomed*) OR (bmj*) OR (british medical*) OR publisher(cambridge*) OR publisher(*gruyter*) OR publisher(elsevi*) OR publisher(emerald*) OR publisher(frontier*) OR publisher(hindawi*) OR publisher(igi global*) OR publisher(iop*) OR publisher(inderscience*) OR publisher(Institute of Electrical and Electronics Engineers) OR publisher(ieee*) OR publisher(MDPI*) OR (multi-disciplinary*) OR publisher(nature*) OR publisher(omics*) OR publisher(oxford university*) OR publisher(public library*) OR publisher(routledge*) OR publisher(sage*) OR publisher(science publishing group*) OR publisher(Springer*) OR publisher(nature*) OR publisher(taylor*) OR publisher(wiley*) OR publisher(*wolters*)) AND (LIMIT-TO (DOCTYPE, ”ar”) OR LIMIT-TO (DOCTYPE, ”ch”) OR LIMIT-TO (DOCTYPE, ”re”) OR LIMIT-TO (DOCTYPE, ”cp”) OR LIMIT-TO (DOCTYPE, ”bk”) OR LIMIT-TO (DOCTYPE, ”no”) OR LIMIT-TO (DOCTYPE, ”ed”) OR LIMIT-TO (DOCTYPE, ”le”) OR LIMIT-TO (DOCTYPE, ”dp”) OR LIMIT-TO (DOCTYPE, ”sh”)) AND (LIMIT-TO (PUBYEAR,2022) OR LIMIT-TO (PUBYEAR,2021) OR LIMIT-TO (PUBYEAR,2020) OR LIMIT-TO (PUBYEAR,2019) OR LIMIT-TO (PUBYEAR,2018)) AND (LIMIT-TO (LANGUAGE, ”English”))
Search Query—Dimensions (n = 1,348,969)
https://app.dimensions.ai/discover/publication?search_mode=content&search_text=language%3Aen&search_type=kws&search_field=full_search&order=times_cited&or_facet_year=2022&or_facet_year=2021&or_facet_year=2020&or_facet_year=2019&or_facet_year=2018&or_facet_user_group_facet=f5d5ce00-20ab-42df-9e87-29a09b90853e&or_facet_publication_type=article&or_facet_publication_type=chapter&or_facet_publication_type=proceeding&or_facet_publication_type=book&or_facet_sdg=40003 (accessed on 13 July 2023)
IF (((STARTSWITH([Code ASJC], ”27”) OR STARTSWITH([Code ASJC], ”29”) OR STARTSWITH([Code ASJC], ”35”) OR STARTSWITH([Code ASJC], ”36”) OR STARTSWITH([Code ASJC], ”24”) OR STARTSWITH([Code ASJC], ”28”) OR STARTSWITH([Code ASJC], ”30”) OR STARTSWITH([Code ASJC], ”32”) OR [Code ASJC]=“3306”) OR (STARTSWITH([Code ASJC], ”32”) AND (CONTAINS([TI-ABS-KW], “hospital”) OR CONTAINS([TI-ABS-KW], “medical staff”) OR CONTAINS([TI-ABS-KW], “doctor”) OR CONTAINS([TI-ABS-KW], “nurse”) OR CONTAINS([TI-ABS-KW], “physician”) OR CONTAINS([TI-ABS-KW], “health”))) OR (CONTAINS([TI-ABS-KW], “good health and well-being”) OR (CONTAINS([TI-ABS-KW], “human”) AND (CONTAINS([TI-ABS-KW], ”health”) OR CONTAINS([TI-ABS-KW], “disease”) OR CONTAINS([TI-ABS-KW], “illness”) OR CONTAINS([TI-ABS-KW], “medicine”) OR CONTAINS([TI-ABS-KW], “mortality”))) OR CONTAINS([TI-ABS-KW], “battered child syndrome”) OR CONTAINS([TI-ABS-KW], “cardiovascular disease”) OR CONTAINS([TI-ABS-KW], “cardiovascular diseases”) OR CONTAINS([TI-ABS-KW], “chagas”) OR CONTAINS([TI-ABS-KW], “child abuse”) OR CONTAINS([TI-ABS-KW], “child neglect”) OR CONTAINS([TI-ABS-KW], “human wellbeing”) OR CONTAINS([TI-ABS-KW], “human well-being”) OR CONTAINS([TI-ABS-KW], “youth wellbeing”) OR CONTAINS([TI-ABS-KW], “youth well-being”) OR CONTAINS([TI-ABS-KW], “child wellbeing”) OR CONTAINS([TI-ABS-KW], “child well-being”) OR CONTAINS([TI-ABS-KW], “woman wellbeing”) OR CONTAINS([TI-ABS-KW], “woman well-being”) OR CONTAINS([TI-ABS-KW], “women wellbeing”) OR CONTAINS([TI-ABS-KW], “women well-being”) OR CONTAINS([TI-ABS-KW], “children wellbeing”) OR CONTAINS([TI-ABS-KW], “children well-being”) OR CONTAINS([TI-ABS-KW], “wellbeing of children”) OR CONTAINS([TI-ABS-KW], “well-being of children”) OR CONTAINS([TI-ABS-KW], “health of children”) OR CONTAINS([TI-ABS-KW], “children health”) OR CONTAINS([TI-ABS-KW], “wellbeing of women”) OR CONTAINS([TI-ABS-KW], “well-being of women”) OR CONTAINS([TI-ABS-KW], “health of women”) OR CONTAINS([TI-ABS-KW], “women health”) OR CONTAINS([TI-ABS-KW], “wellbeing of youth”) OR CONTAINS([TI-ABS-KW], “well-being of youth”) OR CONTAINS([TI-ABS-KW], “health of youth”) OR CONTAINS([TI-ABS-KW], “youth health”) OR CONTAINS([TI-ABS-KW], “young people’s health”) OR CONTAINS([TI-ABS-KW], “young people health”) OR CONTAINS([TI-ABS-KW], “water-borne disease”) OR CONTAINS([TI-ABS-KW], “water-borne diseases”) OR CONTAINS([TI-ABS-KW], “water borne disease”) OR CONTAINS([TI-ABS-KW], “water borne diseases”) OR CONTAINS([TI-ABS-KW], “tropical disease”) OR CONTAINS([TI-ABS-KW], “tropical diseases”) OR CONTAINS([TI-ABS-KW], “chronic respiratory disease”) OR CONTAINS([TI-ABS-KW], “chronic respiratory diseases”) OR CONTAINS([TI-ABS-KW], “infectious disease”) OR CONTAINS([TI-ABS-KW], “infectious diseases”) OR CONTAINS([TI-ABS-KW], “sexually-transmitted disease”) OR CONTAINS([TI-ABS-KW], “sexually transmitted disease”) OR CONTAINS([TI-ABS-KW], “sexually-transmitted diseases”) OR CONTAINS([TI-ABS-KW], “sexually transmitted diseases”) OR CONTAINS([TI-ABS-KW], “communicable disease”) OR CONTAINS([TI-ABS-KW], “communicable diseases”) OR CONTAINS([TI-ABS-KW], “patient with aids”) OR CONTAINS([TI-ABS-KW], “people with aids”) OR CONTAINS([TI-ABS-KW], “with hiv”) OR CONTAINS([TI-ABS-KW], ”hiv virus”) OR CONTAINS([TI-ABS-KW], “hiv/aids”) OR CONTAINS([TI-ABS-KW], “human immunodeficiency virus”) OR CONTAINS([TI-ABS-KW], “tuberculosis”) OR CONTAINS([TI-ABS-KW], “malaria”) OR CONTAINS([TI-ABS-KW], “hepatitis”) OR CONTAINS([TI-ABS-KW], “polio”) OR CONTAINS([TI-ABS-KW], “vaccin”) OR CONTAINS([TI-ABS-KW], “cancer”) OR CONTAINS([TI-ABS-KW], “diabet”) OR CONTAINS([TI-ABS-KW], “maternal mortality”) OR CONTAINS([TI-ABS-KW], “child mortality”) OR CONTAINS([TI-ABS-KW], “childbirth complications”) OR CONTAINS([TI-ABS-KW], “neonatal mortality”) OR CONTAINS([TI-ABS-KW], “neo-natal mortality”) OR CONTAINS([TI-ABS-KW], “premature mortality”) OR CONTAINS([TI-ABS-KW], “infant mortality”)) OR (CONTAINS([TI-ABS-KW], “quality adjusted life year”) OR CONTAINS([TI-ABS-KW], “maternal health”) OR CONTAINS([TI-ABS-KW], “reproductive health”) OR CONTAINS([TI-ABS-KW], “sexual health”) OR CONTAINS([TI-ABS-KW], “preventable death”) OR CONTAINS([TI-ABS-KW], “preventable deaths”) OR CONTAINS([TI-ABS-KW], “tobacco control”) OR CONTAINS([TI-ABS-KW], “substance abuse”) OR CONTAINS([TI-ABS-KW], “drug abuse”) OR CONTAINS([TI-ABS-KW], “tobacco use”) OR CONTAINS([TI-ABS-KW], “alcohol use”) OR CONTAINS([TI-ABS-KW], “substance addiction”) OR CONTAINS([TI-ABS-KW], “drug addiction”) OR CONTAINS([TI-ABS-KW], “tobacco addiction”) OR CONTAINS([TI-ABS-KW], “alcoholism”) OR CONTAINS([TI-ABS-KW], “suicid”) OR CONTAINS([TI-ABS-KW], “postnatal depression”) OR CONTAINS([TI-ABS-KW], “post-natal depression”) OR CONTAINS([TI-ABS-KW], “zika virus”) OR CONTAINS([TI-ABS-KW], “dengue”) OR CONTAINS([TI-ABS-KW], “schistosomiasis”) OR CONTAINS([TI-ABS-KW], “sleeping sickness”) OR CONTAINS([TI-ABS-KW], “ebola”) OR CONTAINS([TI-ABS-KW], “mental health”) OR CONTAINS([TI-ABS-KW], “mental disorder”) OR CONTAINS([TI-ABS-KW], “mental illness”) OR CONTAINS([TI-ABS-KW], “mental illnesses”) OR CONTAINS([TI-ABS-KW], “measles”) OR CONTAINS([TI-ABS-KW], “neglected disease”) OR CONTAINS([TI-ABS-KW], “neglected diseases”) OR CONTAINS([TI-ABS-KW], “diarrhea”) OR CONTAINS([TI-ABS-KW], “diarrhoea”) OR CONTAINS([TI-ABS-KW], “cholera”) OR CONTAINS([TI-ABS-KW], “dysentery”) OR CONTAINS([TI-ABS-KW], “typhoid fever”) OR CONTAINS([TI-ABS-KW], “traffic accident”) OR CONTAINS([TI-ABS-KW], “traffic accidents”) OR CONTAINS([TI-ABS-KW], “healthy lifestyle”) OR CONTAINS([TI-ABS-KW], “life expectancy”) OR CONTAINS([TI-ABS-KW], “life expectancies”) OR CONTAINS([TI-ABS-KW], “health policy”) OR (CONTAINS([TI-ABS-KW], “health system”) AND (CONTAINS([TI-ABS-KW], ”access”) OR CONTAINS([TI-ABS-KW], “accessible”))) OR CONTAINS([TI-ABS-KW], “health risk”) OR CONTAINS([TI-ABS-KW], “health risks”) OR CONTAINS([TI-ABS-KW], “inclusive health”) OR CONTAINS([TI-ABS-KW], “obesity”) OR CONTAINS([TI-ABS-KW], “coronavirus”) OR CONTAINS([TI-ABS-KW], “covid-19”) OR CONTAINS([TI-ABS-KW], “covid 19”) OR CONTAINS([TI-ABS-KW], “social determinants of health”) OR CONTAINS([TI-ABS-KW], “psychological harm”) OR CONTAINS([TI-ABS-KW], “psychological wellbeing”) OR CONTAINS([TI-ABS-KW], “psychological well-being”) OR CONTAINS([TI-ABS-KW], “psychological wellbeing”) OR CONTAINS([TI-ABS-KW], “public health”) OR CONTAINS([TI-ABS-KW], “telemedicine”) OR CONTAINS([TI-ABS-KW], “telecare”) OR CONTAINS([TI-ABS-KW], “health reason”) OR CONTAINS([TI-ABS-KW], “healthcare”)))) THEN “SDG 3” ELSE “0” END
Table 3. Total number of publications assessed (Corpus).
Table 3. Total number of publications assessed (Corpus).
PublicationsTotal Publications Scopus (TPS)

2018-22		Total Publications Dimensions (TPD)

2018-22		% Difference (TPS over TPD)


Total SDG 3 mapped Publications 4,039,6812,147,56588%
Total SDG 3 mapped Publications
(Records screened based on publishers) 		3,021,6271,348,969124%
Table 4. Thematic clusters based on keyword co-occurrence (Dimensions database).
Table 4. Thematic clusters based on keyword co-occurrence (Dimensions database).
Cluster Top 15 KeywordsCluster Theme
1
red		cancer, immunotherapy, diagnosis, biomarkers, deep learning, tumor microenvironment, chemotherapy, machine learning, biomarker, drug delivery, metastasis, artificial intelligence, exosomes, autophagy, therapyOncology and Therapeutic Technological Advancements
2
yellow		Covid-19, Sars-Cov-2, coronavirus, pandemic, public health, pneumonia, 2019-ncov, vaccine, pathogenesis, ace2, outbreak, coronavirus disease 2019, vitamin D, cytokine storm, epidemicSARS-CoV-2 Pathogenesis, Prevention & Public Health
3
green		obesity, diabetes, obesity, cardiovascular disease, heart failure, guidelines, physical activity, hypertension, pregnancy, stroke, diabetes mellitus, atherosclerosis, exercise, myocardial infarction, microbiota, insulin resistanceMetabolic and Cardiovascular Disorders & Therapies
4
blue		epidemiology, depression, mental health, anxiety, mortality, meta-analysis, treatment, risk factors, stress, systematic review, prevalence, incidence, prevention, review, childrenMental Health Epidemiology & Treatment Modalities
Table 5. Thematic clusters based on keyword co-occurrence (Scopus database).
Table 5. Thematic clusters based on keyword co-occurrence (Scopus database).
ClusterTop 15 KeywordsCluster Theme
1
red		inflammation, epidemiology, cancer, diagnosis, immunotherapy, treatment, biomarkers, prognosis, biomarker, sepsis, microbiome, pathogenesis, genetics, therapeutics, personalized medicineDisease Pathogenesis & Treatment Modalities
2
green		depression, anxiety, mental health, meta-analysis, systematic review, stress, public health, physical activity, children, lockdown, adolescents, sleep, aging, quarantine, quality of lifeMental Health & Well-being
3
blue		mortality, cardiovascular disease, obesity, diabetes, hypertension, risk factors, heart failure, prevention, prevalence, stroke, atherosclerosis, cardiovascular diseases, blood pressure, incidence, air pollutionCardio-metabolic & Lifestyle Diseases
4
yellow		Covid-19, Sars-Cov-2, vaccine, ace2, vaccines, immunity, vaccination, spike protein, antibodies, cytokines, cytokine storm, immune response, antibody, neutralization, serologyCOVID-19 & Immunity
5
pink		coronavirus, pandemic, pregnancy, pneumonia, infection, sars-cov, sars, coronavirus disease 2019, severe acute respiratory syndrome coronavirus 2, 2019-ncov, transmission, epidemic, mers-cov, novel coronavirus, critical care Viral Diseases & Patient Care
6
Turquoise 		machine learning, deep learning, artificial intelligence, telemedicine, pandemics, healthcare, prediction, telehealth, big data, classification, radionics, blockchain, convolutional neural network, internet of things, health Digital Health & AI in Medicine
Table 6. Top ten highly cited publications from each database that are not found in the other.
Table 6. Top ten highly cited publications from each database that are not found in the other.
Publication Title (Dimensions)Source TitleTCDPublication Title (Scopus)Source TitleTCS
Clinical features of patients infected with the 2019 novel coronavirus in Wuhan, ChinaThe Lancet36,607PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanationNature9591
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive studyThe Lancet15,880Dexamethasone in hospitalized patients with Covid-19Annals of Internal Medicine6013
SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease InhibitorCell15,1602017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevationNew England Journal of Medicine5913
Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor bindingThe Lancet91252018 ESC/ESH Guidelines for the management of arterial hypertensionEuropean Heart Journal5500
The REDCap consortium: Building an international community of software platform partnersJournal of Biomedical Informatics8467Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelinesEuropean Heart Journal5192
A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family clusterThe Lancet6943Sarcopenia: Revised European consensus on definition and diagnosisJournal of Extracellular Vesicles4998
Structure, Function, and Antigenicity of the SARS-CoV-2 Spike GlycoproteinCell6848Heart disease and stroke statistics—2018 update: A report from the American Heart AssociationAge and Ageing4734
Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptorNature4624Integrating single-cell transcriptomic data across different conditions, technologies, and speciesCirculation4685
Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumoniaJournal of Thrombosis and Haemostasis4478EASL Clinical Practice Guidelines: Management of hepatocellular carcinomaNature Biotechnology4619
Note: TCD = Total Citations in Dimensions, TCS = Total Citations in Scopus.
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Raman, R.; Nair, V.K.; Nedungadi, P. Discrepancies in Mapping Sustainable Development Goal 3 (Good Health and Well-Being) Research: A Comparative Analysis of Scopus and Dimensions Databases. Sustainability 2023, 15, 16413. https://doi.org/10.3390/su152316413

AMA Style

Raman R, Nair VK, Nedungadi P. Discrepancies in Mapping Sustainable Development Goal 3 (Good Health and Well-Being) Research: A Comparative Analysis of Scopus and Dimensions Databases. Sustainability. 2023; 15(23):16413. https://doi.org/10.3390/su152316413

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Raman, Raghu, Vinith Kumar Nair, and Prema Nedungadi. 2023. "Discrepancies in Mapping Sustainable Development Goal 3 (Good Health and Well-Being) Research: A Comparative Analysis of Scopus and Dimensions Databases" Sustainability 15, no. 23: 16413. https://doi.org/10.3390/su152316413

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