This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.
For FULL-TEXT go to https://doi.org/10.1371/journal.pone.0236307
Author(s): Caroline V. Fry, Xiaojing Cai, Yi Zhang, Caroline S. Wagner
Organization(s): University of Hawai’i at Manoa, The Ohio State University, University of Technology Sydney
Source: PloS One
The fuel-cell electric vehicle (FCEV) has been defined as a promising way to avoid road transport greenhouse emissions, but nowadays, they are not commercially available. However, few studies have attempted to monitor the global scientific research and technological profile of FCEVs. For this reason, scientific research and technological development in the field of FCEV from 1999 to 2019 have been researched using bibliometric and patent data analysis, including network analysis. Based on reports, the current status indicates that FCEV research topics have reached maturity. In
addition, the analysis reveals other important findings: (1) The USA is the most productive in science and patent jurisdiction; (2) both Chinese universities and their authors are the most productive in science; however, technological development is led by Japanese car manufacturers; (3) in scientific research, collaboration is located within the tri-polar world (North America–Europe–Asia-Pacific); nonetheless, technological development is isolated to collaborations between companies of the same
automotive group; (4) science is currently directing its efforts towards hydrogen production and storage, energy management systems related to battery and hydrogen energy, Life Cycle Assessment, and greenhouse gas (GHG) emissions. The technological development focuses on technologies related to electrically propelled vehicles; (5) the International Journal of Hydrogen Energy and SAE Technical Papers are the two most important sources of knowledge diffusion. This study concludes by outlining the knowledge map and directions for further research.
https://doi.org/10.3390/su12062334 for FULL-TEXT
Author(s): Izaskun Alvarez-Meaza, Enara Zarrabeitia-Bilbao, Rosa Maria Rio-Belver, and Gaizka Garechana-Anacabe
Organization(s): University of the Basque Country
The five BRICS countries bear 49% of the world’s TB burden and they are committed to ending tuberculosis. This paper maps the scientific landscape related to TB research in BRICS. During the period 1993–2016, there were 38,315 peer-reviewed, among them, there were 11,018 (28.7%) articles related by one or more authors in a BRICS: India 38.7%; China 23.8%; South Africa 21.1%; Brazil 13.0%; and Russia 4.5% (The total was greater than 100% because our criterion was all papers with at least one author in a BRICS). Among the BRICS, there was greater interaction between India and South Africa and organizations in India and China had the highest productivity; however, South African organizations had more interaction with countries outside the BRICS. Publications by and about BRICS generally covered all research areas, especially those in India and China covered all research areas, although Brazil and South Africa prioritized infectious diseases, microbiology, and the respiratory system. An overview of BRICS scientific publications and interactions highlighted the necessity to develop a BRICS TB research plan to increase efforts and funding to ensure that basic science research successfully translates into products and policies to help end the TB epidemic. The bubble charts were generated by VantagePoint and the networks by the Gephi 0.9.1 software (Gephi Consortium 2010) from co-occurrence matrices produced in VantagePoint. The Fruchterman-Reingold algorithm provided the networks’ layout.
For FULL-TEXT see https://doi.org/10.1590/0074-02760190342
Author(s): Kamaiaji Castor, Fabio Batista Mota, Roseli Monteiro da Silva, Bernardo Pereira Cabral, Ethel Leonor Maciel, Isabela Neves de Almeida, Denise Arakaki-Sanchez, Kleydson Bonfim Andrade, Vadim Testov, Irina Vasilyeva, Yanlin Zhao, Hui Zhang, Manjula Singh, Raghuram Rao, Srikanth Tripathy, Glenda Gray, Nesri Padayatchi, Niresh Bhagwandin, Soumya Swaminathan, Tereza Kasaeva, Afrânio Kritski
Organization(s): Fundação Oswaldo Cruz-Fiocruz; Ministério da Saúde, Programa Nacional de Controle da Tuberculose; National Medical Research Centre of Pthtisiopulmonology and Infection Diseases, MoH; National Centre for Tuberculosis Control and Prevention, China CDC; University of KwaZulu-Natal
Source: Memórias do Instituto Oswaldo Cruz
This work aims to analyse a relational structure of patents in automotive sector, especially for electric vehicles. The automotive industry is equivalent to 22% of Brazil’s industrial gross domestic product (GDP), 4% of Brazil’s total GDP, and worldwide it should reach a mark of 100 million vehicles sold by 2020. In recent decades, innovation processes have become a matter of survival for companies. Resource Based View admits that companies are looking for strategic resources that are a source of competitive advantage. This work comprises the automotive market as an organizational field in which the actors struggle to reach more favourable positions from the available resources. The network approach was used together with patent analysis to highlight the existing relationships within this industry in the construction of innovation, through the analysis of patents of high commercial value in the automotive sector, collected through the Derwent database.
For FULL-TEXT see http://leblog.gerpisa.org/node/4456
Author: Filippo Filippo Savoi de Assis
Organization: Universidade Federal de São Carlos – UFSCar
Source: Gerpisa Colloquium
As a basic tool for technology evolution analysis, technology network can visualize the relationship among technologies in different patents. However, the current constructions of technology network only represent common technical information, and cannot reflect different types of technical information. We propose a new approach to construct a fine-grained technology network and display technical information from multiple perspectives. Based on the Subject-Action-Object (SAO) structures extracted from patent documents, we first classify the technical information, and then investigate the semantic relationship among different types of technical information. Based on that, single-type and multi-type technology networks are constructed, which can demonstrate different types of technical information and make the technology evolution analysis easier and more reliable. Finally, taking the Nano-fertilizer patents as a data source, we construct a fine-grained construction of technology network, which might help identify fundamental and emerging technologies in the Nano-fertilizer field.
Author(s): Xiaoman Li, Hongyan Song, Xuefu Zhang, Qian Xu
Organization: Agricultural Information Institute, Chinese Academy of Agricultural Sciences
Source: Proceedings of the 3rd International Conference on Computer Science and Application Engineering
Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. VantagePoint used in data preprocessing. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber. VantagePoint used in data preprocessing.
Author(s): Lin Zhu, Donghua Zhu, Xuefeng Wang,Scott W. Cunningham, Zhinan Wang
Organization(s): Beijing Institute of Technology, Delft University of Technology
Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.
Author(s): Lin Zhu, Donghua Zhu, Xuefeng Wang, Scott W. Cunningham, Zhinan Wang
Organization(s): Beijing Institute of Technology
Lab-on-chip are miniaturized devices capable of performing a variety of chemical, biochemical or biological analyzes of small volumes of fluids into a single chip. This is an emerging technology that holds potential to deliver more reliable and faster results at a lower cost than traditional laboratory methods. The aim of this paper is to give an overview of the products and processes related to lab-on-a-chip based on patent documents. We use patents data from Thomson Reuters Derwent Innovations Index and combine bibliometrics and social network analysis techniques. We found 2984 patents related to lab-on-a-chip technology, of which only 221 claims for a new lab-on-a-chip device. Considering the total of patents, our results show a significant increase in patenting from 2000 to 2008. As of 2006, the interest in patenting in several countries has risen. USA and Japan are the two most frequent countries developing related technologies, and the USA and the European Patent Office are the top target of patenting by non-residents. Overall, one can see a wide dispersion of organizations involved in researching and developing this technology. Technological developments are most frequently associated with the areas of physics, performing operations and chemistry. Most of the documents are aimed to protect inventions related to instruments for measuring and testing, and processes or apparatus for separation or mixing. By providing a lab-on-a-chip patent landscape worldwide, our findings can be used to support R&D decisions and foster new partnerships between organizations willing to develop the capabilities needed to enter this market.
Author(s): Flávia Maria Lins Mendes, Kamaiaji Castor, Roseli Monteiro, Fabio Batista Mota, Leonardo Fernandes Moutinho Rocha
Organization(s): Centro de Estudos Estratégicos, Fundação Oswaldo Cruz (CEE/Fiocruz)
Source: World Patent Information
knowledge diffusion based on scientific collaboration is similar to disease propagation through actual contact. Inspired by the disease-spreading model in complex networks, this study classifies the states of research entities during the process of knowledge diffusion in scientific collaboration into four categories. Research entities can transform from one state to another with a certain probability, which results in the evolution rules of knowledge diffusion in scientific collaboration networks. The knowledge diffusion model of differential dynamics in scientific collaboration of non-uniformity networks is formed, and the relationship between the degree distribution and evolution of knowledge diffusion is further discussed, to reveal the dynamic mechanics of knowledge diffusion in scientific collaboration networks. Finally, an empirical analysis is conducted on knowledge diffusion in an institutional scientific collaboration network by taking the graphene field as an example. The results show that the state evolution of research entities in the knowledge diffusion process of scientific collaboration networks is affected not only by the evolution states of adjacent research entities with whom they have certain collaboration relationships, but also by the structural attributes and degree distributions of scientific collaboration networks. The evolution of knowledge diffusion in scientific collaboration entities with different degrees also shows different trends.
Author(s):Zenghui Yue, Haiyun Xu, Guoting Yuan, Hongshen Pang
Organization(s):Jining Medical University, Chengdu Documentation and Information Center, Chinese Academy of Sciences
Source: Physica A: Statistical Mechanics and its Applications
This paper describes a unique two-step methodology used to construct six linked bibliometric datasets covering the sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa genomes. First, we retrieved all sequence submission data from the European Nucleotide Archive (ENA), including accession numbers associated with each species. Second, we used these accession numbers to construct queries to retrieve peer-reviewed scientific publications that first linked to these sequence lengths in the scientific literature. For each species, this resulted in two associated datasets: 1) A .csv file documenting the PMID of each article describing new sequences, all paper authors, all institutional affiliations of each author, countries of institution, year of first submission to the ENA, and the year of article publication, and 2) A .csv file documenting all institutions submitting to the ENA, number of nucleotides sequenced, number of submissions per institution in a given year, and years of submission to the database. In several upcoming publications, we utilise these datasets to understand how institutional collaboration shaped sequencing efforts, and to systematically identify important institutions and changes in network structures over time. This paper, therefore, should aid researchers who would like to use these data for future analyses by making the methodology that underpins it transparent. Further, by detailing our methodology, researchers may be able to utilise our approach to construct similar datasets in the future.
For full-text https://f1000research.com/articles/8-1200
Author(s): Mark Wong, Rhodri Leng
Organization(s): University of Glasgow, University of Edinburgh