Category Archives: Uncategorized

The contribution of academic genealogy to construction of bibliometric indicators (FULL-TEXT in Portuguese) A contribuição da genealogia acadêmica para a construção de indicadores bibliométricos

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The measurement of scientific production through bibliometrics, which
allows you to assess the performance of researchers and the impacts of their productions, is a much explored object of study in Information Science. The academic genealogy, on the other hand, allows us to study the intellectual heritage that it results from the relationships between advisors and their students. recent studies in Brazil on academic genealogy use as a database the Lattes Platform, extracting information that can be used for various analyses. Therefore, this work aims to investigate whether the application of Academic genealogy and bibliometric analysis together can bring complementary information about the performance of a graduate program in the training of researchers and scientific production, carrying out a study of case based on the Graduate Program in Materials Engineering at Federal University of São Carlos. The methodology used is based on the method of identifying academic genealogy graphs in conjunction with SyncLattes, VantagePoint and Microsoft Excel tools. How results are bibliometric indicators on the program and its descendants were presented, that can contribute to the development of strategies for self-assessment and performance of graduate programs.

FULL-TEXT in Portuguese available at https://www.seer.ufrgs.br/EmQuestao/article/view/101444

Author(s):Vanessa Paula Alves de Moura, Efraim Cekinski
Organization(s):Universidade Federal de São Carlos
Source: Em Questão
Year: 2021

The nature of rapid response to COVID-19 in Latin America: an examination of Argentina, Brazil, Chile, Colombia and Mexico (FULL-TEXT)

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The coronavirus-19 (COVID-19) pandemic mobilized the international scientific community in the search for its cure and containment. The purpose of this paper is to examine the nature of the rapid response to the COVID-19 of the scientific community in selected Latin American countries (Argentina, Brazil, Chile, Colombia and Mexico) in the period running from January to August 2020. Rapid response is reconceptualized from its original meaning in health policy, as the swift mobilization of existing scientific resources to address an emergency (DeVita et al., 2017). The paper explores the rapid response of the Argentinian, Brazilian, Chilean, Colombian and Mexican scientific communities from the perspective of bibliometric and altmetric data. The authors will examine scientific publications indexed to the Web of Science (WoS) dealing with COVID-19. Besides patterns of scientific output and impact as measured by citations, the authors complement the analysis with altmetric analysis. The aim is to verify whether or not factors that explain the extent of scientific impact can also be identified with respect to the wider impact made evident by altmetric indicators (Haustein, 2016). The authors identified a somewhat limited response of the Argentinian, Brazilian, Chilean, Colombian and Mexican scientific communities to COVID-19 in terms of quantity of publications. The authorship of publications in the topic of COVID-19 was associated with authorship of publications dealing with locally relevant diseases. Some factors appear to contribute to visibility of scientific outputs. Papers that involved wider international collaborations and authors with previous publications in arboviruses were associated with higher levels of citations. Previous work on arbovirus was also associated with higher altmetric attention. The country of origin of authors exerted a positive effect on altmetric indicators. A limitation in the analysis is that, due to the nature of the data source (WoS), the authors were unable to verify the career status and the productivity of the authors in the sample. Nonetheless, the results appear to suggest that there is some overlapping in authors conducting research in Arboviruses and COVID-19. Career status and productivity should be the focus of future research. In the context of countries with limited scientific resources, like the ones investigated in our Latin American sample, previous efforts in the study of locally relevant diseases may contribute to the creation of an expertise that can be applied when a health emergency brings about a novel disease.

https://doi.org/10.1108/OIR-09-2020-0391 ResearchGate has FULL-TEXT

Author(s): Janaina Pamplona da Costa, André Luiz Sica de Campos, Paulo Roberto Cintra, Liz Felix Greco, Johan Hendrik Poker
Organization(s): State University of Campinas
Source: Online Information Review
Year: 2021

Understanding the long-term emergence of autonomous vehicles technologies

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Identifying emerging technologies has been of long-standing interest to many scholars and practitioners. Previous studies have introduced methods to capture the concept of emergence from bibliographic records, including the recently proposed Technology Emergence Indicator (Carley et al. 2018). This indicator method has shown to be applicable to various technological fields. However, the indicator uses a limited time window, which can overlook the potential long-term evolution of emerging technologies. Moreover, the existing method suffers from interpretability, because it can be difficult to understand the context in which identified emerging terms are used. In this paper, we propose an improved version of the Technology Emergence Indicator that addresses these issues. In doing so, we examine emerging topics within the field of autonomous vehicles technologies during the period of 1991-2018, guided by a proposition about the long-term diffusion of an emerging technology topic. The results show that different autonomous vehicle technology topics emerge during each of the three 10-year periods under analysis, including an initial period of understanding the surrounding environment and path planning, a second period marked by DARPA Grand Challenge motivated factors associated with the urban environment and communication technologies, and a third period relating to machine learning and object detection. This association with certain emerging technology topics in each decade is also characterized by different trajectories of continued or cyclical carryover across the decades. The results suggest a methodology that practitioners can use in examining research areas to understand which topics are likely to persist into the future.

https://doi.org/10.1016/j.techfore.2021.120852

Author(s): Seokkyun Woo, Jan Youtie,Ingrid Ott, Fenja Scheu
Organization(s): Georgia Institute of Technology, Karlsruhe Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2020

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

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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.

Link to FULL-TEXT https://link.springer.com/article/10.1007/s11192-021-03868-4

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

An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence (FULL-TEXT)

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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.

FUUL-TEXT Pre-publication versionDownload

https://doi.org/10.1016/j.techfore.2021.120621

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

Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research (FULL-TEXT)

Network analysis, Uncategorized

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
Year: 2020

Parameter tuning Naïve Bayes for automatic patent classification

Uncategorized

In an era of exponential technological growth, business intelligence professionals are more in need than ever of an organized patent landscape in which to conduct technology forecasting and industry positioning. However, the construction of such a system requires time and trained experts, both of which are expensive investments for such a small part of any actual analysis. A natural solution is to employ machine learning (ML), a branch of artificial intelligence that uses statistical information to find patterns and make inferences. The primary benefit of using ML is that these algorithms do not require explicit instruction. In this paper, I present an analysis of feature selection for automatic patent categorization. For a corpus of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019), I assign International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. I compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, I apply the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.

https://doi.org/10.1016/j.wpi.2020.101968

Author(s): Caitlin Cassidy
Organization(s): Search Technology
Source: World Patent Information
Year: 2020

A 3-dimensional analysis for evaluating technology emergence indicators

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Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.

DOI: https://doi.org/10.1007/s11192-020-03432-6 432-6

Author(s): Xiaoyu Liu, Alan L. Porter
Organization(s): Beijing Institute of Technology, Search Technology
Source: Scientometrics
Year: 2020

Overlapping Community Discovery for Identifying Key Research Themes

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Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field’s key research themes.

https://doi.org/10.1109/TEM.2020.2972639

Author(s): Lu Huang, Fangyan Liu, Yi Zhang
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: IEEE Transactions on Engineering Management
Year: 2020

A Multi-match Approach to the Author Uncertainty Problem (Full-Text)

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The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies.

For FULL-TEXT see https://doi.org/10.2478/jdis-2019-0006 

Author(s): Stephen F. Carley, Alan L. Porter, Jan L. Youtie
Organization(s): Georgia Institute of Technology
Source: Journal of Data and Information Science
Year: 2019 (online. 2017 print)