Category Archives: Research Examples

Characteristics and Trends of Ocean Remote Sensing Research from 1990 to 2020: A Bibliometric Network Analysis and Its Implications (full-text)

The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.


Author(s): Qiang Wang, Jinping Wang, Mingmei Xue, Xifeng Zhang
Organization(s): Chinese Academy of Sciences, Northwest Normal University
Source: Journal of Marine Science and Engineering
Year: 2022

Social life cycle assessment: mapping scientific knowledge

Social life cycle assessment (sLCA) is a methodology to support decision making on social impacts, positive and negative, actual or potential, related to product life cycles [1,2]. Being a relatively new tool, the indicators are not yet homogenized and the method does not have a standard to be followed nor a code of practice [3] [4]. Therefore, describing the state-of-the-art scientific research into sLCA is necessary to clarify the current situation and to determine future development goals [5]. The aim of this paper is, to describe the scientific trends related to sLCA through a bibliometric and a network analysis. This will enable us to identify the main collaborations and help to standardize the sLCA methodology.

Methodology: The research process consists of four steps. 1, define the search query. 2, retrieve data. 461 articles from the Scopus database and 348 from the WOS database were obtained and exported. 3, clean up the refined database. Using Vantage Point (VP) text mining software, the obtained data were cleaned up, applying fuzzy logic algorithms to clean up the data fields. 4, generate the sLCA scientific profile and the network analysis. This scientific profile will define the publication trends and academic performance, and the network analysis will allow us to determine the main collaboration relationships.

Conclusions: sLCA, as a methodology for measuring the social impacts of companies, is gaining prominence. More and more is being published on sLCA, especially since 2018. The countries doing the most research on the subject are European, with Germany leading by far, followed by Italy. Nonetheless, the United States, Canada, China and Brazil are also among the top researchers. In terms of scientific collaborations, the same countries appear as the main collaborators, although the United States ranks first as an intermediary country. The institutions that contribute the most scientific production on sLCA are mainly European universities of technology. These universities have a closed pat- tern of scientific collaboration on sLCA, i.e. they form research clusters that collaborate little with other clusters. The same is true for the main authors; research is also carried out in closed clusters. In addition, the European Union is the main economic driver of this research topic. Finally, the most frequently used keywords in the publications are those related to life cycle, assessment and sustainability.

Author(s): Naiara Pikatza Gorrotxategi, Izaskun Alvarez-Meaza, Rosa María Río-Belver, Enara Zarrabeitia Bilbao
Organization(s): University of the Basque Country.
Source: Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización
Year: 2022

Integrative model for discovering linked topics in science and technology

• The science and technology semantic linkage integration model improves the identification of linked topics in science and technology (LTSTs).

• Simple fusion and link prediction form a twofold model to identify topics and implicit semantics.

• Term co-occurrence networks of basic and applied research are fused.

• The fusion expands topic networks and enhances their semantic associations.

• LTST chains identified by connected LTST terms provide micro granularity.

Linked topics in science and technology (LTSTs) can provide new avenues for technological innovation and are a key step in the transition from basic to applied research. This paper proposes a science and technology semantic linkage integration model for discovering LTSTs. Particularly, the integrative model fuses the term co-occurrence networks of basic and applied research, which expands the completeness of topic networks by enhancing the semantic characteristics of these networks. It is found that link prediction can further reinforce the semantic association of topic terms in networks between basic and applied topics. Simple fusion explicitly linked the topic terms, which can be used as automatic seed marking for subsequent link prediction to identify implicit linking of topic terms. Furthermore, an application to the gene-engineered vaccines field depicted that newly predicted implicit relations can effectively identify LTSTs. The results also show that implicit semantic recognition of LTSTs can be enhanced through simple fusion, while the recognition of LTST can be improved through link prediction. Therefore, the proposed model can assist experts to identify LTSTs that cannot be recognized through simple fusion.

Author(s): Haiyun Xu, Zenghui Yue, Hongshen Pang, Ehsan Elahi, Jing Li, Lu Wang
Organization(s): Shandong University of Technology, Jining Medical University, Shenzhen University, Chinese Academy of Sciences
Source: Journal of Informetrics
Year: 2022

Research Trends on Pillared Interlayered Clays (PILCs) Used as Catalysts in Environmental and Chemical Processes: Bibliometric Analysis (full-text)

Over the last four decades, a large number of studies have been published on pillared interlayered clays (PILCs) used as adsorbent materials and catalysts or supports for transition metals in heterogeneous catalysis. Particularly, PILCs have been used for water treatment through advanced oxidation processes (AOPs) to remove organic pollutants. They have also been studied in various chemical and environmental processes. Because of the growing interest in PILCs, this article is focused on analyzing scientific publications such as research/review articles and book chapters from the last four decades (from 1980 to 2019) through a bibliometric analysis (BA) to visualize and describe research trends on PILCs. By narrowing the bibliographic search to titles, keywords, and abstracts of publications related to PILCs, using Scopus and Web of Science (WoS) (the two scientific databases), a total of 3425 documents have been retrieved. The bibliometric dataset was analyzed by VantagePoint®. The main research trends identified in the last four decades were the use of PILCs in environmental processes (34.4% of total publications) along with chemical processes (petrochemical reactions 17.5%, SCR NOx 10.8%, and decomposition 8.2%). In environmental processes, PILCs have been used in photo-oxidation (32%), CWPO (21.1%), and heterogeneous catalysis (19.4%). Phenols, dyes, and VOCs have been the main pollutants studied using PILCs as catalysts. Fe, Ti, Zr, Cu, and Co are the most supported active phases in PILCs. Other research trends grouped by characterization techniques, countries, research areas, institutes, scientific journals that have published the most on this topic, number of publications per 5-year period, and most frequently used keywords through the last four decades have been identified. It was determined that the number of publications on PILCs has increased since 1980 and the countries with the highest number of publications are China, Spain, and The United States of America.

For FULL-TEXT go to

Author(s): Iván F. Macías-Quiroga, Julián A. Rengifo-Herrera, Sandra M. Arredondo-López, Alexander Marín-Flórez, Nancy R. Sanabria-González
Organization(s): Universidad Nacional de Colombia sede Manizales, Universidad Nacional de La Plata
Source: The Scientific World Journal
Year: 2022

Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies

  • A hybrid approach to extract technical intelligence for technological opportunities analysis.
  • To blend topic modeling, SAO semantic analysis and machine learning.
  • An optimized LDA-based topic extraction model with high accuracy.
  • A case study on dye-sensitized solar cell.

With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

Author(s): Tingting Ma, Xiao Zhou, Jia Liu, Zhenkai Lou, Zhaoting Hua, Ruitao Wang
Organization(s): Communication University of China, Beijing Wuzi University, Xidian University, Anhui University of Technology
Source: Technological Forecasting and Social Change
Year: 2021

Anticipating New Treatments for Cystic Fibrosis: A Global Survey of Researchers (full-text)

Cystic fibrosis is a life-threatening disease that affects at least 100,000 people worldwide. It is caused by a defect in the cystic fibrosis transmembrane regulator (CFTR) gene and presently, 360 CFTR-causing mutations have been identified. Since the discovery of the CFTR gene, the expectation of developing treatments that can substantially increase the quality of life or even cure cystic fibrosis patients is growing. Yet, it is still uncertain today which developing treatments will be successful against cystic fibrosis. This study addresses this gap by assessing the opinions of over 524 cystic fibrosis researchers who participated in a global web-based survey. For most respondents, CFTR modulator therapies are the most likely to succeed in treating cystic fibrosis in the next 15 years, especially through the use of CFTR modulator combinations. Most respondents also believe that fixing or replacing the CFTR gene will lead to a cure for cystic fibrosis within 15 years, with CRISPR-Cas9 being the most likely genetic tool for this purpose.

For FULL-TEXT go to

Author(s): Bernardo Cabral, Vito Terlizzi, Onofrio Laselva, Carlos Conte Filho, Fabio Mota
Organization(s): Federal University of Bahia, Oswaldo Cruz Foundation, University of Foggia, Federal University of Santa Maria, Anna Meyer Children’s University
Source: Journal of Clinical Medicine
Year: 2022

Detecting trends in sustainability publications: research development and dynamics in “Green and sustainable science and technology” category

Although the sustainability movement began to take shape in the early nineteen seventies, it was not until 1987 with the publication of the Brundtland Report that it acquired institutional recognition. The academic community’s engagement with the movement came a few years later in the form of sustainability science, which developed and consolidated in keeping with the dynamics of any new scientific discipline. This review analyses its development based on the papers listed in the web of science (WoS) category ‘Green and sustainable science and technology’. The bibliometric methodology used included social network and multivariate analysis focusing on journal, discipline and subject inter-relationships to map and analyse developments in this new field. The main findings identified four clusters of journals with different patterns of development: sustainability, renewable energies, green chemistry and green ecology. Mainstream and emerging subjects were determined on the grounds of co-word analysis.

Author(s): Andrés Pandiella-Dominique, Núria Bautista-Puig and Daniela De Filippo
Organization(s): University Carlos III of Madrid
Source: International Journal of Innovation and Sustainable Development
Year: 2021

A postcolonial feminist exploration of the scholarship on women and educational leadership with a bibliometric approach

This study applied a bibliometric approach to a dataset of publications on women and educational leadership to critically explore the nature of research in the field and the utility of the bibliometric method in its mapping. The analysis was conducted on bibliographic records of 823 papers on women and educational leadership published from 1975 to 2020, which had been retrieved from the Web of Science. The results are presented in the form of lists of most impactful papers; most productive research centres/organizations and countries; similar lists of contributing disciplines and publication venues; as well as maps visualizing collaborative activity. A postcolonial feminist perspective used in interpretation of the results reveals that, on the one hand, the approach makes it possible to uncover the persistent coloniality and linguistic hegemony in the field, whereas, on the other hand, bibliometric metrification may contribute to Western epistemic violence and valorisation of scholarship in masculinist terms.

Author(s): Aliya Kuzhabekova
Organization(s): Nazarbayev University
Source: Educational Management Administration and Leadership
Year: 2021

Dynamic network analytics for recommending scientific collaborators

Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field.

Author(s): Lu Huang, Xiang Chen, Yi Zhang, Yihe Zhu, Suyi Li, Xingxing Ni
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Scientometrics
Year: 2021

Identification of topic evolution: network analytics with piecewise linear representation and word embedding

Understanding the evolutionary relationships among scientific topics and learning the evolutionary process of innovations is a crucial issue for strategic decision makers in governments, firms and funding agencies when they carry out forward-looking research activities. However, traditional co-word network analysis on topic identification cannot effectively excavate semantic relationship from the context, and fixed time window method cannot scientifically reflect the evolution process of topics. This study proposes a framework of identifying topic evolutionary pathways based on network analytics: Firstly, keyword networks are constructed, in which a piecewise linear representation method is used for dividing time periods and a Word2Vec mode is used for capturing semantics from the context of titles and abstracts; Secondly, a community detection algorithm is used to identify topics in networks; Finally, evolutionary relationships between topics are represented by measuring the topic similarity between adjacent time periods, and then topic evolutionary pathways are identified and visualized. An empirical study on information science demonstrates the reliability of the methodology, with subsequent empirical validations.

Author(s): Lu Huang, Xiang Chen, Yi Zhang, Changtian Wang, Xiaoli Cao, Jiarun Liu
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Scientometrics
Year: 2022