Science Mapping:A Systematic Review of the Literature

Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.

Analysis of articles on hepatitis C by scientific mapping:1989-2022

BACKGROUND Hepatitis C virus(HCV)poses a significant quandary about public health.It is challenging to study the literature in a particular discipline comprehensively today.One solution is bibliometric analysis,which is often used to track the attributes and evolutionary trajectories of scientific outputs.AIM To examine the 35-year scientific evolution of articles focused on HCV.METHODS This study examined the 35-year scientific evolution of articles focused on HCV.Our study utilized the Web of Science database.The study encompassed a total of 11930 articles.RESULTS Regarding the cumulative count of articles,the leading countries are the United States,Japan,and Italy.Rice CM is the author with the highest recorded H-index and G-index values.The journal with the highest recorded H-index and G-index values is the Journal of Virology.The Journal of Viral Hepatitis contributed 10.94%of the articles,whereas the Journal of Virology published 9.68%.According to the strategic diagram,the keywords most frequently used in 2020-2022 are HCV,epidemiology,and sofosbuvir.CONCLUSION This study provides valuable information about 40 years of academic knowledge on HCV.

Mapping scientific cooperation of the NSFC:A case study of Major International(Regional) Joint Research Program

International cooperations have played an important role in academic activities as more and more researchers endeavor to participate in joint research or exchange with partners aboard. Focused on projects of Major International(Regional) Joint Research Program of the Natural Science Foundation of China(NSFC) from 2003 to 2008, we have investigated the characteristics of the scientific cooperation by mapping the funded projects from the aspects of topics, affiliations and cooperative countries(regions), respectively.Moreover, it is demonstrated that issues related to nano technologies, genes and ecological environments have become the hot topics in the joint research of NSFC, and projects with issues of big science have been preferentially funded under the program’s guide posted every year. Moreover, most granted projects were undertaken by the minority of affiliations with the leading research capability, which is the same to partners abroad. The aggregation effect has been greatly improved with the bilateral or multilateral agreement signed with scientific organizations abroad, so has the competitive mechanism of scientific resources allocation.

Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

Information visualization analysis on Advances in Psychological Science:1983-2014

Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.

Internet of things:Conceptual network structure,main challenges and future directions

Internet of Things(IoT)is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities.In this study,we investigate the existing strategic themes,thematic evolution structure,key challenges,and potential research opportunities associated with the IoT.For this study,we conduct a Bibliometric Performance and Network Analysis(BPNA),supplemented by an exhaustive Systematic Literature Review(SLR).Specifically,in BPNA,the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of Science(WoS)database,which was released between 2002 and 2019.The results reveal that 31 clusters are classified according to their importance and development,and the conceptual structures of key clusters are presented,along with their performance analysis and the relationship with other subthemes.The thematic evolution structure describes the important cluster(s)over time.For the SLR,23 documents are analyzed.The SLR reveals key challenges and limitations associated with the IoT.We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.

基于可视化学科多样性测度指数和主题模型的领域学科交叉知识图谱构建--以纳米科技领域为例

[目的/意义]基于文献计量学和文本挖掘方法探索与某领域相关的学科交叉知识图谱的新思路。[方法/过程]提出可视化学科多样性测度指数和主题模型的领域学科交叉知识图谱研究框架,基于Science Overlay Map和嵌入领域本体的LDAvis进行领域学科交叉知识图谱的构建,并以纳米科技领域为例,验证学科交叉知识图谱研究框架的有效性和适用性。[结果/结论]基于Science Overlay Map的领域学科交叉科学地图,从全学科的角度展示纳米科技领域与其他学科的交叉情况,基于LDAvis结合领域本体的学科交叉主题交互图则聚焦具体的学科交叉主题和主题之间的相互关系,二者的结合可以从宏观和微观上更清晰地把握纳米科技领域与其他学科的交叉情况。本研究可以弥补学科交叉主题识别结果不容易被解释的局限性,为领域学科交叉知识图谱研究提供了一种解决方案的新视角。

Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

Purpose: The goal of this study is to explore whether deep learning based embed ded models can provide a better visualization solution for large citation networks. De sign/methodology/approach: Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic Open Ord method with different edge cutting strategies and parameters. Findings: The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps' layout has very high stability.Research limitations: The computational and time costs of training are very high for network em bedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested. Practical implications: This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliomet ric analysis tasks. Originality/value: This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer,more stable science map. We also designed a practical evaluation method to investigate and compare maps.

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields.This also helps in having a better collaboration with governments and businesses.This study aims to investigate the development of research fields over time,translating it into a topic detection problem.Design/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents.Document embedding approaches are utilized to transform documents into vector-based representations.The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(i.e.LDA)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI publications.Findings:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset.Using the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the topics.Research limitations:We noticed that it is not possible to generalize one solution for all downstream tasks.Hence,it is required to fine-tune or optimize the solutions for each task and even datasets.In addition,interpretation of cluster labels can be subjective and vary based on the readers’opinions.It is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further limited.Practical implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and reviewers of the academic research to detect,summarize,analyze,and visualize research topics from decades of academic documents.This helps the scientific community and all related organizations in fast and effective analysis of the fields,by establishing and explaining the topics.Originality/value:In this study,we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction.We also use a concept extraction method as a labeling approach in this study.The effectiveness of the method has been evaluated in a case study of AI publications,where we analyze the AI topics during the past three decades.

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Purpose： The main goal of this study is to discover the scientific evolution of Cancer-Related Symptoms in Complementary and Alternative Medicine research area, analyzing the articles indexed in the Web of Science database from 1980 to 2013.Design/Methodology/Approach： A co-word science mapping analysis is performed under a longitudinal framework（1980 to 2013）. The documental corpus is divided into two subperiods,1980–2008 and 2009–2013. Thus, the performance and impact rates, and conceptual evolution of the research field are shown.Findings： According to the results, the co-word analysis allows us to identify 12 main thematic areas in this emerging research field： anxiety, survivors and palliative care,meditation, treatment, symptoms and cancer types, postmenopause, cancer pain, low back pain, herbal medicine, children, depression and insomnia, inflammation mediators, and lymphedema. The different research lines are identified according to the main thematic areas,centered fundamentally on anxiety and suffering prevention. The scientific community can use this information to identify where the interest is focused and make decisions in different ways.Research limitation： Several limitations can be addressed： 1） some of the Complementary and Alternative Medicine therapies may not have been included; 2） only the documents indexed in Web of Science are analyzed; and 3） the thematic areas detected could change if another dataset was considered.Practical implications： The results obtained in the present study could be considered as an evidence-based framework in which future studies could be built.Originality/value： Currently, there are no studies that show the thematic evolution of this research area.

Nuclear Planetology:Especially Concerning the Moon and Mars

To approach basic scientific questions on the origin and evolution of plan- etary bodies such as planets, their satellites and asteroids, one needs data on their chemical composition. The measurements of gamma-rays, X-rays and neutrons emit- ted from their surface materials provide information on abundances of major elements and naturally radioactive gamma-ray emitters. Neutron spectroscopy can provide sen- sitive maps of hydrogen- and carbon-containing compounds, even if buried, and can uniquely identify layers of carbon-dioxide frost. Nuclear spectroscopy, as a means of compositional analysis, has been applied via orbital and lander spacecraft to extrater- restrial planetary bodies： the Moon, Venus, Mars, Mercury and asteroids. The knowl- edge of their chemical abundances, especially concerning the Moon and Mars, has greatly increased in recent years. This paper describes the principle of nuclear spec- troscopy, nuclear planetary instruments carried on planetary missions so far, and the nature of observational results and findings of the Moon and Mars, recently obtained by nuclear spectroscopy.

Big data in education:Themes and trends

With the rapid growth of big data research,the existing research has investigated the research themes and trends of big data in different disciplines,but less has paid attentions on the field of education.Using bibliographic data from the Web of Science(Wos),we conduct bibliometric analysis and science mapping to explore the research themes and trends of big data in education.The results show that though education is not the major producer of big data research,it does have a positive development trend.In addition,we find that big data in education mainly serves as a tool to facilitate educational outcomes.Implications,limitations,and futuredirections arediscussed.

China's research contribution in big data

Big data is one of the current and future research frontiers.It has received international attention,and some countries have even upgraded big data research to a national strategy.Therefore,it is interesting to understand the status quo of big data research and identify the status and contribution of a country.Our study is divided into two parts.The first part of this study combines core lexical query and expanded lexical query to get relatively integral publications’data sets on big data.Citation relationships and a maximum connected subgraph algorithm are used to clean and filter unrelated publications.Then the Leiden algorithm is selected to cluster the citation network for big data and VOSviewer is used to map the big data knowledge structure.In the second part of this study,we analyze China’s research contribution in terms of research output and highly-cited papers.In order to better show the distribution of big data research in China,we utilized science overlay mapping to visualize the status quo of China’s research in big data.Our study shows that China is one of the most important countries in big data research and the research covers almost all areas of big data.However,the research performance is relatively low.In terms of knowledge structure with science overlay mapping,China’s research mainly focuses on cloud computing,the Internet of Things(Io T),and social media.However,research topics with a greater rate of highly-cited papers are mainly found in cloud computing,big data medicine,and Industry 4.0.These topics are also the dominant areas of China’s big data research.