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The purpose of this paper is to develop an analytical framework toward facilitating the quantitative measurement of interdisciplinary understanding regarding sustainable energy systems with an application in the area of capacity-building projects in higher education.

The analytical framework is developed using the portfolio representation measurement approach in combination with a survey questionnaire. The subsequent assessment is carried out using the statistical measure of mean signed deviation to capture variation from an established baseline across the project group and visualized via radar diagrams.

The results provide a quantitative assessment framework for evaluating the degree of interdisciplinary understanding in the project groups. The application of the framework to the DESIRE project indicates the most significant degree of variation across economic and regulatory dimensions of sustainability. Discrepancies in general and educational contexts are observed.

The exploitable value of the results is sensitive to the derivation of composite indicators of the dimensions defined as well as the survey design. The case study was carried out on an ex-post basis, potentially biasing the results reported and limiting their interpretability and theoretical value.

The analytical framework can be used as a basis for assessing and engaging in discussions on interdisciplinarity understanding at the outset of capacity-building projects.

The contribution of this paper is practical in scope and entails the development of a quantitative framework for measuring interdisciplinarity in the specific context of capacity-building projects in the field of sustainability research in higher education institutions.

The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of subject categories using Stirling’s diversity index and specialization index.

Interdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling’s diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities.

Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities with computer science, business and economics, mathematics, and biotechnology and applied microbiology as the core. Four major discipline communities in Big Data research represent different directions with different development statuses and trends. Community 1, with computer science as the core, is the most mature and central to the whole interdisciplinary network. Accounting for all network indicators, computer science, engineering, business and economics, social sciences, and mathematics are the most important disciplines in Big Data research.

This study deepens our understanding of the degree and trend of interdisciplinary collaboration in Big Data research through a longitudinal study and quantitative measures based on two indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and characteristics of related developments of a specific research area, or to conduct comparative studies between different research areas.

This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application.

Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis.

As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one.

This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted.

This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS.

The purpose of this paper is to identify criteria for and definitions of disciplinarity, and how they differ between different types of literature.

This synthesis is achieved through a purposive review of three types of literature: explicit conceptualizations of disciplinarity; narrative histories of disciplines; and operationalizations of disciplinarity.

Each angle of discussing disciplinarity presents distinct criteria. However, there are a few common axes upon which conceptualizations, disciplinary narratives, and measurements revolve: communication, social features, topical coherence, and institutions.

There is considerable ambiguity in the concept of a discipline. This is of particular concern in a heightened assessment culture, where decisions about funding and resource allocation are often discipline-dependent (or focussed exclusively on interdisciplinary endeavors). This work explores the varied nature of disciplinarity and, through synthesis of the literature, presents a framework of criteria that can be used to guide science policy makers, scientometricians, administrators, and others interested in defining, constructing, and evaluating disciplines.

This paper aims to explore the characteristics of knowledge diffusion in library and information science (LIS) to reveal the impact of knowledge in LIS on other disciplines and the disciplinary status of LIS.

Taking the 573 highly cited papers (HCP) of LIS during the years 2000–2019 in Web of Science and 85,638 papers citing them from non-LIS disciplines as the analysis object, this paper analysed the disciplines to which the citing papers belonged regarding the Biglan model, and the topics and their characteristics of the citing disciplines using latent Dirichlet allocation topic clustering.

The results showed that the knowledge in LIS was exported to multiple disciplines and topics. (1) Citations from other disciplines were overall increasing, and the main citing disciplines, mainly from applied science disciplines, were medicine, computer science, management, economics, education, sociology, psychology, journalism and communication, earth science, engineering, biology, political science, chemistry and agronomy. However, those disciplines had fewer citations to LIS during for the years from 2000 to 2004, with rapid growth in the next three time periods. (2) The citing papers had various topics and showed an increasing trend in quantity. Moreover, topics of different disciplines from 2000 to 2019 had various characteristics.

From the perspective of discipline and topic, this study analyses papers citing the HCP of LIS from non-LIS disciplines, revealing the impact of knowledge in LIS on other disciplines.

Synergy indicators and social network analysis (SNA), as practical tools, provide the possibility of explaining the pattern of scientific collaboration and visualization of network relations. Recognition of scientific capacities is the basis of synergy. The present study aims to measure and discover the synergistic networks of COVID-19’s top papers at the level of co-authorship, countries, journals, bibliographic couples and titles.

The synergy indicator, co-authorship co-citation network analysis methods were applied. The research population comprises COVID-19’s top papers indexed in Essential Science Indicator and Web of Science Core Collection 2020 and 2021. Excel 2016, UCINET 6.528.0.0 2017, NetDraw, Ravar Matrix, VOSviewer version 1.6.14 and Python 3.9.5 were applied to analyze the data and visualize the networks.

The findings indicate that considering the three possible possibilities for authors, countries and journals, more redundancy and information are created and potential for further cooperation is observed. The synergy of scientific collaboration has revealed that “Wang, Y,” “USA” and “Science of the Total Environment” have the most effective capabilities and results. “Guan (2020b)” and “Zhou (2020)” are bibliographic couplings that have received the most citations. The keywords “CORONAVIRUS DISEASE 2019 (COVID-19)” were the most frequent in article titles.

In a circumstance that the world is suffering from a COVID-19 pandemic and all scientists are conducting various researches to discover vaccines, medicines and new treatment methods, scientometric studies, and analysis of social networks of COVID-19 publications to be able to specify the synergy rate and the scientific collaboration networks, are not only innovative and original but also of great importance and priority; SNA tools along with the synergy indicator is capable of visualizing the complicated and multifaceted pattern of scientific collaboration in COVID-19. As a result, analyses can help identify existing capacities and define a new space for using COVID-19 researchers’ capabilities.

Although interdisciplinary research is an increasing trend in scientific funding projects, they are suffering from a lower probability of being funded. The purpose of this paper is to analyze the current situation on successful case of funding application and provides suggestions on how libraries can expand services to help scientific funding application.

This paper utilizes the co-occurrences of disciplinary application codes to construct an interdisciplinary knowledge flow network. Based on 193517 sponsored projects of the National Natural Science Foundation of China, the authors study the interdisciplinary flow of knowledge and investigate the evolution of network structure using social network analysis.

Results show that the interdisciplinary knowledge flow network is not only a small-world network but also a scale-free network. Two main knowledge flow paths across scientific departments exist, showing the heterogeneity of knowledge distributions across scientific disciplines. The authors also find that if two disciplines in the same scientific department both have a wide influence to other disciplines, they are more prone to link together and create a knowledge chain.

Funding consultation currently has not occupied an advisory role either in library services or in the research team. This paper conducts a co-occurrences network analysis of interdisciplinary knowledge flow in scientific funding projects. Considering the complexity of funding application and the advantage of traditional library services on information collection, integration, and utilization, the authors conclude the possibility and necessity of embedding funding consultation in traditional library services.

The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links.

Three SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles.

The resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time.

This is the first study using Persson’s combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis.

Research objects, such as datasets and classification standards, are difficult to be incorporated into a document-centric framework of citations, which relies on unique citable works. The Diagnostic and Statistical Manual for Mental Disorder (DSM)—a dominant classification scheme used for mental disorder diagnosis—however provides a unique lens on examining citations to a research object, given that it straddles the boundaries as a single research object with changing manifestations.

Using over 180,000 citations received by the DSM, this paper analyzes how the citation history of DSM is represented by its various versions, and how it is cited in different knowledge domains as an important boundary object.

It shows that all recent DSM versions exhibit a similar citation cascading pattern, which is characterized by a strong replacement effect between two successive versions. Moreover, the shift of the disciplinary contexts of DSM citations can be largely explained by different DSM versions as distinct epistemic objects.

Based on these results, the authors argue that all DSM versions should be treated as a series of connected but distinct citable objects. The work closes with a discussion of the ways in which the existing scholarly infrastructure can be reconfigured to acknowledge and trace a broader array of research objects.

This paper connects quantitative methods and an important sociological concept, i.e. boundary object, to offer deeper insights into the scholarly communication system. Moreover, this work also evaluates how versioning, as a significant yet overlooked attribute of information resources, influenced the citation patterns of citable objects, which will contribute to more material-oriented scientific infrastructures.

Industry–university–research cooperation (IURC) is a crucial way to build an innovative country. How to improve the effectiveness of IURC has become an important issue to be solved urgently.

This paper studies the data of industry, university and research activities in various regions of China from 2016 to 2018 and analyzes the impact mechanism of innovation input and open innovation environment on the effectiveness of IURC based on innovation value chain theory.

This research finds that innovative talent input has an inverted U-shaped impact on the effectiveness of IURC. When there are more innovative funds invested, the marginal effect of IURC will decrease. When innovative talent input exceeds a certain value, the open innovation environment can alleviate the positive marginal effect of its decline.

This paper contributes to the literature and provides practical guidelines for improving the efficacy of IURC.