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
In the last 40 years, the aeronautical industry has managed to move from a specialized sector to a worldwide leading industry. Companies, governments and associations all over the world acknowledge the importance of the aviation industry in supporting global development and the economy. However, aviation will be facing new challenges related to sustainability and performance in a technological environment in evolution. To succeed, the aeronautical industry must keep innovation as one of its main assets. It must master a wide range of technologies and then collaborate to integrate them into an aircraft design and development program. A collaborative approach to innovation is key to achieve these goals. The main purpose of this paper is to analyze the structure of technological innovation networks in the aviation industry and to characterize the
map of the “Aviation Technology Space”. Two different approaches and methods are used. In one approach, we performed a bibliometric network analysis of aviation research scientific publications using a keyword co-occurrence analysis method to map the aerospace collaboration structures. Complementarily, we performed a patent analysis to evaluate the innovation capacity of the aviation industry in the cutting-edge technologies previously identified. From the results of this analysis, the paper provides recommendations for future innovation and research policies to allow the sector to fulfill the demanding goals by the year 2050.
For FULL-TEXT click here
Author(s): Rosa Maria Arnaldo Valdés, Serhat Burmaoglu, Vincenzo Tucci, Luiz Manuel Braga da Costa Campos, Lucia Mattera, Víctor Fernando Gomez Comendador
Organization(s): Universidad Politécnica de Madrid, Katip Celebi University
South-south collaboration on health and development research is a critical mechanism for social and economic progress. It allows sharing and replicating experiences to find a “southern solution” to meet shared health challenges, such as access to adequate HIV/AIDS prevention and treatment. This study aimed to generate evidence on the dynamics of south-south collaboration in HIV/AIDS research, which could ultimately inform stakeholders on the progress and nature of collaboration towards increased research capacities in low- and middle-income countries (LMIC).
METHODS: Bibliometric and social network analysis methods were used to assess the 10-year (2006-2015) scientific contribution of LMIC, through the analysis of scientific publications on HIV/AIDS prevention and/or treatment. Five dimensions oriented the study: knowledge production, co-authorship analysis, research themes mapping, research types classification and funding sources.
RESULTS: Publications involving LMIC have substantially increased overtime, despite small expression of south-south collaboration. Research themes mapping revealed that publication focus varied according to collaborating countries’ income categories, from diagnosis, opportunistic infections and laboratory-based research (LMIC single or LMIC-LMIC) to human behavior and healthcare, drug therapy and mother to child transmission (LMIC-HIC). The analysis of research types showed that south-south collaborations frequently targeted social sciences issues. Funding agencies acknowledged in south-south collaboration also showed diverse focus: LMIC-based funders tended to support basic biomedical research whereas international/HIC-based funders seem to cover predominantly social sciences-oriented research.
CONCLUSIONS: Although the global environment has fostered an increasing participation of LMIC in collaborative learning models, south-south collaboration on HIV/AIDS prevention and/or treatment research seemed to be lower than expected, stressing the need for strategies to foster these partnerships. The evidence presented in this study can be used to strengthen a knowledge platform to inform future policy, planning and funding decisions, contributing to the development of enhanced collaboration and a priority research agenda for LMICs.
Link to FULL-TEXT https://www.ncbi.nlm.nih.gov/pubmed/29490665
Author(s): Bruna Fonseca, Priscila Albuquerque, Ed Noyons, Fabio Zicker
Organization(s): Center for Technological Development in Health (CDTS) at Oswaldo Cruz Foundation (Fiocruz), Leiden University
Source: Global Health
Understanding how a technology is introduced and shared in a society has a strategic value for the planning of technological development and assessing new market opportunities. Among other technologies, microscopy has had a significant role in advancing different fields of science. In Brazil, its use spans from biomedical to engineering areas. Here, we used social network analysis (SNA) to map and quantify the flow of interaction between Brazilian researchers involved in microscopy techniques. The analysis examines co-occurrence of thematic networks and scientific co-authorship in articles published in a ten years window, as retrieved from Scopus database. The results showed an increasing volume of publications using microscopy in Brazil. The two major areas of interest are material and life sciences, which present significant intra-regional interaction. USA, Spain, Germany, Portugal and the United Kingdom are the main partner countries for international scientific collaborations. The share of Brazilian publications applying microscopy follows the global trends, with a slight predominance in health and life sciences. Our results provide a context of the strengths and gaps of the field in Brazil and may help to inform researchers and policy makers for further advancing the field.
Author(s): Priscila C. Albuquerque, Brunade Paula Fonseca e Fonseca, Wendell Girard-Dias, Fabio Zicker, Wanderley de Souza, Kildare Miranda
Organization(s): Fundação Oswaldo Cruz, Universidade Federal do Rio de Janeiro
Global academic exchange and cooperation have become an increasing trend in both academia and industry, but how to quickly and effectively identify potential partners is becoming an urgent problem. This paper proposes a link prediction-based model to help researchers identify partners from a large collection of academic articles in a given technological area. We initially construct a co-authorship network, and take a series of indices based on network and similarity of researchers into consideration. A fitting model of link prediction is then established, in which logistic regression analysis is involved. An empirical study on four journals of informetrics is conducted to demonstrate the reliability of the proposed method.
Author(s): Lu Huang, Yihe Zhu, Yi Zhang, Xiao Zhou, Xiang Jia
Organization(s): Beijing Institute of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)