ORCID Profile
0000-0002-7731-0301
Current Organisation
University of Technology Sydney
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Publisher: Wiley
Date: 06-2017
DOI: 10.1002/ASI.23814
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2021
Publisher: Wiley
Date: 20-06-2020
DOI: 10.1002/TIE.22158
Publisher: Elsevier BV
Date: 09-2019
Publisher: InTech
Date: 21-11-2012
DOI: 10.5772/50973
Publisher: Springer Science and Business Media LLC
Date: 10-2019
Publisher: Elsevier BV
Date: 11-2021
Publisher: Frontiers Media SA
Date: 24-09-2021
Abstract: This article surveys topic distributions of the academic literature that employs the terms bibliometrics, scientometrics, and informetrics. This exploration allows informing on the adoption of those terms and publication patterns of the authors acknowledging their work to be part of bibliometric research. We retrieved 20,268 articles related to bibliometrics and applied methodologies that exploit various features of the dataset to surface different topic representations. Across them, we observe major trends including discussions on theory, regional publication patterns, databases, and tools. There is a great increase in the application of bibliometrics as science mapping and decision-making tools in management, public health, sustainability, and medical fields. It is also observed that the term bibliometrics has reached an overall generality, while the terms scientometrics and informetrics may be more accurate in representing the core of bibliometric research as understood by the information and library science field. This article contributes by providing multiple snapshots of a field that has grown too quickly beyond the confines of library science.
Publisher: Emerald
Date: 19-06-2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: IEEE
Date: 08-2022
Publisher: Springer Science and Business Media LLC
Date: 07-05-2014
Publisher: Frontiers Media SA
Date: 24-05-2021
Abstract: The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions erge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses and 4) genes that are frequently highlighted include ACE2 , IL6 , TMPRSS2 , and TNF . Emerging genes to the COVID-19 consist of FURIN , CXCL10 , OAS1 , OAS2 , OAS3 , and ISG15 . This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 10-2017
Publisher: IEEE
Date: 08-2022
Publisher: Elsevier BV
Date: 09-2022
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Frontiers Media SA
Date: 08-11-2021
Publisher: Wiley
Date: 24-03-2023
DOI: 10.1002/ASI.24754
Abstract: Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
Publisher: Springer Science and Business Media LLC
Date: 05-03-2014
Publisher: Springer Science and Business Media LLC
Date: 07-02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 31-05-2023
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 09-2016
Publisher: ACM
Date: 08-2020
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2024
Publisher: IEEE
Date: 08-2022
Publisher: Public Library of Science (PLoS)
Date: 21-07-2020
Publisher: IEEE
Date: 08-2015
Publisher: Frontiers Media SA
Date: 06-11-2020
Publisher: Elsevier BV
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 02-2022
Publisher: Springer Science and Business Media LLC
Date: 13-09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Elsevier BV
Date: 03-2021
Publisher: Springer Science and Business Media LLC
Date: 09-10-2021
Publisher: WORLD SCIENTIFIC
Date: 30-07-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 06-2014
Publisher: Elsevier BV
Date: 06-2021
Publisher: OAE Publishing Inc.
Date: 2022
Abstract: Aim: Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. Methods: We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. Results: The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector however, it is still at an immature stage, and more efforts are needed to nourish its development. Conclusion: The research findings can leverage future research and add value to the existing literature on the subject of data analytics.
Publisher: Inderscience Publishers
Date: 2018
Publisher: Elsevier BV
Date: 07-2022
Publisher: ACM
Date: 14-08-2022
Publisher: Public Library of Science (PLoS)
Date: 24-05-2016
Publisher: Springer Science and Business Media LLC
Date: 20-07-2013
Publisher: WORLD SCIENTIFIC
Date: 13-08-2020
Publisher: IEEE
Date: 08-2018
Publisher: Elsevier BV
Date: 09-2019
Publisher: Walter de Gruyter GmbH
Date: 06-2021
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 08-2018
Publisher: Elsevier BV
Date: 09-2022
Publisher: IEEE
Date: 08-2015
Publisher: Wiley
Date: 2019
Publisher: Elsevier BV
Date: 07-2021
Publisher: Wiley
Date: 03-05-2022
DOI: 10.1002/ASI.24653
Abstract: Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision‐making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
Publisher: IEEE
Date: 08-2018
Publisher: Springer Science and Business Media LLC
Date: 24-08-2022
Publisher: American Chemical Society (ACS)
Date: 05-07-2017
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 11-2016
Publisher: Mary Ann Liebert Inc
Date: 10-2021
Publisher: IEEE
Date: 07-2017
Publisher: Springer Science and Business Media LLC
Date: 05-02-2022
Publisher: Springer Science and Business Media LLC
Date: 11-07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 2020
Publisher: IEEE
Date: 07-2017
Publisher: Elsevier BV
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 07-03-2017
Publisher: Informa UK Limited
Date: 02-05-2014
Publisher: Elsevier BV
Date: 03-2017
Publisher: Public Library of Science (PLoS)
Date: 25-05-2022
DOI: 10.1371/JOURNAL.PONE.0261624
Abstract: The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters—two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 09-2016
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 14-03-2017
Publisher: Elsevier BV
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 14-03-2017
Publisher: Springer Science and Business Media LLC
Date: 20-03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Informa UK Limited
Date: 18-06-2013
Publisher: MIT Press - Journals
Date: 2021
DOI: 10.1162/QSS_A_00100
Abstract: Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One topic of interest is to understand the problem-solving patterns in China’s research systems now and in the future. Targeting a set of high-quality research articles published by Chinese researchers between 2009 and 2018, and indexed in the Essential Science Indicators database, we developed an intelligent bibliometrics-based methodology for identifying the problem-solving patterns from scientific documents. Specifically, science overlay maps incorporating link prediction were used to profile China’s disciplinary interactions and predict potential cross-disciplinary innovation at a macro level. We proposed a function incorporating word embedding techniques to represent subjects, actions, and objects (SAO) retrieved from combined titles and abstracts into vectors and constructed a tri-layer SAO network to visualize SAOs and their semantic relationships. Then, at a micro level, we developed network analytics for identifying problems and solutions from the SAO network, and recommending potential solutions for existing problems. Empirical insights derived from this study provide clues to understand China’s research strengths and the science policies underlying them, along with the key research problems and solutions that Chinese researchers are focusing on now and might pursue in the future.
Publisher: Springer Science and Business Media LLC
Date: 17-10-2022
No related grants have been discovered for Yi Zhang.