In this paper, scientometrics cognitive and knowledge visualization technology were used to evaluate global scientific production and development trends in construction and building technology research of smart cities. All the data was collected from the Science Citation Index-Expanded (SCIE) database and Journal Citation Reports (JCR). The published papers from the subject of construction and building technology and their journals, authors, countries and keywords spanning over several aspects of research topics, proved that architecture/building research grew rapidly over the past 30 years, and the trend still continues in recent smart cities era. The purposed of this study were to identify the journals in the field of construction and building technology in smart city, make a comparative report on related researches, as well as propose a quality evaluation of those journals. Based on JCR and SCI paper data, the journals related to construction and building technology in smart city were assessed using ten metrics: languages, active degree, references, citation trends, main countries, leading institutes, cooperation trends, productive authors, author keywords and keywords plus. The results indicate that all the factors have great significance and are related to the impact of a journal. It also provides guidance to both evaluators and the study groups which assess journals during the process of judging or selecting research outlets, and future perspective on how to improve the impact of a paper or a journal.
Author(s): Liang-xing Su, Peng-hui Lyu , Zheng Yang, Shuai Ding, Kai-le Zhou
Organization(s): Wuhan University; Hefei University of Technology
Source: Scientometrics http://link.springer.com/article/10.1007/s11192-015-1697-0
Bibliometric and “tech mining” studies depend on a crucial foundation—the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of “legacy technology” terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze “Big Data,” we confront all three challenges—emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a “Hit-Ratio” that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replaceable searches with high recall and satisfactory precision for other emerging technology studies.
Author(s): Ying Huang, Jannik Schuehle, Alan L. Porter, and Jan Youtie
Organization(s): Beijing Institute of Technology and Georgia Institute of Technology
With growing attention to societal issues and implications of synthetic biology, we investigate sources of social science publication knowledge in synthetic biology and probe what might be learned by comparison with earlier rounds of social science research in nanotechnology. “Social science” research is broadly defined to include publications in conventional social science as well as humanities, law, ethics, business, and policy fields. We examine the knowledge clusters underpinning social science publications in nanotechnology and synthetic biology using a methodology based on the analysis of cited references. Our analysis finds that social science research in synthetic biology already has traction and direction, rooted in an ethical, legal, and social implications framework. However, compared with nanotechnology, social science research in synthetic biology could further explore opportunities and openings for engagement, anticipatory, and downstream application perspectives that will help to build a wider platform for insights into current and future societal impacts.
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Author(s): Philip Shapira, Jan Youtie, and Yin Li
Organization(s): University of Manchester and Georgia Institute of Technology
Source: Journal of Responsible Innovation
Despite increasing awareness of the need to trace the trajectory of innovation system research, so far little attention has been given to quantitative depiction of the evolution of this fast-moving research field. This paper uses CiteSpace to demonstrate visually intellectual structures and developments. The study uses citation analysis to detect and visualize disciplinary distributions, keyword co-word networks and journal cocitation networks, highly cited references, as well as highly cited authors to identify intellectual turning points, pivotal points and emerging trends, in innovation systems system research from 1975 to 2012. As with other science mapping software tools (eg, Bibexcel and VantagePoint), CiteSpace can be used to produce and analyze co-occurrence network of key words and subject categories (co-word analysis), and co-citation networks of authors, documents and journals.
Author(s): Zhigao Liu, Yimei Yin, Weidong Liu, and Michael Dunford
Organization(s): Chinese Academy of Sciences, Beijing Union University, University of Sussex
We investigate the relationships between the citation impacts of scientific papers and the sources of funding which are acknowledged as having supported those publications. We examine several relationships potentially associated with funding including first citation, total citations and the chances of becoming highly cited. Furthermore, we explore evidence on the links between citations and types of funding by organization and also with combined measures of funding. In particular, we examine the relationship between funding intensity and funding variety and citation. Our empirical work focuses on six small advanced European economies, applying a zero inflated negative binomial model to a set of more than 240,000 papers authored by researchers from these countries. We find that funding is not related to the first citation but is significantly related to the number of citations and top percentile citation impact. Additionally, we find that citation impact is positively related to funding variety and negatively related with funding intensity. Finally there is an inverse relationship between the relative frequency of funding and citation impact. The results presented in the paper raise insights for the design of research programs and the structure of research funding and for the behavior and strategies of researchers and sponsoring organizations.
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Author(s): Abdullah Gök, John Rigby, Philip Shapira
Organization(s): MIoIR Manchester University
Source: Journal of the American Society for Information Science and Technology
Engineering education research (EER) is a relatively young field of inquiry, established with the
intent to improve the academic experiences of young and emerging engineers. While many
researchers’ perceptions of how to improve engineering education stem from traditional
classroom experiences, a select group of researchers belong to EER-oriented departments, labs,
and research centers. These on-campus resources create a formal bridge between EER-expert
networks and offer researchers an opportunity to collaborate with other like-minded individuals.
However, researchers lacking access to similar EER resources may be unable to establish
connections to engineering education’s expert community of practice.
The purpose of this paper is to answer the question “How is collaboration within the EER
community of practice impacted by an individual’s access to EER resources?” Formal
collaborations were catalogued using co-authorship data from publications in the Journal of
Engineering Education between the years 2008 to 2012. Influential researchers, collaboration
trends, critical brokers, and other hidden structures were analyzed using social network analysis
methods. Results of this study found that researchers on campuses lacking formal EER resources
are unable to broker connections into EER’s expert community of practice. Consequently, these
researchers may be unable to adopt best practices from and exchange relevant information with
the greater community.
Author(s): Scottie-Beth Fleming
Organization(s): Georgia Institute of Technology
Source: 121st American Society for Engineering Education (ASEE) Annual Conference and Exhibition
In order to enrich the existing scientific research evaluation methods for scientific researchers, institutions, regions or journals that engaged in some technical field, subject or field, the paper reviews the shortcoming in current visualization used for scientific research evaluation. And then, the paper designs and realizes three applications of social network visualization, first improves the traditional co-author network marking signature order, then integrates correlative network with dynamic threshold, and at last constructs citation network to convey the quantity, quality and influence of the object being evaluated. For each model, we propose a virtual example without real meaning to show the interpretation of the network map.
Author(s): Yunsheng Du, Yuqin Liu, Pengjun Qiu, Xiaohan Shen
Organization(s): Beijing Institute of Technology
Source: Modeling and Computation in Engineering III (45 technical papers from the 3rd International Conference on Modeling and Computation in Engineering)
Nurse scientists commonly weigh the impact of their work on the discipline of nursing as well as within the larger healthcare arena. Bibliometrics, a statistical method used in citation and content analysis, is a quantitative approach for calculating output and for analyzing value and merit of scientific output. Bibliometric mapping is a method for visually representing bibliometric data. A synthesis between creative design and information visualization, bibliometric mapping highlights the impact of given research on a discipline and has the potential to foster increased data comprehension. Widely used in the field of information science, bibliometrics has received less attention in nursing and healthcare. This paper describes the methodological considerations for bibliometrics, software that could be considered for citation analyses, and an exemplar that shows the visual richness of bibliometric mapping. Recommendations are made for facilitating bibliometric analyses.
Author(s): Paige M. Alfonzo, Teresa J. Sakraida, and Marie Hastings-Tolsma
Organization(s): University of Colorado
Source: Online Journal of Nursing Informatics
Research that integrates the social and natural sciences is vital to address many societal challenges, yet is difficult to arrange, conduct, and disseminate. This paper compares diffusion of the research supported by a unique U.S. National Science Foundation program on Human and Social Dynamics (“HSD”) with a matched group of heavily cited papers. Continue reading Distance and velocity measures: using citations to determine breadth and speed of research impact
This paper introduces author-level bibliometric co-occurrence network by discussing its history and contribution to the analysis of scholarly communication and intellectual structure. Continue reading Comparative study on structure and correlation among author co-occurrence networks in bibliometrics