Category Archives: Research Type

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

Research Examples, Tech mining,

Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-mean as,principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.

https://doi.org/10.1016/j.joi.2018.09.004

Author(s): Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan Porter, Hongshu Chen, Guangquan Zhang
Organization(s): University of Technology Sydney, Beijing Institute of Technology, Georgia Institute of Technology
Source: Journal of Informetrics
Year: 2018

Chapter 2 – Lessons From 10 Years of Nanotechnology Bibliometric Analysis

Research Examples, Research Type, Tech mining

This chapter summarizes the 10-year experiences of the Program in Science, Technology, and Innovation Policy (STIP) at Georgia Institute of Technology (Georgia Tech) in support of the Center for Nanotechnology in Society at Arizona State University (CNS-ASU) in understanding, characterizing, and conveying the development of nanotechnology research and application. This work was labeled “Research and Innovation Systems Assessment” or (RISA) by CNS-ASU. CNS-ASU was designed to implement a set of methods to anticipate societal impacts (including environmental, health, and safety impacts) and lay the foundation for making changes to emerging technologies at an early stage in their development.

RISA concentrates on identifying and documenting quantifiable aspects of nanotechnology, including academic, commercial/industrial, and government nanoscience and nanotechnology (nanotechnologies) activity, research, and projects. RISA at CNS-ASU engaged in the first systematic attempt of its kind to define, characterize, and track a field of science and technology. A key element to RISA was the creation of a replicable approach to bibliometrically defining nanotechnology. Researchers in STIP, and beyond, could then query the resulting datasets to address topical areas ranging from basic country and regional concentrations of publications and patents to findings about social science literature, environmental, health, and safety research and usage, to study corporate entry into nanotechnology and to explore application areas as special interests arose. Key features of the success of the program include the following:

  • Having access to “large-scale” R&D abstract datasets
  • Analytical software
  • A portfolio that balances innovative long-term projects, such as webscraping to understand nanotechnology developments in small and medium-sized companies, with research characterizing the emergence of nanotechnology that more readily produces articles
  • Relationships with diverse networks of scholars and companies working in the nanotechnology science and social science domains
  • An influx of visiting researchers
  • A strong core of students with social science, as well as some programming background
  • A well-equipped facility and management by the principals through weekly problem-solving meetings, mini-deadlines, and the production journal articles rather than thick final reports.

https://doi.org/10.1016/B978-0-12-813588-4.00002-6

Author(s): Jan Youtie, Alan L.Porter, Philip Shapira, Nils Newman
Organization(s): Georgia Institute of Technology, Search Technology
Source: Nanotechnology Environmental Health and Safety (Third Edition)
Year: 2018

Measuring Interdisciplinary Research Categories and Knowledge Transfer: A Case Study of Connections between Cognitive Science and Education

Interdisciplinarity, Research Examples

This is a “bottom-up” paper in the sense that it draws lessons in defining disciplinary categories under study from a series of empirical studies of interdisciplinarity. In particular, we are in the process of studying the interchange of research-based knowledge between Cognitive Science and Educational Research. This has posed a set of design decisions that we believe warrant consideration as others study cross-disciplinary research processes.

https://doi.org/10.1162/posc_a_00317

Author(s): Alan L. Porter, Stephen F. Carley, Caitlin Cassidy, Jan Youtie, David J. Schoeneck, Seokbeom Kwon and Gregg E. A. Solomon
Organization(s): Georgia Institute of Technology, Search Technology Inc.
Source: Perspectives on Science
Year: 2019

An integrated solution for detecting rising technology stars in co-inventor networks

Network analysis, Research Examples, Research Type, ST&I indicators

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.

https://doi.org/10.1007/s11192-019-03194-w

Author(s): Lin Zhu, Donghua Zhu, Xuefeng Wang, Scott W. Cunningham, Zhinan Wang
Organization(s): Beijing Institute of Technology
Source: Scientometrics
Year: 2019

Bibliometric Analysis of Trends in Global Sustainable Livelihood Research (Full-Text)

Research Examples, Research profiling

The concept of sustainable livelihoods (SL) is one of the most important subjects of sustainable development, and is an important long-term goal for poverty alleviation. There has been growing interest in the nature and practical application of SL in recent decades. This paper applies bibliometric analysis to collect and analyze data on sustainable livelihoods from the expanded Science Citation index (SCIE) and the Social Sciences Citation Index (SSCI). Bibliometric maps can assist greatly in visualizing and summarizing large volumes of data and in studying scientific outputs. The findings offer insights into research trends pertaining to SL, such as these: (1) In recent decades there has been an increase in both the number of papers on SL and their scientific influence. (2) The most active journals are Sustainability, Ecology and Society, Land Use Policy, and International Journal of Sustainable Development and World Ecology. (3) SL papers are distributed mainly in the fields of Environmental Sciences, Environmental Studies, Ecology, Planning & Development, and Green & Sustainable Science & Technology. (4) The USA and UK are leaders in SL research as measured by both the quantity and quality of SL publications. Some developing countries, notably India and China, have seen an increase in SL publications in recent years. (5) Wageningen University in Netherlands, the Chinese Academy of Science, and the Center for International Forestry Research (CIFOR), headquartered in Indonesia, have had a major influence in the field of international SL research. (6) International cooperation has a positive effect on the growth of SL research, suggesting that there is a need for strengthening cooperation among countries, international institutions, and individuals. (7) Major areas of SL research (“hot topics”) are theoretical research on the SL concept; ecosystem conservation; poverty reduction in the poverty-stricken areas; the impact of climate change on livelihoods; and linkages between SL-related policies and institutional change

For Full-Text https://doi.org/10.3390/su11041150

Author(s): Chenjia Zhang, Yiping Fang, Xiujuan Chen, Tian Congshan
Organization(s): Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, University of Chinese Academy of Sciences
Source: Sustainability
Year: 2019

What people learn about how people learn: An analysis of citation behavior and the multidisciplinary flow of knowledge

Citation analysis, Interdisciplinarity, Research Examples, Research Type, ST&I indicators

We explore the contention that the seminal US National Academies consensus report,How People Learn(HPL),played a major role in bridging the flow of knowledge from Cognitive Science to Education. Our paper yielded four important results: First, HPL is, on a number of bibliometric measures, an unusually interdisciplinary work.Focusing on the fields of particular interest here, our citation analysis shows the Education, Cognitive Science,and Borderfield (e.g., Educational Psychology, Learning Sciences, and Learning Technology and Human-Computer Interaction) literatures all to have been major influences on it. Second, we found HPL to be unusually highly cited–and by publications from an unusually diverse set of disciplines. Beyond Education, Cognitive Science, and Border field publications, HPL was also relatively highly cited by publications in Medical/Health-related, Engineering, and other Discipline-Based Education Research fields. Third, undermining the claim that HPL served as a gateway to the Cognitive Science literature, we found Education articles citing HPL not to be more likely to have Cognitive Science as a major influence than are Education articles more generally, as in-dicated by their cited references. Finally, the Education publications that cited HPL were far more likely to refer to concepts in HPL that were already prevalent in the Education literature rather than to concepts from Cognitive Science. Conversely, the Cognitive Science publications that cited HPL were more apt to refer to concepts already in the Cognitive Science literature. Taken together, these results are a caution that, even for a highly regarded multidisciplinary work cited widely by publications from multiple disciplines, its direct influence could be largely disciplinary. Implications for the policy goals of fostering interdisciplinary research and the role of National Academies consensus reports are discussed.

https://doi.org/10.1016/j.respol.2019.103835

Author(s): Gregg E. A. Solomon, Jan Youtie, Stephen Carley, Alan L. Porter  
Organization(s): National Science Foundation, Georgia Institute of Technology                                                                                                        
Source: Research Policy                                                                                       
Year: 2019

Mapping the lab-on-a-chip patent landscape through bibliometric techniques

Network analysis, Patents, Research Examples

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.

https://doi.org/10.1016/j.wpi.2019.101904

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
Year: 2019

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Research Examples, Research profiling

Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and society in the future. By means of bibliometric method, the study aims at providing an overview on the research tendency and the most concerned topics of AI during the past decade. The database of Web of Science was chosen and the articles published in AI journals were retrieved. Top 10% of the yearly high-citation articles (12,301 articles) published since the year of 2008 were selected as sampling articles for the analysis. The bibliographic records were used for the overall analysis, and the core keywords were studied and classified into three categories (algorithm, general technology and application technology) for topics analysis. As results, number of articles in AI by year and country, the country collaboration and well-known institutes and researchers in AI were presented. Also we proposed and concluded the five most concerned topics, which are perception intelligence (1st), human mind simulated intelligence (2nd), classical model based machine learning (3rd), bio-inspired intelligence (4th), and big-data based intelligence (5th). It is the authors’ wish that the study were helpful for researchers to have an overall grasp of the recent status of AI development.

https://doi.org/10.1007/s00542-019-04426-y

Author(s): Fang Gao, Xiaofeng Jia, Zhiyun Zhao, Chih-Cheng Chen, Feng Xu, Zhe Geng, Xiaotong Song
Organization(s): Institute of Scientific and Technical Information of China (ISTIC)
Source: Microsystem Technologies
Year: 2019

Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field

Network analysis, Research Examples, ,

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.

https://doi.org/10.1016/j.physa.2019.04.201

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
Year: 2019

Analysing the theoretical roots of technology emergence: an evolutionary perspective

Research Examples, Research profiling

There has been much research concerning emergence in technology, ever since knowledge has been accepted as a prime engine of economic growth. However, even though there are a growing number of publications, the concept remains ambiguous. In this study, we aim to trace emergence discussions to find the evolution of related concepts, in order to explore usage in the technological context. To achieve this, the philosophy of science, complexity, and economic literatures are reviewed in accordance with the emergence concept qualitatively. Then, a bibliometrics study is performed to strengthen the qualitative argument and find evidence of emergence in technology studies for comparison. Based on the findings, we can assert that the definition of technology emergence needs to be revised with consideration of its theoretical foundations. Moreover, after discussion, research questions are posed for future research.

https://doi.org/10.1007/s11192-019-03033-y

Author(s): Serhat Burmaoglu, Olivier Sartenaer, Alan Porter, Munan Li
Organization(s): Izmir Katip Celebi University, University of Cologne, Georgia Institute of Technology
Source: Scientometrics
Year: 2019