Figure 5 - uploaded by Siluo Yang
Content may be subject to copyright.
Source publication
Bibliometrics, scientometrics, and informetrics (also called the three metrics) differ in subject background but are the same in theories, methods, technologies, and applications. Analyzing their current situation and relationships can help comprehensively understand the three fields. In this study, we collect the data of the three metrics through...
Context in source publication
Context 1
... latter analysis is based on the cluster name provided by Citespace. The clustering results are shown in Figs. 5, 6, and 7. Figure 5 shows five main subjects in bibliometrics: 1) research on the general development trend and influence of bibliometric analysis; 2) research based on bibliometric indicators of the scientific research output, the ranking of universities, and the evaluation on individual academic Figure 6 shows five main subjects in scientometrics; 1) application of scientometrics methods in biology, indicating the extensive application of scientometrics methods in other fields; 2) research on the quality of scientific research output and the research trend of scientometrics as a branch of science; 3) research on scientometrics methods (such as citation analysis and impact factors) based on journals and other research outputs, indicators (such as the H index), and their application; 4) development trend of scientific cooperation, cooperation model, and academic cooperation network based on scientific research output; 5) ranking or visualization of discipline contents, personnel, and journals through scientometrics methods, as well as research on the development trend of disciplines. Figure 7 shows five main subjects in informetrics; 1) research and application of the H index and other new metrics in network environment, as well as research on information systems; 2) research on the influence of articles and authors on the basis of informetrics methods and citation analysis; 3) research on the distribution and ranking of high-impact articles, authors, and journals based on the H index, as well as research on the model of informetrics; 4) research on the development of informetrics and its relationship with bibliometrics and scientometrics; 5) effect of network environment on the patterns of scientific research activities. ...
Similar publications
Bibliometrics, scientometrics, and informetrics (also called the three-metrics) are three related terms in metrology. With development of science and social science and continuation of metrology, convergence among these three terms has been developed a lot. Analyzing their current situation and relationships can promote comprehensively understandin...
Citations
... Informetrics uses informetric methods and mathematical models to analyze the distribution and ranking within information systems. While scientometrics focuses on scientific development trends (Yang et al., 2020), providing deeper insight into thematic structures and research trends. ...
Introduction
In an era where technology is revolutionizing the way business is done, specialists are continuously developing Interactive Voice Response (IVR) systems used in call centers in an attempt to meet the ever-changing needs of both customers and businesses. Before investing in an IVR system, call center managers must have a clear picture of the advantages and challenges associated with this technology, and for researchers, it is important to know what are the emerging topics that could be future research directions in the field. However, there is a lack of comprehensive reviews that present an overview of how IVR systems are used in call centers, and this paper aims to fill this gap in the literature by conducting a scientometric research on scientific production in the field.
Methods
A total of 284 documents indexed in the Web of Science database between 1991 and 2023 were analyzed using VOSviewer software. The scientometric analysis included a semantic examination of research trends and thematic clustering within the field.
Results
The semantic analysis of scientific production highlighted four main research directions: Automatic Speech Recognition, IVR flow optimization, Reliability of IVR systems as a methodology for studies, and Human-Computer Interaction for Development (HCI4D). These clusters highlight the intellectual structure of the field.
Discussion
The paper discusses the general intellectual structure of the field, with the four semantic groups being reviewed. Additionaly, emerging topics were identified and the advantages and challenges that accompany the use of this technology in call centers were discussed.
... Bibliometrics, informatics, and scientometrics share similar approaches, ideas, applications, and technologies. However, they differ in their topic backgrounds [31,32]. The bibliometric technique is a vital statistical instrument used to ...
... Bibliometrics, informatics, and scientometrics share similar approaches, ideas, applications, and technologies. However, they differ in their topic backgrounds [31,32]. The bibliometric technique is a vital statistical instrument used to identify and analyze the current areas of scientific knowledge, as well as to emphasize the relevant information on future prospects and supporting research [33]. ...
Cement-based composites (CBCs) are essential in the construction sector due to their cost-effectiveness, availability, and versatility, but they struggle with low tensile strength and poor heat resistance. Recent advancements have highlighted the potential of nanomaterials, particularly graphene oxide (GO), in enhancing the mechanical, thermal, and electrical properties of CBCs. This study aims to provide a comprehensive review of the incorporation of GO into cementitious composites, examining its impact on microstructure, mechanical properties, rheology, and durability; thus, a bibliometric review and scientometric analysis were conducted to thoroughly evaluate the existing literature. A total of 263 studies were selected for thorough study. It can be concluded that GO content acts as a pore filler, decreasing porosity by 23% and average pore size by 22%, while boosting compressive strength by up to 15% at a 0.05% concentration. It also enhances workability, stability, and resistance to chloride ingress, sulfate attack, alkali–silica reaction, and carbonation. Incorporating GO reduces cement consumption and carbon footprint, leading to more durable structures and supporting sustainable construction by efficiently utilizing waste materials. The optimal GO concentration for these benefits ranges from 0.03% to 0.1% by weight of cement, as higher concentrations may cause agglomeration. GO-modified cementitious materials are well suited for high-performance and durable applications, particularly in environments with chemical and mechanical stresses.
... Scientometrics is commonly referred to synonymously in the literature as bibliometrics, infometrics or scientific mapping. According to Yang & Yuan (2017), bibliometric, scientometric and infometric "differ in the degrees of utilization and recognition but are similar in the general direction". Examples of publication metrics used to assess scholarly productivity, impact and relevance are Impact factor, h-index, Journal impact quartile, Article Influence Score, CiteScore. ...
Bibliometric analysis is an important tool in scientific research designed to explore and analyse a range of scientific data. The purpose of this paper is to highlight the growing importance and relevance of business agility issues in the scientific community. The aim is to provide a brief insight into business agility through bibliometric analysis of articles included in the WOS and Scopus databases. As a result, a comparison of these databases is presented along with a description of the resulting clusters using the software tools VOSviewer and SciMAT. The area of interest in the databases is Business, Economics, Management and Finance in the publication years 1994-2023. The results show that although the databases overlap to some extent, there are some slight differences in terms of bibliometrics or scientometrics. Although the Scopus database had a higher number of publications, the number of keyword occurrences is higher in the database WOS. There are also slight differences in the most numerous keywords. In terms of clusters, the number is the same, but slight differences are also observed. Based on the analysis of the occurrence of keywords, it is possible to note an increased interest in the issue of agility, which is linked to a number of other areas of management. The Scopus database is recommended to study business agility.
... İlk kez 1969'da Pritchard, daha önceleri istatistik bibliyografyası olarak kullanılan kavrama karşılık bibliometri kavramını önermiştir (Broadus, 1987). 2007-2016 arasındaki 10 yıllık literatürün analiz edildiği çalışmada bibliometri teriminin bilim adamları tarafından daha fazla kullanılıp benimsendiği ortaya konulmuştur (Yang et al., 2020). Bibliometrinin bilimsel bir disiplin olarak ne zaman ortaya çıktığı ise tartışmalı bir konudur. ...
Günümüz popüler bilim kavramlarından biri olan Antroposen, insan toplumunun Dünya yüzey sistemleri üzerindeki etkisine (özellikle olumsuz manada) vurgu yapan disiplinler arası bir çalışma alanıdır. Jeologlar kavramın yeni bir zaman aralığı olup olmadığını tartışırken jeomorfologların bir kısmı insan faaliyetleri ile muazzam bir dönüşüm geçiren yapay peyzaja odaklanmıştır. Böylelikle Antroposen, Jeomorfoloji’de Antropojenik Jeomorfoloji olarak karşılık bulmuştur. Antropojenik Jeomorfoloji, yer şekillerinin oluşumu ve gelişiminde insanları üçüncü bir ajan olarak ele almakta ve insan yapımı yer şekillerine odaklanmaktadır. Bu çalışmanın amacı Antropojenik Jeomorfoloji’ye ilişkin araştırmaları, veri madenciliği ve niceliksel yolla ortaya koymaktır. Bu amaç doğrultusunda 01/02/2024 tarihinde Web of Science veri tabanında Antropojenik Jeomorfolojiye ilişkin anahtar kelimelerle gerçekleştirilen sorgu işlemi sonucu yayınlara ait veri setine ulaşılmıştır. Veri seti, temizleme ve filtreleme işlemlerine tabi tutulduktan sonra kalan 103 makale üzerinden bibliometrik analiz gerçekleştirilmiştir. R programlama dili sürüm 4.3.1 içinde yer alan bibliometrix paketinin bir ara yüzü olan biblioshiny kullanılarak veriler analiz edilerek görselleştirilmiştir. Bulgulara göre; 2000’li yılların ortalarından itibaren alandaki yayın sayısı artmaya başlamıştır. 2010 yılından sonra dalgalanmalar olsa da yayın sayısı dikkat çekici şekilde artmıştır. Nitekim yayınların %90’ı bu döneme aittir. Araştırma hacmi açısından öne çıkan ilk üç ülke ise İtalya, Polonya ve İngiltere’dir. Ancak alandaki çalışmalarda uluslararası iş birliğinin sınırlı olduğu görülmektedir. Araştırmalarda yazarların sıklıkla kullandığı kelimeler antropojenik yer şekilleri, Antropojenik Jeomorfoloji, jeomorfolojik haritalama, insan etkisi ve madenciliktir. Bu kelimeler kavramsal ve metodolojik anlamda gelişme içerisinde olan disiplindeki ana araştırma alanlarını yansıtması bakımından önemlidir. İnsan aktivitesi ile bozulan peyzaj, bu alanların restorasyonu ve antropojenik yer şekillerini jeomorfolojik haritalara entegre etme çabası da dikkat çekicidir. Sonuç olarak uzaktan algılama teknolojilerindeki gelişimlerin alana önemli bir katkı sağladığı görülmüştür. Çalışma bu alandaki bilimsel üretime genel bir bakış sunmakla beraber Antropojenik Jeomorfolojinin gelişimini doğrulama çabası olarak değerlendirilmelidir.
... It serves scientific decision making and management using information that is generally from scientific publications [1][2][3][4][5]. The terminology "scientometrics" ("naukometriya") was coined in 1969 [6] and obtained broad acceptance; it has grown in popularity as the journal Scientometrics was established in 1978 [5,7]. Scientometrics is commonly synonymously referred to as informetrics, bibliometry, bibliometrics, bibliometric analysis, science mapping, or knowledge structure in the literature, although these terms are essentially recognized as separate fields [7]. ...
... The terminology "scientometrics" ("naukometriya") was coined in 1969 [6] and obtained broad acceptance; it has grown in popularity as the journal Scientometrics was established in 1978 [5,7]. Scientometrics is commonly synonymously referred to as informetrics, bibliometry, bibliometrics, bibliometric analysis, science mapping, or knowledge structure in the literature, although these terms are essentially recognized as separate fields [7]. Scientometrics originated in information and library science, but it has evolved over time and has been widely applied in a variety of other disciplines in order to identify research landscapes (e.g., growth, structure, interrelationship, and productivity) or map historical footprints, emerging hotspots, or scholarly fields [8,9]. ...
Scientometrics is a quantitative and statistical approach that analyzes research on certain themes. It originated from information/library science but has been applied in various disciplines, including information science, library science, natural science, technology, engineering, medical sciences, and social sciences and humanities. Numerous scientometric studies have been carried out, but no study has attempted to investigate the overall research status of scientometrics. The objective of this study was to investigate the research status of scientometrics based on 16,225 publications archived in the Web of Science Core Collection between 1992 and 2020. The results show that there has been a marked increase in publications on scientometric studies over the past decades, with “Information Science Library Science” being the predominant discipline publishing scientometric studies, but scientometrics has been widely adopted in a variety of other disciplines (240 of 254 Web of Science categories). It was found that Web of Science, Vosviewer, and Scientometrics are the most utilized database, software, and journal for scientometric studies, respectively. The most productive author (Lutz Bornmann from the Max Planck Society, Germany), organization (University of Granada, Spain), and country (USA) are also identified. In addition, high-impact scientometric studies and the research landscape are analyzed through citation networks and the co-occurrence of keywords method.
... Scientometrics is a discipline that quantitatively analyzes researchers and research results with mathematical methods, reveals the scientific development process, quantifies scientific research activities with citation analysis and other methods, and provides a basis for scientific decision making and management [42][43][44]. The map of scientific knowledge is a bibliometric method [45]. ...
As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences.
... In this respect, incorporating the science mapping method helps to eliminate subjective interpretations, since it provides more insights into the investigated literature before conducting the systematic review [21]. Generally, to investigate the dynamics of literary production in a specific domain, science mapping studies make use of three distinct but overlapping methods: bibliometric analysis, scientometric analysis, and informetrics [22,23]. Compared to bibliometric analysis, which focuses primarily on reviewing the general properties of the relevant literature using mathematical and statistical methods, scientometric analysis extends beyond this to measure and analyze the literature to gain insights as to the practices of researchers and their socio-organizational structures. ...
Efficient planning and scheduling of operations within the offsite construction supply chain is critical for the successful completion of projects. Despite significant efforts and the application of various approaches to improve planning and scheduling practices, the existing literature in this field lacks coherence and a comprehensive review that establishes the scope, boundaries, and categorization of planning and scheduling in supply chain operations remains unexplored. To address this research gap, this paper presents a structured framework for supply chain management that encompasses strategic planning, master planning and scheduling, and detailed planning and scheduling. Through an analysis of the literature, this paper identifies areas that have received insufficient attention from the research community, specifically strategic planning, the procurement stage, and logistics operations. To advance the field, future research should focus on the development of integrated planning and scheduling models that simultaneously consider multiple operations and incorporate realistic features of transportation and onsite tasks.
... Fig. 1 below gives an overall view of these terminologies. According to the literature [1], the most important definitions of the related terminologies are that informetrics is the quantitative study of any form of information; it is not simply a bibliographic record or any social group, or is it limited to scientists. Scientometrics is the quantitative study of various kinds of intelligence processes in the development of science, and it uses quantitative methods to evaluate scientific research activities and thus guide science policy. ...
... They can be used interchangeably by authors; however, they can also differ between disciplines. For example, scientometrics is associated with the science of science, informetrics with information science and bibliometrics with library and document science [1]. ...
In this research work, we studied two main research questions. First, we explored how artificial intelligence (AI) publications are seen on various altmetric platforms. Second, by looking at publications in the field of AI, we examined whether bibliometrics and altmetrics are correlated. For each of the 8000 AI publications on the Web of Science (WoS) database with altmetric scores, we recorded the citation counts and altmetric scores. Pearson's correlation for the two lists of variables was found to be 0.2. This result is near to zero, indicating that there is weak correlation between citations and altmetric attention scores. Further, in the two top lists of publications according to citation count and altmetric score, respectively, we found only one paper in common. For the time being, altmetric scores are weakly correlated with citations in the AI publications. In terms of research impact, altmetrics should be regarded as complementary to traditional bibliometrics rather than as a replacement measure. Beside these results, this article gives an overview of this area of research (scientometrics and informetrics). All related terminologies are defined, and for the purpose of this study we focus on bibliometrics and altmetrics.
... The methodologies reveal the basic patterns of a particular subject in the literature, the connection points with different topics and fields, and the bibliometric elements of particular techniques. Although the theories put forward in the past and the tools and techniques used by these theories are similar, differences exist in the subjects focused on and the dimensions within the scope of the analysis (Hood and Wilson, 2001;Björneborn and Ingwersen, 2004;Garfield, 2009;Yang and Yuan, 2017). The methodologies include metrics, basic statistical summary tables, charts, and advanced text and network analytics that enable these visualizations (Glänzel and Schubert, 2004;Liu et al., 2014). ...
Designing a business intelligence-based monitoring platform for evaluating research collaborations within university networks: the case of UNICA - the Network of Universities from the Capitals of Europe.
Muhammet Damar, Güzin Özdağoğlu, and Luciano Saso
Introduction. The scope of the study is to provide decision support for academic networks to reveal the efficiency of the collaborations among the researchers. This research proposes a monitoring environment to evaluate collaboration patterns in all research areas and foster innovative, interdisciplinary, and international research.
Method. The paper presents a novel application framework for the Network of the Universities from the Capitals of Europe (UNICA) based on business intelligence. The framework is applied by analysing co-authorships through data from the Web of Science (2015 and 2020).
Analysis. Co-authorships between member universities are queried from the large-scale bibliographic data. A new bibliometric data warehouse is created with the integrated use of database operations with text analytics. Dashboards associated with the data warehouse contain many performance indicators and statistics based on interactive filters.
Results. The findings cover many features of the monitoring environment and statistics in various research domains (Life Science and Biomedicine, Physical Sciences, Technology, Social Sciences, Arts, and Humanities). User-friendly geographical maps visualized the most significant collaborations in various domains.
Conclusions. The study provides an intellectual contribution by revealing the differences in collaboration levels of the research areas and indicating the policy requirements to close these gaps.
DOI: https://doi.org/10.47989/irpaper945
Introduction
Scientific collaborations globally emerge as the scientific capacities of the countries go beyond their borders, and thus the dissemination of knowledge also accelerates in the same direction (Leydesdorff and Wagner, 2008). National and international academic collaborations improve inter-institutional relationships and provide appropriate environments for creating values that increase scientific and industrial benefits by coming together from different perspectives. Policymakers should follow effective measures to reveal the collaborative linkages among the countries and thus create new policies to balance the asymmetric benefits from the collaborations (Chen et al., 2019).
Data regarding the number of papers that include authors from different countries may provide partial information about collaborations (Katz and Martin, 1997). Still, we need these data to initialize the investigation since co-authorships are more straightforward means of evaluating collaboration (Melin, 2000). Tsai et al. (2016) emphasized a similar idea by stating that revealing the big picture necessitates scientific research to make collaborations visible and increase their widespread impact. Methodologies such as bibliometrics and scientometrics (Jacobs, 2010; Wagner and Leydesdorff, 2005) are the approaches developed for this purpose. The methodologies reveal the basic patterns of a particular subject in the literature, the connection points with different topics and fields, and the bibliometric elements of particular techniques. Although the theories put forward in the past and the tools and techniques used by these theories are similar, differences exist in the subjects focused on and the dimensions within the scope of the analysis (Hood and Wilson, 2001; Björneborn and Ingwersen, 2004; Garfield, 2009; Yang and Yuan, 2017). The methodologies include metrics, basic statistical summary tables, charts, and advanced text and network analytics that enable these visualizations (Glänzel and Schubert, 2004; Liu et al., 2014).
The co-authorship aspect of bibliometrics is frequently used to identify research collaborations (Melin and Persson, 1996; Ponomariov and Boardman, 2016). In addition to the indicators, tabular reports and co-authorship networks (Glänzel and Schubert, 2004; Kumar, 2015) identify collaborations among the authors, institutions, organizations, journals, and other entities in the bibliometric datasets. It is also possible to add more dimensions as legends on these networks, e.g., time and clusters. Many studies investigate collaborative research within the selected frameworks, e.g., countries, research areas, and institutions; and the “Theoretical background” section exemplifies these publications.
International science arises as a communications network distinct from national systems with its own internal dynamics (Hicks and Katz, 1996). National systems have regulations and structures in place to facilitate scientific communication, whereas the network operates primarily as a self-organizing system at the global level (Leydesdorff and Wagner, 2008). This self-organizing nature and its dynamics should still produce outcomes based on common strategies and targets, which necessitate “strategic intelligence” from a managerial perspective. Science and technology foresight, innovation policy evaluation, and technology assessment are all typical strategic intelligence techniques (Rotolo et al., 2017), and scientometrics or related methodologies can serve as a complementary component for intelligence. Combining the abovementioned components and perspectives, this study proposes an application framework based on business intelligence to reveal collaboration patterns in academic or university networks, e.g., UNICA.
Bibliometric data can mediate the assessment of joint science and technology efforts to see the outputs of policies and activities carried out in strategic alliances in the scope of strategic intelligence if the relevant data is processed in a business intelligence framework. Business intelligence covers the processes and technologies that businesses have used to manage large-scale raw data in the past and current data and then turn them into meaningful information. Because of this significant information, businesses or institutions can see trends in operations and make predictions for the future. The institution can create a series of valuable strategies that will enable effective decisions with predicted results (Foley and Guillemette, 2010; Elena, 2011; Pham, 2016). Although the basic process of the business intelligence system is based on the transformation of data into knowledge, decisions, and, eventually, action, it also has a general architecture (Popovič et al., 2009; Sharda et al., 2015; Skyrius et al., 2018).
In this context, the proposed business intelligence application framework is developed to track the dynamics and patterns of collaborations. UNICA is a collaborative academic network with 55 universities from 37 European capitals, and this association is a strategic alliance with a clear mission, vision, and goals (UNICA, 2021). Universities and academic networks such as UNICA need decision support to maintain their activities; thus, business intelligence would provide a relevant set of tools and technologies for supplying the necessary support. For university networks in particular, the main goal is to improve collaborations among members, and the network executives should monitor the level of collaboration. For this purpose, bibliometric data provided by the scientific databases comprise a source for the raw data to be processed in business intelligence environments in order to develop such a monitoring platform. We created a data warehouse from pairwise co-authorships obtained from pre-processing the bibliometric data between 2015-2020 retrieved from the Web of Science. Collaboration maps and other dashboard components developed from this data warehouse provide interactive navigation between various visuals and enable querying according to the research areas and different dimensions.
In summary, the paper presents the procedure for handling a large-scale bibliometric dataset on the basis of the proposed approach for obtaining a relevant data warehouse for obtaining subsets in order to discover co-authorships and research areas according to geographical location. Accordingly, the system took its roots from BI to develop a customized monitoring and reporting platform. The application provides multidimensional insights into discovering collaborations while suggesting a process-monitoring approach for UNICA members and managers.
Theoretical background
This research presents an application framework that uncovers the collaborations between researchers in an academic network, i.e., UNICA, regarding business intelligence and co-authorship connections. The development process of the application framework and its monitoring platform has both socio-technical and methodological components. In this context, this section summarizes the related studies and ultimately explains the position of this study in the literature.
Scientific collaborations and networks
Since Francis Bacon, debate has continued regarding which knowledge domain is essential for the future of societies, which scientific research should be fostered, and how to choose science and technology policies (Stehr, 2005). The reason behind the current version of the debate is that the major economies of recent years have been developed based on knowledge (Dunning, 2000). Higher education institutions that provide strategic and transformational resources are among the key players in creating and growing this knowledge. The essence of knowledge policy and evaluation goes beyond traditional science and technology policies. There is a need for regulations and initiatives that will guide each nation on its path to becoming a knowledge society that will encourage the creation of essential resources (Schmidt, 2007). Creating relevant knowledge goes beyond institutions and even countries and has become one of the underlying reasons for establishing international networks.
Managing research collaborations between countries requires special efforts since research dynamics and conditions are not similar. Chen et al. (2019) investigated the phenomenon of international research collaboration in terms of its historical background. They emphasized the importance of such a review for discovering the dynamics of a research domain. Accordingly, Wagner et al. (2019) conducted in-depth research to investigate the novelty, creativity, and conventionality in internationally collaborated papers and isolated many factors that impact collaborative research efforts in basic areas. Academic networks provide the necessary environments to encourage and support collaborations by means of well-established research policies.
Literature discussing the increase in such international linkages or networks in science has been growing rapidly. These discussions take shape under three categories: quantitative approaches to reveal the co-authorship networks through scientometrics or related methodologies; social science research on the dynamics of collaborations; and policy research that analyses the contributions of collaborations for funding and outputs (Wagner and Leydesdorff, 2005).
Social and political research shows that higher education institutions are trying to maintain and improve their position in an increasingly competitive environment in responding to social, economic, and political changes (Daniel, 2015). In addition, countries that desire to maintain the intellectual developments growing at the global level in their own countries seek out new science and technology policies in this direction, and higher education institutions try to adapt to these policies (Skolnikoff, 1993). Although higher education institutions and individuals compete, it is observed that effective collaboration mechanisms have been established in the light of common strategies and objectives when the subject is to produce science and knowledge and to share funds and resources for this purpose. For example, special development assistance initiatives between Scandinavian, Latin American, and African countries have resulted in the formation of global linkages or networks between these countries. Project calls, in which the European Union has dramatically supported research proposals of two or more countries, also affect the growth of the network in the relevant regions (Wagner and Leydesdorff, 2005).
As scientific capacity increases and cooperation develops, information flows between institutions and countries also progress. In order to institutionalize these collaborations, networks are formed around specific common criteria, such as region, capitals, or research domains. The European University Association, the Asian University Association, the Latin-American Network of Universities, UNICA, and Association of African Universities are only a few examples of these networks. Countries within this sharing environment may need to produce more conscious policies and actions for the community they are members of, and scientifically more advanced members may need to support and encourage less developed members to improve cooperation and research quality in the entire network. At this point, the effectiveness of the established network comes to the fore. Investments in science, policies developed, economic gains, and global collaborations may all be measured (Leydesdorff and Wagner, 2008). Achievement of efficiency and effectiveness may only be possible by measuring the collective scientific productivity of community members. Such measurement requires large-scale data including many dimensions, e.g., bibliometric data with geographical dimensions.
Various qualitative and quantitative approaches, such as data visualizations, graph modelling, clustering, and cognitive mapping, can process these dimensions. The approaches in this research domain enable discovering, integrating, and reusing scientific productions from bibliometric data and play a crucial role in innovative research (Shotton et al., 2009; Osinska and Klimas, 2021). Domain knowledge graph modelling, for example, is one of the essential techniques used to express the crucial characteristics of a specific study field. It is necessary for the visualization, retrieval, bibliometric analysis, and findings of scientific publications. The information that represents academic production significantly aids this capacity. Title, author, publisher, subject keywords, and other elements traditionally describe metadata (Leydesdorff and Persson, 2010; Liu et al., 2014).
Designing an effective monitoring mechanism can be achieved with an information system (Chung and Zeng, 2016) structure for users at all levels. Such an information system should have data storage, pre-processing, visualization, analysis, and reporting capabilities. In this sense, researchers can embed data analytics in the scope of bibliometrics and related methodologies into a holistic information system that would serve as a monitoring platform for networks. Therefore, they need data-driven decision support mechanisms with the help of the significant portfolio of bibliometric data and the technological infrastructure of business intelligence.
Related work on research collaborations through co-authorships
Various research papers in the literature focus on identifying collaborations. The characteristics that make these studies original and allow them to be published in prestigious journals are that each research project has its own dynamics and setting for providing information in different dimensions within each framework. Therefore, these studies inspire and contribute to this paper in terms of their findings and research design.
For example, the scope of the studies that investigate collaborations regarding co-authorships has been organized depending on the research areas. Cunningham and Dillon (1997) identified authorship patterns in information systems in articles published in the selected journals by creating summary tables of authors’ collaborations, gender, location, institutions, and journals. Velden et al. (2010) evaluated the patterns of collaboration in co-authorship networks at the mesoscopic level by integrating the data collected from the interviews and the statistical data obtained from network analysis in three research fields of chemistry. Khasseh et al. (2017) investigated the relationship between researchers’ productivity and performance with their centrality measurements of researchers in infometrics by calculating the social network metrics in UCI.net to determine the rankings among journals and authors regarding the research area.
Many researchers have investigated the cooperation of various countries, for example, Chinese scientific collaboration between China and the US (Tang and Shapira, 2011), scientific collaboration in the tourism field between Australia and New Zealand (Benckendorff, 2010), and patterns of collaboration at the country and continental levels in Africa (Pouris and Ho, 2014).
Visualising and interpreting data on a particular network can become complicated due to the rapidly increasing data size. However, researchers in information technology have developed many tools and techniques (Miller, 2011) to facilitate these analyses, and advances in information enable new capabilities for network analysis such as identifying opinion leaders in discussion forums (Song et al., 2007); highlighting expertise in networks (Zhang et al., 2007); analysing the research trends and collaboration patterns in Iran (Nikzad et al., 2011), Spain (Ardanuy, 2012), and Hungary (Inzelt et al., 2009); evaluating the authorship trends and collaboration patterns with the help of VOSviewer software (Koseoglu et al., 2018); comparing collaborations in different field of science (Lariviere et al., 2006); discovering collaboration levels regarding the trends in authorship patterns and the authors’ collaborative research (Khaparde and Pawar, 2013); revealing business stakeholders (Chung et al., 2009); analysing inter-university and international collaboration networks (Olmeda‐Gómez et al., 2009); investigating collaboration patterns in national and international scientific databases, i.e., the Web of Science Social Science Citation Index (WoS-SSCI) and ULAKBIM; and visualizing research patterns of infectious disease transmission (Zeng et al., 2010).
Akakandelwa (2009) conducted informetric research on papers published by faculty members at the University of Zambia using the Thomson Scientific database to discover authorship patterns and collaboration. Abramo et al. (2009) investigated research collaboration between Italian universities and industry with the help of bibliometric analysis on the dataset retrieved from the Observatory of Public Research database. Abramo et al. generated a data warehouse based on their criteria and revealed collaboration patterns based on several dimensions, i.e., sectors, journals, and research areas.
A few papers exist where locational framing has been reduced to institutional levels. Perianes-Rodríguez et al. (2010) developed a method for detecting, identifying, and visualizing research structures through co-authorship networks along with the internal dynamics at the Carlos III University of Madrid. The implementation of the proposed approach was presented using a bibliometric dataset that was retrieved from Web of Science using the papers belonging to the academics at the selected unit. Cheng et al. (2013) identified the degree and type of research collaboration in Malaysia through co-authorships. Lundberg et al. (2006) focused on the role of co-authorships for identifying collaborations and evaluated the usability of co-authorships for university-industry collaboration by comparing the results with industrial funding information.
Researchers have also emphasized journal-level collaborations in other papers. For example, Hou et al. (2008) developed collaboration networks for the journal Scientometrics from records of its articles in Web of Science by conducting social network analysis, co-occurrence analysis, cluster analysis, and frequency analysis of title words. Hou et al. visualised the microstructure of collaboration networks in the field of scientometrics during the 1978-2004 period and presented the rankings among the authors, collaborative studies, and keywords. Mohammadamin et al. (2017) focused on the same journal to reveal collaborations through co-authorship networks and related metrics to reveal rankings among the authors and institutions. Elango and Rajendran (2012) demonstrated the authorship trends and collaboration patterns in marine science based on the bibliometric data retrieved from the Indian Journal of Marine Sciences. They applied metrics including a collaboration index, a collaboration co-efficient, and a dominance factor in addition to the test of Lotka’s law.
Although the focus of the studies included here is on collaborations investigated through bibliometric data and in the context of co-authorship, the element that distinguishes the reflections from each other is how the concept of co-authorship is handled. While metrics and basic statistics are clearly at the forefront in some studies, in others, network visuals and performance indicators of the cooperation density in these networks stand out.
Analysing co-authorships through bibliometrics requires special tools, e.g., Google Earth (Google Maps); visualising networks, e.g., Pajek and Gephi; or software packages, e.g., VOSviewer and CiteSpace; and reporting summaries, e.g., Web of Science and Scopus. These tools overlay the network of relationships among addresses in scientific publications and visualize patterns of international collaboration using a projection on a world map (Hicks and Katz, 1996). Each tool has different features, and each application has various constraints (Leydesdorff and Persson, 2010).
The originality of the paper
The culture of compliance with external stakeholders and policymakers has continually increased its importance (Olsen, 2014). Higher education institutions are under increasing pressure to be more accountable to their stakeholders (Macfadyen et al., 2014). When relevant data are achievable, analysts and decision-makers can gather key inputs which can develop knowledge with a higher future impact such as geographical distributions of key organisational actors in collaborative networks and revealing main scientific and technological fields (Rotolo et al., 2017). Business intelligence volume and scale of available data, visualisation, and reporting technologies have enabled higher education institutions to develop interactive decision support tools that facilitate institutional decision-makers to access information specific enough to support their decision-making needs (Daniel, 2015; McCoy and Rosenbaum, 2019; Williamson, 2016). In this context, this paper proposes a business intelligence framework with various dashboards that can serve as a decision support system for monitoring the level of research collaborations in networks.
Similar to other studies, this study measures collaboration levels by extracting and analysing co-authorship information from bibliometric data. Unlike existing studies, this research primarily offers an application framework for large networks where higher education institutions are members from various countries. This paper provides an efficient application framework designed in a business intelligence environment to monitor research collaboration levels in different dimensions, such as the main research and sub-areas, by using co-authorships, country, institutions, and other features observable in bibliometric datasets. Therefore, the platform provides decision support to users at different levels, e.g., member institutions and network executives, by offering customisable visuals where many more dimensions can be processed interactively with the help of business intelligence techniques and tools for efficiently managing information with relevant bibliometric data. In this sense, the findings revealed the intensity of collaborations in basic or main research areas and presented many insights about the areas in which collaborations are relatively few. Therefore, the results provided an information infrastructure for the network to develop the policies necessary to improve research impact.
Different platforms collect and process bibliographic data and produce reports for institutions including the Current Research Information System, InCites, and SciVal (EuroCRIS, 2022; Elsevier, 2022; Özel et al., 2020). In particular, the Current Research Information System has an integrated structure that can also monitor inter-institutional research (Sivertsen, 2019; Schlattmann, 2017). What makes this study different from the existing platforms is that it is a voluntary effort developed entirely for the specific needs of the relevant network, without requesting data from its members.
The president of UNICA participated in the dashboards’ requirement analysis and design processes prepared through a large-scale data set of 55 different institutions. The resulting design developed gradually according to feedback and recommendations. Although this business intelligence based monitoring platform processes large-scale and complicated data and analytics behind the scenes, its use does not require advanced skills in bibliometrics, scientometrics, or related methodologies. One of the critical features of the system design is its capacity for flexible expansion and development combining new dimensions and tables in the database and for setting new analytics and visuals by integrating institutional data with bibliometric data.
... Scientometrics was first defined by Mikołajczyk and Grochowski (2018) as "a quantitative study of the research on the development of science". This technique measures the citation processes, impact of research, and plots the existing knowledge and its development in the research area based on academic literature datasets (Siluo and Qingli 2017). The scientometric analysis facilitates the visualization and mapping of a knowledge domain that then helps researchers to analyze the intellectual landscape of a knowledge domain and find the research problems that may need to be solved, along with the techniques the researchers have developed to solve similar research problems (Su and Lee 2010). ...
The application of deep learning in construction has attracted increasing attention among researchers in recent years. In this review article, comprehensive scientometric analysis and critical review were performed to analyze the state-of-the-art literature on the application of deep learning in construction. This research used the science mapping method to quantitatively and systematically analyze 423 related bibliographic records retrieved from the Scopus database, and further, a critical review was performed on the collected themes of all the related publications. The results of the critical review indicate that deep convolution neural networks, you only look once, single-shot detectors, recurrent neural networks, residual neural networks, and fast region-based convolution neural networks have been the most widely used deep-learning methods in the construction industry. The most commonly addressed problems in the construction industry using deep-learning methods include classification of construction equipment, worker's safety helmet detection, ergonomics analysis, image enhancement, and feature extraction. This paper provides an in-depth understanding and big-picture overview of the existing literature along with the challenges and future direction of research on deep learning in construction.
