Category Archives: Interdisciplinarity

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University (Full-Text)

Citation analysis, Interdisciplinarity, Research Examples, Research profiling, VOSViewer

This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at
the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicine engineering interdisplinarity. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicine engineering.

For FULL-TEXT https://doi.org/10.1007/s12204-022-2418-5

Author(s): WANG Qingwen, CUI Tingting, DENG Peiwen
Organization(s): Shanghai Jiao Tong University
Source: Journal of Shanghai Jiao Tong University
Year: 2022

The Relationship between Forward and Backward Diversity in CORE Datasets

Interdisciplinarity, Research Examples, VantagePoint,

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

https://doi.org/10.1007/s11192-019-03163-3

Author(s): Stephen F. Carley, Seokbeom Kwon, Alan L. Porter, Jan L. Youtie
Organization(S): Search Technology, Georgia Institute of Technology
Source: Scientometrics
Year: 2019

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

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

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Interdisciplinarity, Research Examples

Formal concept analysis (FCA) and concept lattice theory (CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration. We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research (IDR) topics in Information & Library Science (LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.

Findings: The CLT approach is suitable for IDR topic identification and predictions.

Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.

Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.

http://manu47.magtech.com.cn/Jwk3_jdis/CN/abstract/abstract317.shtml

Author(s): Haiyun Xu, Chao Wang, Kun Dong, Zenghui Yue
Organization(s): Institute of Scientific and Technical Information of China, Qilu University of Technology, Shandong University of Technology, Jining Medical University
Source: Journal of Data and Information Science
Year: 2019

Interdisciplinarity and collaboration: on the relationship between disciplinary diversity in departmental affiliations and reference lists

Interdisciplinarity, Research Examples

This study explores the characteristics of scientific activity patterns through co-author affiliations to obtain new insights into interdisciplinary research. To classify the interdisciplinarity in research, we explored and compared two different approaches: the diversity of disciplines reflected in the listed affiliations of the authors and the diversity of the subject categories reflected in the reference list. To assess the diversity in departmental affiliations, we developed an explorative methodology that retrieves feature words from a combination of manual work and the thesaurus function in the Thomson Data Analyzer text mining tool. To assess the diversity in references, we followed the conventional approach applied in previous work. With both approaches, we relied on diversity as the measure for assessing interdisciplinarity of 157,710 articles published in PloS One (2007–2016). Based on a comparison between the results of both approaches, our study confirms that different methodologies and indicators can produce seriously inconsistent, and even contradictory results. In addition, different indicators may capture different understandings of such a multi-faceted concept as interdisciplinarity. Our results are summarized in a schematic representation of this twofold perspective as a method of indexing the different types of interdisciplinarity commonly found in research studies.

https://doi.org/10.1007/s11192-018-2853-0

Author(s): Lin Zhang, Beibei Sun, Zaida Chinchilla-Rodríguez, Lixin Chen, Ying Huang
Organization(s): Wuhan University, North China University of Water Resources and Electric Power
Source: Scientometrics
Year: 2018

Measuring and visualizing research collaboration and productivity (full-text)

Interdisciplinarity, Research Examples, Science mapping

This paper presents findings of a quasi-experimental assessment to gauge the research productivity and degree of interdisciplinarity of research center outputs. Of special interest, we share an enriched visualization of research co-authoring patterns.

We compile publications by 45 researchers in each of 1) the iUTAH project, which we consider here to be analogous to a “research center,” 2) CG1— a comparison group of participants in two other Utah environmental research centers, and 3) CG2—a comparison group of Utah university environmental researchers not associated with a research center. We draw bibliometric data from Web of Science and from Google Scholar. We gather publications for a period before iUTAH had been established (2010–2012) and a period after (2014–2016). We compare these research outputs in terms of publications and citations thereto. We also measure interdisciplinarity using Integration scoring and generate science overlay maps to locate the research publications across disciplines.

We find that participation in the iUTAH project appears to increase research outputs (publications in the After period) and increase research citation rates relative to the comparison group researchers (although CG1 research remains most cited, as it was in the Before period). Most notably, participation in iUTAH markedly increases co-authoring among researchers—in general; and for junior, as well as senior, faculty; for men and women: across organizations; and across disciplines.

The quasi-experimental design necessarily generates suggestive, not definitively causal, findings because of the imperfect controls.

This study demonstrates a viable approach for research assessment of a center or program for which random assignment of control groups is not possible. It illustrates use of bibliometric indicators to inform R&D program management. New visualizations of researcher collaboration provide compelling comparisons of the extent and nature of social networking among target cohort.ings of a

For full-text DOI: https://doi.org/10.2478/jdis-2018-0004

Author(s): Jon Garner, Alan L. Porter, Andreas Leidolf, Michelle Baker
Organization(s): Georgia Institute of Technology, Utah State Universit
Source: Journal of Data and Information Science (JDIS)
Year: 2018

Lessons from Ten Years of Nanotechnology Bibliometric Analysis

Interdisciplinarity, Network analysis, Research evaluation, Research profiling, Research Type

This paper 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.

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:

  • 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://smartech.gatech.edu/handle/1853/55931?show=full

Author(s): Jan Youtie, Alan Porter, Philip Shapira, Nils Newman
Organization: Georgia Institute of Technology
Source: OECD Blue Sky Forum on Science and Innovation Indicators
Year: 2016

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

Data Type, Funding, Interdisciplinarity, Research Examples, Research Type

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area? This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data. Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period. We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields. We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.

FULL-TEXT at http://dx.doi.org/10.1371/journal.pone.0154509

Author(s): Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang
Organization(s): Beijing Institute of Technology; Georgia Institute of Technology
Source: PLoS ONE
Year: 2016

How Multidisciplinary are the Multidisciplinary Journals Science and Nature?

Interdisciplinarity, Research Examples

Interest in cross-disciplinary research knowledge interchange runs high. Review processes at funding agencies, such as the U.S. National Science Foundation, consider plans to disseminate research across disciplinary bounds. Publication in the leading multidisciplinary journals, Nature and Science, may signify the epitome of successful interdisciplinary integration of research knowledge and cross-disciplinary dissemination of findings. But how interdisciplinary are they? The journals are multidisciplinary, but do the individual articles themselves draw upon multiple fields of knowledge and does their influence span disciplines? This research compares articles in three fields (Cell Biology, Physical Chemistry, and Cognitive Science) published in a leading disciplinary journal in each field to those published in Nature and Science. We find comparable degrees of interdisciplinary integration and only modest differences in cross-disciplinary diffusion. That said, though the rate of out-of-field diffusion might be comparable, the sheer reach of Nature and Science, indicated by their potent Journal Impact Factors, means that the diffusion of knowledge therein can far exceed that of leading disciplinary journals in some fields (such as Physical Chemistry and Cognitive Science in our samples).

FULL-TEXT at http://dx.doi.org/10.1371/journal.pone.0152637

Author(s): Gregg E. A. Solomon , Stephen Carley, and Alan L. Porter
Organization(s): Harvard University, Georgia Institute of Technology
Source: PlosONE
Year: 2016