Category Archives: Research Examples

Research networks generated by organizational structures, coauthorships and citations: A Case Study of German Centre for Integrative Biodiversity Research (iDiv) (FULL-TEXT)

Exploring whether different patterns emerge across networks generated by organizational structures, coauthorships and citations for characterizing and evaluating cooperative relationships is particularly important for transferring the research results into practice. This research-in-progress paper focuses on using the structure of scientific collaborations and mapping knowledge transfer to gain insight into the influence of collaborative research centres linked to the German Research Foundation (DFG) funding. Within the German Centre for Integrative Biodiversity Research (iDiv), the DFG sponsors research conducted across all participating universities and institutes by more than hundred research groups who bring their expertise to the manifold research fields of biodiversity. Using iDiv’s research from 2013-2020, we build co-authorship networks and identify the most cohesive communities in terms of collaboration and compare them with groups presented on its website. Corresponding cited and citing works are analysed by distributions to investigate disciplinary collaboration. Our findings show that the number of publications and the intensity of research collaboration have maintained a steady increase. Despite the highly cohesive cooperation structure addressed by iDiv, the internal scientific collaboration has not gained strong momentum compared with its growing trends in international collaborations. The tendency towards covering cross-disciplinary research foci is not evident.

Link for FULL-TEXT

Author(s): Zhao Qu
Organization(s):German Centre for Higher Education Research and Science Studies (DZHW)
Source: arXiv:2103.11911v1 [cs.DL]
Year: 2021

Chitosan Biomedical Applications for the Treatment of Viral Disease: A Data Mining Model Using Bibliometric Predictive Intelligence (FULL-TEXT)

Chitosan has attracted increasing attention from researchers in the pharmaceutical and biomedical fields as a potential agent for the prevention and treatment of infectious diseases. However, identifying the development of emerging technologies related to this biopolymer is difficult, especially for newcomers trying to understand the research streams. In this work, we designed and implemented a research process based on a bibliometric predictive intelligence model. Our aim is to glean detailed scientific and technological trends through an analysis of publications that include certain word phrases and related research areas. Cross correlation, factor mapping, and the calculation of “emergent” scores were also used. A total of 1,612 scientific papers on chitosan technology related to viral disease treatment published between 2010 and 2020 were retrieved from the Web of Science. Results from the keyword modelling quantitatively highlight three major frontier research and development topic groups: drug delivery and adjuvants, vaccines and immune response, and tissue engineering. More specifically, the emergent scores show that much of the chitosan-based treatment for viral diseases is in the in vitro stage of development. Most chitosan applications are in pharmacology/pharmacy and immunology. All results were confirmed by experts in the field, which indicates that the validated process can be applied to other fields of interest.


Author(s): Worasak Klongthong, Veera Muangsin, Chupun Gowanit, Nongnuj Muangsin
Organization(s): Chulalongkorn University
Source: Journal of Chemistry
Year: 2020

Corporate Engagement with Nanotechnology through Research Publications (FULL-TEXT)

Assessing corporate engagement with an emerging technology is essential for understanding the development of research and innovation systems. Corporate publishing is used as a system-level knowledge transfer indicator, but prior literature suggests that publishing can run counter to private sector needs for management of dissemination to ensure appropriability of research benefits. We examine the extent of corporate authorship and collaboration in nanotechnology publications from 2000 to 2019. The analysis identified 53,200 corporate nanotechnology publications. Despite the potential for limits on collaboration with corporate authors, this paper finds that eight out of 10 nanotechnology corporate publications involved authors from multiple organizations and nearly one-third from multiple countries and that these percentages were higher in recent years. The USA is the leading nation in corporate nanotechnology publishing, followed by Japan and Germany, with China ranking fourth, albeit with the greatest publication growth rate. US corporate publishing is more highly cited and less cross-nationally collaborative. Asian countries also have fewer collaborative authorship ties outside of their home countries. European countries had more corporate collaborations with authors affiliated with organizations outside of their home countries. The paper concludes that distinguishing corporate publications, while difficult due to challenges in identifying small- and medium-sized corporations and grouping variations in corporate names, can be beneficial to examining national systems of research and development


Author(s): Jan Youtie, Robert Ward, Philip Shapira, Alan L. Porter, Nils Newman
Organizaton(s): Georgia Institute of Technology, Search Technology
Source: Journal of Nanoparticle Research
Year: 2021

An Exploratory Perspective to Measure the Emergence Degree for a Specific Technology Based on the Philosophy of Swarm Intelligence (FULL-TEXT)

How to evaluate or measure the emergence degree or level for a specific technology is rarely discussed in the prior studies, and it should be a valuable issue for the relevant areas on technology forecasting, foresight, and technological strategies for macro and micro economies, particularly for those emerging economies who are chasing the technology advances in the developed countries. A conceptual framework inspired by swarm intelligence theory is introduced to measure the emergence degree or level for a specific technology. Swarm intelligence belongs to complex systems theory, and has evolved into a helpful tool for heuristic algorithms and optimization computation, and brought forward an insightful perspective on the evolution and emergence of natural or social systems in the past decades. To verify the proposed framework for measuring emergence degree of a specific technology based on the basic philosophy of swarm intelligence, a case study analyzes an annual set of emerging technologies of the World Economic Forum. The theoretical and empirical analyses could present a fresh vision to investigate the essence of technology emergence, and provide some supplemental thoughts for the policy-making on those emerging or new technologies.


Author(s): Munan Li, Alan L. Porter, Arho Suominen, Serhat Burmaoglu, Stephen Carley
Organization(s): South China University of Technology, Search Technology, VTT Technical Research Centre of Finland, Izmir Katip Celebi University
Source: Technological Forecasting and Social Change
Year: 2021

Impact of the environmental management system standardization on the managerial image of firms: An empirical study.

We describe a characterization of the conceptual and emotional changes on environmental issues in a sample of firms certified under the ISO 14001 standard. Business communications regarding the main Spanish industrial firms have been downloaded from the ABI/INFORM database and processed using Vantage Point software, in order to study the evolution of the main concepts and emotions before and after the certification year. Our study concludes that in the years before certification environmental management was fundamentally tied to operative issues, broadly pivoting on the immediate impact of a firm’s productive activities. Environmental management gains strategic traction in the years after certification, positioning itself near corporate decision-making concepts and associated with adjectives that denote relevance and positivity. The sentiment analysis points at an increased positivity of environment-related issues, accompanied by a general decrease in negative emotions and an increased presence of expectation and planning emotions.

Author(s): Gaizka Garechana, Rosa Rio-Belver, Enara Zarrabeitia, Izaskun Alvarez-Meaza
Organization(s): University of the Basque Country UPV/EHU
Source: Journal of Emerging Technologies in Accounting JETA
Year: 2021

Topic Evolution, Disruption and Resilience in Early COVID-19 Research (FULL-TEXT)

The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.


Author(s): Yi Zhang, Xiaojing Cai, Caroline V. Fry, Mengjia Wu, Caroline S. Wagner
Organization(s): University of Technology Sydney, The Ohio State University, University of Hawai’i at Manoa Shidler College of Business
Source: Scientometrics
Year: 2021

Collaboration Network and Trends of Global Coronavirus Disease Research: A Scientometric Analysis (FULL-TEXT)

As a global pandemic threatens health and livelihoods, finding effective treatments has become a vital issue that requires worldwide collaboration. This study examines research collaboration and network profiles through a case study of coronavirus diseases, including both the extinct severe acute respiratory syndrome coronavirus (SARS-CoV) and the emerging species (SARS-CoV-2). A scientometric process was designed to apply quantitative tools and a qualitative approach employing technological expertise to accomplish a three-level collaboration analysis. The text mining software, VantagePoint, was used to analyze research articles from the Web of Science database to identify the key national, organizational, and individual players in the coronavirus research field combined with indicators, namely, the breadth and depth of collaboration. The results show that China and the United States are at the center of coronavirus research networks at all three levels, including many endeavors involving single or joint entities. This study demonstrates how governments, public sectors, and private sectors, such as the pharmaceutical industry, can use scientometric analysis to gain insight into the holistic research trends and networks of players in this field, leading to the formulation of strategies to strengthen research and development programs. Furthermore, this approach can be utilized as a visualization and decision support tool for further policy planning, identification and execution of collaboration, and research exchange opportunities. This scientometric process should be directly applicable to other fields.

Link to FULL-TEXT 10.1109/ACCESS.2021.3066450

Author(s): Jakkrit Thavorn, Chupun Gowanit, Veera Muangsin, Nongnuj Muangsin
Organization(s): Chulalongkorn University
Source: IEEE Access
Year: 2021

A bibliometric and descriptive analysis of inclusive education in science education

This article aims to map the scientific production concerning the inclusion of people with disabilities in Science Education to promote a reflection on the production of this area. Bibliometric analysis is used to help understand what stage of research a particular subject is at. Publications on the topic indexed at the Web of Science Core Collection (WoS) were evaluated. A total of 119 articles published between 2009 and July 2019 were selected as dealing specifically with the subject. An increase in the number of articles associating Science teaching (ST) and Inclusive Education (IE) was noted. The journals that published the most, the most productive authors in the area and their collaboration networks were identified. A content analysis of the research was also carried out and the main investigated topics were pointed out. Educational levels, types of disabilities, central themes and specific science areas prevailing in the mapped research were also indicated. We conclude that, despite the growing number of articles, scientific production associating SE and IE is still small, concentrated, and not shared with the scientific community through scientific education journals, and that most research is focused on the use of methodologies and resources, and not on their development.

Author(s): Michele Waltz Comarú, Renato Matos Lopes,Luiza Amara Maciel Braga, Fabio Batista Mota, Cecília Galvão
Organization(s):Federal Institute of Rio de Janeiro, Oswaldo Cruz Foundation, University of Lisbon
Source: Studies in Science Education
Year: 2021

Tracking developments in artificial intelligence research: constructing and applying a new search strategy (FULL-TEXT)

Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category “artificial intelligence”. We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.


Author(s): Na Liu, Philip Shapira, Xiaoxu Yue
Organization(s):Shandong Technology and Business University, University of Manchester, Tsinghua University
Source: Scientometrics
Year: 2021

Expert Opinions on the Most Promising Treatments and Vaccine Candidates for COVID-19: Global Cross-sectional Survey of Virus Researchers in the Early Months of the Pandemic (FULL-TEXT)

The COVID-19 pandemic presents a great public health challenge worldwide, especially given the urgent need to identify effective drugs and develop a vaccine in a short period of time. Globally, several drugs and vaccine candidates are in clinical trials. However, because these drugs and vaccines are still being tested, there is still no definition of which ones will succeed.This study aimed to assess the opinions of over 1000 virus researchers with knowledge on the prevention and treatment of coronavirus-related human diseases to determine the most promising drug and vaccine candidates to address COVID-19. We mapped the clinical trials related to COVID-19 registered at These data were used to prepare a survey questionnaire about treatments and vaccine candidates for COVID-19. In May 2020, a global survey was conducted with authors of recent scientific publications indexed in the Web of Science Core Collection related to viruses, severe acute respiratory syndrome coronavirus, coronaviruses, and COVID-19. Remdesivir, immunoglobulin from cured patients, and plasma were considered to be the most promising treatments in May 2020, while ChAdOx1 and mRNA-1273 were considered to be the most promising vaccine candidates. Almost two-thirds of the respondents (766/1219, 62.8%) believed that vaccines for COVID-19 were likely to be available in the next 18 months. Slightly fewer than 25% (289/1219, 23.7%) believed that a vaccine was feasible, but probably not within 18 months. The issues addressed in this study are constantly evolving; therefore, the current state of knowledge has changed since the survey was conducted. However, for several months after the survey, the respondents’ expectations were in line with recent results related to treatments and vaccine candidates for COVID-19.

Author(s): Bernardo Pereira Cabral, Luiza Braga, Fabio Mota
Organization(s): Oswaldo Cruz Foundation, Federal University of Bahia, Fluminense Federal University
Year: 2021