Collaborative Networks Research Articles

Creative Hubs and the Cultural Divide: Exploring European Creative Collaborations

Abstract The economies generated within the creative and cultural sectors are playing an increasing role in international competition between territories. Collaborations and synergies between territories can create the right conditions for the overall growth of cultural economies and make the them more attractive and better performing. Building collaborative cultural policies is challenging due to the parties’ differing attitudes, priorities, and resources. In this contribution, we have focused on the funds made available by the Framework Program of the European Commission dedicated to developing cultural and creative sectors. The analysis highlights the characteristics of a network of collaborations between partners of different cities. Our results show non-trivial features linked to the cultural-creative dimensions of cities. Findings unveil complex cultural-creative collaboration dynamics. Results show that effective collaborative policies require carefully balancing diverse attitudes. JEL classification numbers: L82, R58, Z11 Keywords: Creativity, cities network, enabling environment.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

The convergence of IoT and sustainability in global supply chains: Patterns, trends, and future directions

This study investigates the pivotal role of Internet of Things (IoT) applications in advancing sustainable supply chains through a comprehensive analysis of research trends, international collaborations, and thematic evolution. Utilizing data from the Web of Science database, the research employs an innovative methodological framework combining keyword network analysis, country collaboration networks, and thematic mapping. The study reveals seven distinct keyword communities, highlighting the dominance of enabling technologies and the emergence of sector-specific applications. Analysis of international collaborations identifies five country clusters, each with unique research foci and collaborative patterns. The thematic map and evolution analysis uncover critical research gaps, including underexplored areas such as security in IoT-enabled supply chains and the integration of smart city concepts. The study projects future research directions, emphasizing the convergence of artificial intelligence and IoT, and the need for more holistic approaches to sustainability in supply chain management. The findings underscore the potential of IoT to foster sustainable business models that balance economic, social, and environmental benefits. This research contributes to the growing body of knowledge on IoT applications in sustainable supply chains by providing a multifaceted analysis of the field’s structure, evolution, and future trajectory. The insights offered are valuable for researchers, policymakers, and practitioners seeking to leverage IoT for creating more efficient, resilient, and sustainable supply chains in an increasingly globalized and environmentally conscious market.

Scoping review of epigenetics on neurodegenerative diseases: research frontiers and publication status.

Epigenetics has significantly evolved and emerged as important players in the pathogenesis of neurodegenerative diseases. However, a scientometric synthesis of such changes over time is currently lacking. We conducted a comprehensive search of the Web of Science Core Collection from inception until November 5, 2022, using appropriate keywords. Our primary objective was to employ scientometric analysis to depict changes in keywords over time and to assess the structure and credibility of clusters. Additionally, we examined the network of research (countries, institutions, and authors) using CiteSpace and VOSviewer. We identified 25 clusters with well-structured networks (Q = 0.82) and highly credible clustering (S = 0.91) from 16,181 articles published between 1999 and 2022. Our findings are as follows: (a) the literature and research interest concerning the epigenetics of neurodegenerative diseases are continuously growing; (b) the three most productive countries are the USA, China, and Germany; (c) international collaborative relationships exist, alongside small, isolated collaboration networks of individual institutions. The number and impact of global publications on the epigenetics of neurodegenerative diseases have expanded rapidly over the past 20 years. This review provides valuable guidelines for researchers interested in neurodegenerative diseases research.

Thermoelectric Materials: A Scientometric Analysis of Recent Advancements and Future Research Directions

This scientometric study looks at the current trend in thermoelectric materials research and explores the evolving domain of thermoelectric materials research using a combination of bibliometric and scientometric methodologies. The analysis examines global research trends from a dataset of over 37,739 research articles, focusing on thematic evolution, annual growth rates, and significant contributions. Six principal research clusters were identified, encompassing energy conversion, material synthesis and nanostructures (the most prominent cluster), computational modeling and material properties, measurement and characterization, material performance enhancement, and material processing and microstructure. Each cluster highlights a critical aspect of the field, reflecting its broad scope and depth. The key findings reveal a marked annual increase in research output, highlighting the growing global importance of thermoelectric materials in sustainable energy solutions. This is especially evident in the significant contributions from China and the USA, emphasizing their leadership in the field. The study also highlights the collaborative nature of thermoelectric research, showing the impact of global partnerships and the synergistic effects of international collaboration in advancing the field. Overall, this analysis provides a comprehensive overview of the thermoelectric materials research landscape over the past decade, offering insights into trends, geographic contributions, collaborative networks, and research growth. The findings underscore thermoelectric materials’ vital role in addressing global energy challenges, highlighting recent advancements and industrial applications for energy efficiency and sustainability.

Artificial Intelligence Integration and Social Innovation: Interdisciplinary Research Trends Aligned with the Sustainable Development Goals

This study investigates the integration of Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Prompt Engineering into the social sciences and their impact on collaborative networks, thematic developments, and research trends aligned with the Sustainable Development Goals (SDGs). Utilizing bibliometric analysis and topic modeling, the research analyzes a dataset of 389 publications from the Web of Science (WoS) database, spanning the last decade. The findings highlight significant growth in interdisciplinary research at the intersection of these technologies and social sciences, with notable contributions in management, business, and environmental studies. Key themes identified include AI-driven innovation in product development, energy sector advancements, and the role of AI in educational and healthcare settings. The study underscores the transformative potential of AI in driving sustainable development, while also emphasizing the importance of addressing ethical considerations and ensuring responsible implementation. This research contributes to a deeper understanding of how AI and related technologies are reshaping the social sciences and their role in achieving global sustainability goals.

Alternative Pharmaceutical Innovation Models in Competitive Markets: A Collaborative Approach to Develop a Novel Drug for Hepatitis C.

Alternative innovation models have emerged to address failures of the traditional pharmaceutical system, particularly for diseases where market incentives do not attract sufficient research and development efforts. However, the feasibility of such models for diseases with significant markets is not well-established. This article analyses the development of a novel drug (ravidasvir) for the treatment of hepatitis C, a highly profitable market. Data from qualitative research methods, including literature reviews and semi-structured interviews, was analyzed using a novel conceptual framework focusing on actors, resources, organizational practices, and outcomes. Dissimilar to other projects, ravidasvir did not involve any major pharmaceutical companies. Rather, it leveraged the capacities of actors less traditionally involved in the development of novel medicines by constructing a collaborative network of private and public partners from low- and middle-income countries with a shared goal. The collaboration was successful in developing a highly effective, easy-to-use, and affordable medicine and contributed significantly to capacity-strengthening. However, the case also highlighted that strategic behavior by competing for-profit firms could pose significant challenges and that changing external conditions reduced the potential public health impact of the drug. Lessons from ravidasvir can inform future efforts to develop alternative innovation models for therapeutic areas with significant commercial interest.

Worldwide research trends in Crohn's disease treatment over the past 2decades: a bibliometric analysis.

The treatment of Crohn's disease (CD) has received widespread attention in clinical practice, but there is currently a lack of quantitative evaluation of the literature published in this field. This study aimed to describe the development trends and research hotspots of CD treatment through bibliometric analysis. Publications related to CD treatment published from 2004 to 2023 were searched in the WoSCC. Microsoft Office Excel 2021 was used for the analysis and visualization of the annual number of publications. CiteSpace was used to visualize the collaboration networks of authors, institutions, and countries, as well as to construct a reference timeline visualization map and identify keywords with the strongest citation bursts. The bibliometric analysis included 25,608 publications between 2004 and 2023. The most productive year was 2021. The United States of America (n = 7,891) and the University of California System (n = 939) are the country and institution with the most published papers, respectively. Among the 97,564 authors, Peyrin-Biroulet, Laurent (n = 424) published the most articles. The core journals were Inflammatory Bowel Diseases, Journal of Crohns and Colitis, Alimentary Pharmacology and Therapeutics, etc. The timeline view showed that "#5 JAK Inhibitor" was the most recent topic. The keywords that burst and persist from 2020 to 2023 include "ustekinumab" and "vedolizumab". An increasing number of researchers are dedicating their efforts to exploring the treatment of CD, with the United States making the largest contribution to this field. Currently, the research hotspots predominantly involve drug therapy including ustekinumab, vedolizumab, and JAK inhibitors. Our study provides valuable information for scholars studying CD treatment.

A Comparative Bibliometric Analysis of Ethical Leadership Research: Insights From Scopus and Web of Science (2010-2024)

This research provides a bibliometric analysis of articles published between 2010 and 2024 on "Ethical Leadership" using Scopus and Web of Science databases. The analysis aims to see the status of the ethical leadership. Ethical leadership represents a crucial aspect of effective management, and it has been the subject of increasing interest from researchers. The investigation involved an examination of both Scopus and Web of Science. These databases are widely recognized as prominent sources of academic research. The comparative analysis is designed to provide detailed insight into the citation patterns, and collaborative networks associated with ethical leadership research. The search scope was narrowed by using the term "ethical leadership" as a keyword in both databases. Furthermore, other disciplines were excluded by entering "education" into the search filter. The term "Ethical Leadership" was entered into the search field. As a result, 122 articles obtained from the Web of Science database and 133 articles from Scopus. Different results were obtained from the data analyzed.

A Bibliometric Analysis of 100 Top-cited Articles on Cleft Orthodontics.

This bibliometric study seeks to provide a comprehensive overview of the 100 most frequently cited articles in the domain of cleft orthodontics. The analysis will reveal key influential publications, collaborative author networks, and identify prevailing research themes within the field. The studies related to Orthodontics in the realm of cleft lip and palate (CLP) were retrieved from the Scopus database on 30th August 2024 using key terms. The results obtained were sorted in descending order of citations and the 100 top-cited articles were hand-filtered. RStudio software version 4.2.0 and Bibliometrix R-package was used for performing scientometrics involving co-citation, co-occurrence, collaboration and co-word analyses, bibliographic coupling and network mapping. A total of n = 3984 articles were retrieved from which top-100 cited articles were filtered. These documents were published during 1950-2019 with peak production in 1997. The United States and the Netherlands were the most prolific countries involved in the given research. The majority of the highly referenced articles pertained to alveolar bone grafting, and treatment outcomes being the second common focus followed by Infant and early orthopedics (Naso-alveolar moulding, Maxillary Protraction) and facial growth during the given period. Thematic mapping depicted bone grafting, alveoloplasty (infant orthopedics) and maxillofacial development as the more developed core topics than the psychology and self-perception of patients with CLP. Recent research trends have shifted towards three-dimensional assessment methods.

Predictors of Mortality in Ruptured Abdominal Aortic Aneurysms.

Introduction The outcome of a ruptured abdominal aortic aneurysm (AAA) without any interventions is close to uniformly fatal. The Society for Vascular Surgery suggests a door-to-intervention time of less than 90 minutes in a patient with a ruptured AAA. Admission factorsassociated with poor outcomes in ruptured AAAs include hypotension, renal insufficiency, severe anemia, advanced age, and cardiac arrest. Patients who are particularly at high risk for open AAA repair may be candidates for endovascular repair, which may decrease mortality. This study aimed to assess the relationship between systolic blood pressure (SBP) and serum bicarbonate levels in predicting mortality in patients with ruptured AAAs. Methods This retrospective study was performed using the United States Collaborative Network of 57 academic medical centers/healthcare organizations in the TriNetX database. A total of 4,226 patients with ruptured AAAs were identified. Patients were categorized based on SBP of ≤90 mmHg, any SBP, or >90 mmHgand further stratified by bicarbonate levels. Rounded cutoffs of the bicarbonate ranges (<10, 10.01-15; 15.01-20, >20.01) were chosen for interpretative purposes. Mortality outcome was assessed within 90 days after presentation for the ruptured AAA. Results After exclusions, 4,174 patients presented with ruptured AAA between September 30, 2003, and September 30, 2023, in the database. Overall, 90-day mortality in any SPB cohort was 28%. Patients who presented with a ruptured AAA with an SBP ≤ 90 had a 46.3% mortality. Those who presented with a SBP > 90 had a 20.1% mortality. Additionally, as bicarbonate levels decreased, mortality increased within each SBP group. Conclusions Early recognition and intervention are critical for survival in patients with ruptured AAAs. Metabolic acidosis is an important marker of the severity of hemorrhage in these patients. In this large cohort study of ruptured AAAs, mortality increases significantly with hypotension and metabolic acidosis, represented by lower bicarbonate levels. Abnormalities in the serum bicarbonate may be seen before severe changes invital signs in hemorrhaging patients. Early recognition of metabolic acidosis may lead to earlier life-saving interventions in patients with ruptured AAAs.

Modeling inter-organizational business process governance in the age of collaborative networks

Collaborative networks require inter-organizational business process governance (IO-BPG) mechanisms to define ownership over shared resources and activities, accountability over operations, inter-organizational roles and responsibilities, and strategic partner alignment. We developed an IO-BPG modeling approach aiming to incorporate (1) IT governance activities (e.g., IT performance measurement), (2) data governance activities (e.g., data strategy management), and (3) “shadow” parallel governance-related operations. Resulting from a design science research project, our contributions include the building blocks (domain attributes, ontology, and requirements) of a novel BPMN extension, its demonstration in logistics operations, its evaluation, and design principles to guide IO-BPG modeling. Suggestions for the development and evaluation of future BPMN extensions are also highlighted based on the lessons learned in this project. For practitioners, our contribution can improve accountability reports over data assets and operations, identify dataset ownership, assist in the coordination of governance activities in networked businesses, and comply with regulations and strategic partnership agreements.

ATD-300 an AI empowered miniature acoustic device for automatic gunshot attacks detection and image capture

Built on over 30 years of acoustic source recognition expertise in transient impulsives signature, recently highly empowered by neural networks, and wave propagation model driven algorithms, ACOEM has developed the ATD-300, an acoustic sensor for automatic gunshot detection, identification and localization. What sets this sensor apart from the competition is its reliability in terms of detection rate over false alarm and robust high resolution shooter pointing capability. Therefore it can be used directly by police departments without third-party human validation. Resulting from the design effort to distribute intelligence among a collaborative sensor network, processing and artificial intelligence algorithms are fully embedded locally, enabling an alert to be raised in less than three seconds and a camera to be pointed at the shooter with only one sensor. In addition, as the system is already installed in several American cities, we will show how a device-to-server distributed artificial intelligence approach continuously improves the performance of the solution, fed by real field data.

Measuring the Collective Community Capacity of a Network to Address Health Inequities during a Public Health Emergency: Findings from the National COVID-19 Resiliency Network.

This study assesses the collective community capacity of the National COVID-19 Resiliency Network (NCRN), a multisectoral network mitigating the disproportionate impact of COVID-19 on minoritized populations. From January to April 2022, we used two concurrent data collection methods: a Collective Community Capacity (C3) survey (n=65) and key informant interviews (KIIs) (n=26). The C3 assessed capacity for creation of a shared vision, engagement in community change, and distributive leadership. KIIs assessed perspectives on network formation and implementation. We used a convergent design and triangulation for interpretation. NCRN has growing collective community capacity. The C3 survey found high capacity for establishing a shared mission and evidence of mutual commitment, trust, and accountability. About three-quarters of respondents strongly agreed that partners addressed social, economic, and cultural barriers related to COVID-19. Interviewees valued NCRN leaders' openness, availability, and willingness to listen. Partners learned from one another, increased their health communication capacity, and supported sustainability. They sought greater opportunities to partner and support decision-making. NCRN developed a collaborative network with a shared vision of improving health equity during and beyond the COVID-19 pandemic, while identifying areas for improvement in distributive leadership. Findings can support other organizations seeking to build collective community capacity to address equity in public health emergencies.

Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis

Background Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. Methods A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. Results The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including ‘human’ and ‘dental caries,’ indicated research hotspots. Conclusion We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.

Analyzing hockey publications through scientometric and altmetric perspectives

Purpose This study aims to present scientometric mapping and altmetric analysis of publications related to “Hockey” in the past three decades. By using the advanced analytical techniques of mapping coupled with altmetric analysis, this paper aims to reveal the complex network of collaborations, the dispersion of expertise worldwide and prevailing thematic trends in the field of hockey. Design/methodology/approach The data was extracted from the Web of Science (WoS) database and Altmetric Explorer for articles related to hockey over the past three decades. VOSviewer was used to conduct network analysis whereas MS-Excel was used for altmetric data analysis. The study focused on the articles retrieved using the key term “Hockey” in English language publications. The altmetric attention scores (AAS) were used to measure the level of online attention on different platforms, complementing traditional bibliometric analysis. Findings The study reveals a notable increase in the productivity of hockey research over the past 30 years, with a specific focus on major surges in publication output and altmetric attention in recent times. Coauthorship and country-wise mapping analysis highlight global research collaboration trends, while keyword analysis underscores thematic concentrations. Key journals such as British Journal of Sports Medicine and American Journal of Sports Medicine emerge as crucial dissemination platforms. The importance of X posts (Formerly Twitter) and Mendeley in the diffusion of hockey literature is highlighted by altmetric research. Originality/value The study provides a concise overview of research conducted on the game of hockey. This research will be advantageous for researchers and individuals involved in the hockey community, as it offers bibliographic insights and aids in identifying suitable media for disseminating their findings.

Assessing Africa’s position in the development of AI-enabled ECG devices

Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Exploring the role of artificial intelligence technology in empowering women-led startups

The study aims to investigate how artificial intelligence (AI) influences women-led startups in Saudi Arabia, aiming to understand their unique experiences, challenges, and opportunities within the AI technology landscape. This study used a qualitative method, conducting 16 in-depth interviews with women entrepreneurs operating businesses in Saudi Arabia. The analysis was performed using thematic analysis with NVivo 12, uncovering key themes and insights. The findings reveal that cultural norms, societal expectations, limited awareness, and financial constraints are directly associated with women’s involvement in AI-driven businesses. Cultural biases emerged as impediments, underscoring the need for targeted interventions such as awareness campaigns and educational initiatives to dismantle ingrained biases and foster an environment that recognizes and celebrates the contributions of women in the tech and AI sectors. Educational programs, collaborations between academia and industry, and mentorship initiatives were identified as pivotal components to prepare women entrepreneurs to navigate the intricate landscape of AI adoption. Financial inclusion emerged as a critical pillar, advocating for equitable access to funding and resources tailored specifically for women-led AI startups. The study further emphasizes the importance of fostering supportive ecosystems that extend beyond financial aid. Creating networks for mentorship, guidance, and collaboration provides women entrepreneurs with platforms to share experiences and resources, enhancing resilience and the potential for success in the AI landscape. Acknowledgment The authors extend their appreciation to the Arab Open University for funding this work through research fund No. AOUKSA-524008.

Postoperative Complications in Lingual Versus Palatine Tonsillectomies.

The aim of this study was to investigate the risks of lingual tonsillectomy (LT) in a large cohort and compare these risks to those of palatine tonsillectomy (PT). A retrospective cohort study was conducted using data from the United States collaborative network within TriNetX. The LT group was defined using Current Procedural Terminology (CPT) code 42870 and PT group using CPT codes 42820, 42821, 42825, or 42826. Groups were further subdivided into pediatric and adult populations and matched based on propensity scores within the cohorts. Complications occurring within 14 days of procedure were compared within each cohort. There were 1,357 adult patients (mean age, 42.9 years) and 863 pediatric patients (mean age, 8.1 years). Adults who had LT were more likely to experience postoperative dysphagia (OR = 2.6, p < 0.001) and require admission to the hospital (OR = 4.3, p < 0.001) or intensive care unit (OR = 6.1, p < 0.001). There was no significant difference in bleeding between adult PT and LT groups, occurring at rates of 3.8% and 4.4%, respectively (p = 0.50). Pediatric patients who had LT were also more likely to experience postoperative dysphagia (OR = 2.4, p = 0.017) and require admission to the hospital (OR = 8.2, p < 0.001) or intensive care unit (OR = 2.7, p = 0.012). The postoperative bleed rate was 3.2% in the pediatric PT cohort, which was 2.4 times higher compared to those who underwent LT (1.5%, p = 0.016). Postoperative complications after lingual tonsillectomy are more common than after palatine tonsillectomy in both adults and children. III Laryngoscope, 2024.

Research on the collaborative relationship of task-driven urban earthquake emergency organizations

Earthquake emergency response is a complex process, and a large number of tasks are involved in the process of urban earthquake emergency response. To further study the collaborative relationship between organisations in the city-level emergency collaboration network driven by different emergency tasks, this study first constructs a task–organisation relationship framework and explores the collaborative mechanism of organisations driven by tasks. Then, the co-expression analysis was used to analyse the degree of correlation between task-driven emergency organisations. Finally, considering the influence of tasks on the degree of inter-organisational collaboration, this study constructs a task-driven weighted directed emergency organisation collaboration network. Social network analysis was used to reveal the structural attributes of the task-driven organisational collaboration network, the collaborative characteristics among organisations in the network, and the structural positions of organisations in the collaborative network. It is found that the task-driven earthquake emergency collaboration network was relatively loose, and there were small groups with close collaboration in the network. Most organisations in these small groups are in the same task module, and their tasks are similar. Commanders in the task execution process are usually at the core of the network, and these organisations are usually the main coordinators of the overall emergency collaboration and task module networks. These findings can improve our understanding of earthquake emergency organisational collaboration relationships and inspire the optimisation of urban earthquake emergency management.