Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization

Near-infrared photoimmunotherapy in cancer treatment: a bibliometric and visual analysis

Background

Near-infrared photoimmunotherapy (NIR-PIT) is an emerging cancer treatment technology that combines the advantages of optical technology and immunotherapy to provide a highly effective, precise, and low side-effect treatment approach. The aim of this study is to visualize the scientific results and research trends of NIR-PIT based on bibliometric analysis methods.

Methods

The Web of Science Core Collection (WoSCC) database was searched in August 2024 for relevant publications in the field of NIR-PIT. Data were analyzed using mainly CiteSpace and R software for bibliometric and visual analysis of the country/region, authors, journals, references and keywords of the publications in the field.

Results

A total of 245 publications were retrieved, including articles (n = 173, 70.61%) and reviews (n = 72, 29.39%). The annual and cumulative number of publications increased every year. The highest number of publications was from the United States (149, 60.82%), followed by Japan (70, 28.57%) and China (33, 13.47%). The research institution with the highest number of publications was National Institutes of Health (NIH)-USA (114, 46.53%). Kobayashi H (109) was involved in the highest number of publications, Mitsunaga M (211) was the most frequently cited in total. CANCERS (17) was the most frequently published journal, and NAT MED (220) was the most frequently co-cited journal. The top 10 keywords include near-infrared photoimmunotherapy (166), photodynamic therapy (61), monoclonal antibody (58), in vivo (50), cancer (46), expression (31), breast cancer (27), enhanced permeability (24), antibody (23), growth factor receptor (16). Cluster analysis based on the co-occurrence of keywords resulted in 13 clusters, which identified the current research hotspots and future trends of NIR-PIT in cancer treatment.

Conclusion

This study systematically investigated the research hotspots and development trends of NIR-PIT in cancer treatment through bibliometric and visual analysis. As an emerging strategy, the research on the application of NIR-PIT in cancer treatment has significantly increased in recent years, mainly focusing on the targeting, immune activation mechanism, and treatment efficacy in solid tumors has received extensive attention. Future studies may focus on improving the efficacy and safety of NIR-PIT in cancer treatment, as well as developing novel photosensitizers and combination therapeutic regimens, and exploring the efficacy of its application in a wide range of solid tumors, which will provide an important reference and guidance for the application of NIR-PIT in clinical translation.

Gaze-based attention network analysis in a virtual reality classroom

This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and interconnectedness of the networks were calculated to describe the network structure. The measures, derived from graph theory, allowed for statistical inference testing and the interpretation of participants' visual attention in 3D VR environments. Our method provides useful insights when analyzing students' learning in a VR classroom, as reported in a corresponding evaluation article with N = 274 participants. •Guidelines on implementing gaze-ray casting in VR using the Unreal Engine and the HTC VIVE Pro Eye.•Creating gaze-based attention networks and analyzing their network structure.•Implementation tutorials and the Open Source software code are provided via OSF: https://osf.io/pxjrc/?view_only=1b6da45eb93e4f9eb7a138697b941198.

Sustainability in the global value chain-a scientometric analysis

For its promise in enhancing sustainability, the global value chain (GVC) has grown in relevance and sparked many studies. Due to different value activities in multiple countries and industry clusters, the competition and cooperation among value chains have attracted the considerable attention of business leaders and academicians worldwide. GVC-related sustainability research is a niche area despite its widespread presence in the literature. To bridge the gap, we use scientometric analysis in this paper, examining the corpus of 753 articles published in Web of Science journals from 2001 till 2021. This review illuminates the research performance constituents (e.g., most prolific authors, nations, institutions, and journals), the themes and issues that underpin the fields' intellectual structure, and transforming discoveries. GVC depends on nine basic clusters for sustainability research (i.e., global value chain participation, gendered global production network, repositioning organisational dynamics, labour stands, learning opportunities, Internet era). Future studies can be conducted to generate new knowledge across ten thematic (based on keywords) clusters (i.e., market liberalisation, trade pollution nexus, value chain dynamics, global value chain reconfiguration, non-governmental organisation, multipolar governance). A model that encompasses current knowledge of the global value chain for sustainability is developed, and avenues for future research are provided.

Mapping 26 years of climate change research in finance and accounting: a systematic scientometric analysis

Climate change and climate finance continue to attract substantial research interest in several dimensions and categories through COVID-19 breakout and resulting disruptions were crucial. An in-depth scientometric analysis was undertaken to gain concise insights on evolution and publication trends of this multi-dimensional field. Corpus of 657 articles, extracted from Web of Science from 1995 to 2020, were used to identify networks of co-authorship, keywords, subject categories, institutions, and countries engaged in publishing on climate finance along with co-citation and cluster analysis. Networks and interactive visualizations created using CiteSpace revealed new research areas where climate finance may be beneficial along with potential directions of development for climate finance discipline. We identify carbon neutrality, accounting for sustainability, planetary boundaries framework, sustainable finance, managing climate risk for third pole, financial innovation and green finance, green swans, COVID pandemic and corporate law and governance in climate finance as emerging domains of climate finance research, seeking overwhelming research attention globally.

Research on application of tumor treating fields in glioblastoma: A bibliometric and visual analysis

Background

Glioblastoma, one of the common tumors of the central nervous system (CNS), is prone to recurrence even after standard treatment protocols. As an innovative physiotherapy method emerging in recent years, the tumor treating fields (TTFields) technique has been approved for the treatment of glioblastoma due to its non-invasive and portable features. The purpose of this study is to visualize and analyze the scientific results and research trends in TTFields therapy for glioblastoma.

Methods

Publications related to TTFields therapy for glioblastoma were searched in the Web of Science Core Collection (WoSCC) database in September 2022. A bibliometric and visual analysis of publications in this field was performed mainly using CiteSpace and R software for country/region, author, journal, reference and keyword.

Results

A total of 618 publications in this field were retrieved, and 248 were finally obtained according to the search criteria, including 159 articles (64.11%) and 89 reviews (37.89%). The cumulative number of publications increased year by year, with an average growth rate (AGR) of 28.50%. The test results of Pearson correlation coefficient showed a high positive correlation between publications and citations (r=0.937, p<0.001). The USA had the largest number of publications (123, 49.60%), followed by Germany (32, 12.90%) and China (30, 12.10%). As for the country/region collaborations, the USA cooperated most closely with other countries/regions, followed by Germany and China. The degree of collaboration (DC) between countries/regions was 25.81%. The institutions with the largest number of publications were Tel Aviv Univ (10), Harvard Med Sch (10) and Novocure Ltd (10). Moreover, Wong E (18) possessed the greatest number of publications, followed by Weinberg U (11) and Kirson E (10). The DC between authors was 97.58%. STUPP R (236) was the most cited author followed by KIRSON ED (164) and GILADI M (104). JOURNAL OF NEURO-ONCOLOGY (22) was the journal with the largest number of published publications (75), followed by FRONTIERS IN ONCOLOGY (15) and CANCERS (13). The top 10 keywords that occurred frequently included glioblastoma (156), tumor treating field (152), temozolomide (134), randomized phase III (48), brain (46), survivor (46), cancer (44), trial (42), alternating electric field (42) and radiotherapy (36). Furthermore, cluster analysis was performed on the basis of keyword co-occurrence, and finally 15 clusters were formed to determine the current research status and future development trend of TTFields therapy for glioblastoma.

Conclusion

TTFields has been increasingly known as the fourth novel physical anti-tumor therapy in addition to surgery, radiotherapy and anti-tumor drugs. Cooperation and communication between countries/regions need to be enhanced in future research. Several studies have demonstrated the therapeutic potential of TTFields in glioma, and its application alone or in combination with other treatments has become a current research hotspot.

Reviewing topics of COVID-19 news articles: case study of CNN and China daily

Purpose

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

Design/methodology/approach

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

Findings

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

Originality/value

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.

Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles

The outbreak of coronavirus disease 2019 (COVID-19) has had a significant repercussion on the health, economy, politics and environment, making coronavirus-related issues more complicated and difficult to adequately address by relying on a single field. Interdisciplinary research can provide an effective solution to complex issues in the related field of coronavirus. However, whether coronavirus-related research becomes more interdisciplinary still needs corroboration. In this study, we investigate interdisciplinary status of the coronavirus-related fields via the COVID-19 Open Research Dataset (CORD-19). To this end, we calculate bibliometric indicators of interdisciplinarity and apply a co-occurrence analysis method. The results show that co-occurrence relationships between cited disciplines have evolved dynamically over time. The two types of co-occurrence relationships, Immunology and Microbiology & Medicine and Chemical Engineering & Chemistry, last for a long time in this field during 1990-2020. Moreover, the number of disciplines cited by coronavirus-related research increases, whereas the distribution of disciplines is uneven, and this field tends to focus on several dominant disciplines such as Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. We also measure the disciplinary diversity of COVID-19 related papers published from January to December 2020; the disciplinary variety shows an upward trend, while the degree of disciplinary balance shows a downward trend. Meanwhile, the comprehensive index ²D^s demonstrates that the degree of interdisciplinarity in coronavirus field decreases between 1990 and 2019, but it increases in 2020. The results help to map the interdisciplinarity of coronavirus-related research, gaining insight into the degree and history of interdisciplinary cooperation.

The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments

With the development of technological innovations, Big Data is transforming the socio-economic world, impacting almost every organization and person. The transformations associated with the development of Big Data have important consequences for the sustainability of organizations, regions, and the society as a whole, and as such, they have been specifically addressed by the academic literature focusing on sustainability. Despite its importance, and perhaps because of its rapid emergence, there is a lack of studies dealing with the analysis of this body of literature and its trends. The current research attempts to fill this gap. The study develops a bibliometric and visualization analysis of the literature on the nexus between Big Data and Sustainability. The research analyzes 726 documents on this topic, published until the end of 2020, in the Web of Science Core Collection database through the VOSviewer software. The results indicate the main trends and developments on the topic related to the most cited papers, authors, publications, institutions, and countries. The visualized frameworks, structures and trends are useful for both researchers and practitioners, as they can help them understand the current situation, issues to consider, and main developments on the topic.

Social network analysis of mythology field

Purpose

The main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.

Design/methodology/approach

In this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.

Findings

The findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.

Originality/value

The findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Background

Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear.

Objective

The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis.

Methods

A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities.

Results

The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.

Conclusions

This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.

Mapping the Topics and Evolutions of Chinese Children’s Bestsellers

Children’s books involve a large number of topics. Research on them has been paid much attention to by both scholars and practitioners. However, the existing achievements do not focus on China, which is the fastest growing market for children’s books in the world. Studies using quantitative analysis are low in number, especially on the intellectual structure, evolution patterns, and development trends of topics of children’s bestsellers in China. Dangdang.com, the biggest Chinese online bookstore, was chosen as a data source to obtain children’s bestsellers, and topic words in them were extracted from brief introductions. With the aid of co-occurrence theory and tools of social network analysis and visualization, the distribution, correlation structures, and evolution patterns of topics were revealed and visualized. This study shows that topics of Chinese children’s bestsellers are broad and relatively concentrated, but their distribution is unbalanced. There are four distinguished topic communities (Living, Animal, World, and Child) in terms of centrality and maturity, and they all establish their individual systems and tend to be mature. The evolution of these communities tends to be stable with powerful continuity.

Digital humanities research: interdisciplinary collaborations, themes and implications to library and information science

Purpose

The purpose of this paper is to examine the structure, patterns and themes of interdisciplinary collaborations in the digital humanities (DH) research through the application of social network analysis and visualization tools.

Design/methodology/approach

The sample includes articles containing DH research in the Web of Science Core Collection as of December 2018. First, co-occurrence data representing collaborations among disciplinary were extracted from the subject category. Second, the descriptive statistics, network indicators and interdisciplinary communities were calculated. Third, the research topics of different interdisciplinary collaboration communities based on system keywords, author keywords, title and abstracts were detected.

Findings

The findings reveal that while the scope of disciplines involved in DH research is broad and evolving over time, most interdisciplinary collaborations are concentrated among several disciplines, including computer science, library and information science, linguistics and literature. The study further uncovers some communities based on closely collaborating disciplines and the evolving nature of such interdisciplinary collaboration communities over time. To better understand the close collaboration ties, the study traces and analyzes the research topics and themes of the interdisciplinary communities. Finally, the implications of the findings for DH research are discussed.

Originality/value

This study applied various informetric methods and tools to reveal the collaboration structure, patterns and themes among disciplinaries in DH research.

Exploring the topic structure and evolution of associations in information behavior research through co-word analysis

This study aims to reveal the distribution of topics, and the associations among them, in information behavior research from 2009 to 2018. Working with a collection of 6744 publications from the Web of Science database, co-word analysis is used to investigate the overall topic structure, the associations among the topics, and their evolution in different years, which is supplemented by visualization with science maps. The results uncovered an unbalanced distribution of topics, and that the topics cluster into six communities representing subdivisions of this field: information behavior in patient-centered studies; information interaction in the digital environment; information literacy in health and academic contexts; health literacy on the Internet; information behavior in child-centered studies; and information behavior in medical informatics. The findings supplement and provide refinements to work on the state of this field, and help researchers obtain an overview of the past decade to guide their future work.

Cross-national digital humanities research collaborations: structure, patterns and themes

Purpose

The purpose of this paper is to examine the structure, patterns and themes of cross-national collaborations in Digital Humanities research through the application of social network analysis and visualization tools.

Design/methodology/approach

The sample includes articles containing Digital Humanities research in the Web of Science Core Collection as of December 2018. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators and international communities were calculated. Third, the research topics of different cross-national collaboration communities based on ISI keywords, author keywords, title and abstracts were detected.

Findings

The results show that the scope of international collaborations in Digital Humanities research is broad, but the distribution among nations is unbalanced. The USA, Germany and England were identified as the major contributors. Five research communities are identified, led by the USA, Germany, England, Belgium and France. The communities share common research topics such as history, GIS, text mining, visualization, while each has its own research emphasis.

Originality/value

This study applied various informetric methods and tools to reveal the collaboration structure, patterns and themes among nations in Digital Humanities research.

What is your definition of Big Data? Researchers' understanding of the phenomenon of the decade

The term Big Data is commonly used to describe a range of different concepts: from the collection and aggregation of vast amounts of data, to a plethora of advanced digital techniques designed to reveal patterns related to human behavior. In spite of its widespread use, the term is still loaded with conceptual vagueness. The aim of this study is to examine the understanding of the meaning of Big Data from the perspectives of researchers in the fields of psychology and sociology in order to examine whether researchers consider currently existing definitions to be adequate and investigate if a standard discipline centric definition is possible.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.

OBJECTIVE

This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.

METHODS

The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities.

RESULTS

The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.

CONCLUSIONS

The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

The evolution of data science and big data research: A bibliometric analysis

In this study the evolution of Big Data (BD) and Data Science (DS) literatures and the relationship between the two are analyzed by bibliometric indicators that help establish the course taken by publications on these research areas before and after forming concepts. We observe a surge in BD publications along a gradual increase in DS publications. Interestingly, a new publications course emerges combining the BD and DS concepts. We evaluate the three literature streams using various bibliometric indicators including research areas and their origin, central journals, the countries producing and funding research and startup organizations, citation dynamics, dispersion and author commitment. We find that BD and DS have differing academic origin and different leading publications. Of the two terms, BD is more salient, possibly catalyzed by the strong acceptance of the pre-coordinated term by the research community, intensive citation activity, and also, we observe, by generous funding from Chinese sources. Overall, DS literature serves as a theory-base for BD publications.

Theme Mapping and Bibliometrics Analysis of One Decade of Big Data Research in the Scopus Database

Recently, the popularity of big data as a research field has shown continuous and wide-scale growth. This study aims to capture the scientific structure and topic evolution of big data research using bibliometrics and text mining-based analysis methods. Bibliographic data of journal articles regarding big data published between 2009 to 2018 were collected from the Scopus database and analyzed. The results show a significant growth of publications since 2014. Furthermore, the findings of this study highlight the core journals, most cited articles, top productive authors, countries, and institutions. Secondly, a unique approach to identifying and analyzing major research themes in big data publications was proposed. Keywords were clustered, and each cluster was labeled as a theme. Moreover, the papers were divided into four sub-periods to observe the thematic evolution. The theme mapping reveals that research on big data is dominated by big data analytics, which covers methods, tools, supporting infrastructure, and applications. Other critical aspects of big data research are security and privacy. Social networks and the Internet of things are significant sources of big data, and the resources and services offered by cloud computing strongly support the management and processing of big data.

Detecting the Interdisciplinary Nature and Topic Hotspots of Robotics in Surgery: Social Network Analysis and Bibliometric Study

Background

With the widespread application of a robot to surgery, growing literature related to robotics in surgery (RS) documents widespread concerns from scientific researchers worldwide. Although such application is helpful to considerably improve the accuracy of surgery, we still lack the understanding of the multidiscipline-crossing status and topic distribution related to RS.

Objective

The aim of this study was to detect the interdisciplinary nature and topic hotspots on RS by analyzing the current publication outputs related to RS.

Methods

The authors collected publications related to RS in the last 21 years, indexed by the Web of Science Core Collection. Various bibliometric methods and tools were used, including literature distribution analysis at the country and institution level and interdisciplinary collaboration analysis in the different periods of time. Co-word analysis was performed based on the keywords with high frequency. The temporal visualization bar presented the evolution of topics over time.

Results

A total of 7732 bibliographic records related to RS were identified. The United States plays a leading role in the publication output related to RS, followed by Italy and Germany. It should be noted that the Yonsei University in South Korea published the highest number of RS-related publications. Furthermore, the interdisciplinary collaboration is uneven; the number of disciplines involved in each paper dropped from the initial 1.60 to the current 1.31. Surgery; Engineering; Radiology, Nuclear Medicine, and Medical Imaging; and Neurosciences and Neurology are the 4 core disciplines in the field of RS, all of which have extensive cooperation with other disciplines. The distribution of topic hotspots is in imbalanced status, which can be categorized into 7 clusters. Moreover, 3 areas about the evolution of topic were identified, namely (1) the exploration of techniques that make RS implemented, (2) rapid development of robotic systems and related applications in surgery, and (3) application of a robot to excision of tissues or organs targeted at various specific diseases.

Conclusions

This study provided important insights into the interdisciplinary nature related to RS, which indicates that the researchers with different disciplinary backgrounds should strengthen cooperation to publish a high-quality output. The research topic hotspots related to RS are relatively scattered, which has begun to turn to the application of RS targeted at specific diseases. Our study is helpful to provide a potential guide to the direction of the field of RS for future research in the field of RS.