Factors influencing the research impact in cancer research: a collaboration and knowledge network analysis.

BACKGROUND: Cancer is a major public health challenge globally. However, little is known about the evolution patterns of cancer research communities and the influencing factors of their research capacity and impact, which is affected not only by the social networks established through research collaboration but also by the knowledge networks in which the research projects are embedded. METHODS: The focus of this study was narrowed to a specific topic - 'synthetic lethality' - in cancer research. This field has seen vibrant growth and multidisciplinary collaboration in the past decade. Multi-level collaboration and knowledge networks were established and analysed on the basis of bibliometric data from 'synthetic lethality'-related cancer research papers. Negative binomial regression analysis was further applied to explore how node attributes within these networks, along with other potential factors, affected paper citations, which are widely accepted as proxies for assessing research capacity and impact. RESULTS: Our study revealed that the synthetic lethality-based cancer research field is characterized by a knowledge network with high integration, alongside a collaboration network exhibiting some clustering. We found significant correlations between certain factors and citation counts. Specifically, a leading status within the nation-level international collaboration network and industry involvement were both found to be significantly related to higher citations. In the individual-level collaboration networks, lead authors' degree centrality has an inverted U-shaped relationship with citations, while their structural holes exhibit a positive and significant effect. Within the knowledge network, however, only measures of structural holes have a positive and significant effect on the number of citations. CONCLUSIONS: To enhance cancer research capacity and impact, non-leading countries should take measures to enhance their international collaboration status. For early career researchers, increasing the number of collaborators seems to be more effective. University-industry cooperation should also be encouraged, enhancing the integration of human resources, technology, funding, research platforms and medical resources. Insights gained through this study also provide recommendations to researchers or administrators in designing future research directions from a knowledge network perspective. Focusing on unique issues especially interdisciplinary fields will improve output and influence their research work.

The Application of MicroRNAs in Glaucoma Research: A Bibliometric and Visualized Analysis.

Glaucoma is similar to a neurodegenerative disorder and leads to global irreversible loss of vision. Despite extensive research, the pathophysiological mechanisms of glaucoma remain unclear, and no complete cure has yet been identified for glaucoma. Recent studies have shown that microRNAs can serve as diagnostic biomarkers or therapeutic targets for glaucoma; however, there are few bibliometric studies that focus on using microRNAs in glaucoma research. Here, we have adopted a bibliometric analysis in the field of microRNAs in glaucoma research to manifest the current tendencies and research hotspots and to present a visual map of the past and emerging tendencies in this field. In this study, we retrieved publications in the Web of Science database that centered on this field between 2007 and 2022. Next, we used VOSviewer, CiteSpace, Scimago Graphica, and Microsoft Excel to present visual representations of a co-occurrence analysis, co-citation analysis, tendencies, hotspots, and the contributions of authors, institutions, journals, and countries/regions. The United States was the main contributor. Investigative Ophthalmology and Visual Science has published the most articles in this field. Over the past 15 years, there has been exponential growth in the number of publications and citations in this field across various countries, organizations, and authors. Thus, this study illustrates the current trends, hotspots, and emerging frontiers and provides new insight and guidance for searching for new diagnostic biomarkers and clinical trials for glaucoma in the future. Furthermore, international collaborations can also be used to broaden and deepen the field of microRNAs in glaucoma research.

How Do Heterogeneous Networks Affect a Firm's Innovation Performance? A Research Analysis Based on Clustering and Classification.

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry-University-Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.

Elements, characteristics, and performances of inter-enterprise knowledge recombination: Empirical research on green innovation adoption in China's heavily polluting industry.

Knowledge recombination has become a hot topic in green innovation adoption (GIA) research in recent years. However, previous studies have mainly discussed the knowledge recombination within a single enterprise but ignored inter-enterprise knowledge recombination (Inter-KR). In this paper, a theoretical framework of Inter-KR is developed, and the three features of Inter-KR are discussed: elements, characteristics, and performances. Multisource heterogeneous data combined with Natural language process, network analysis and econometric regression are used to analyze the above three features. Social network theory is introduced to elaborate on the influence mechanism of Inter-KR. The empirical results indicate that enterprises can effectively improve their environmental performance by participating in Inter-KR: 1) the participation of Inter-KR is significantly influenced by time and national policy; 2) further refinement and decomposition of the characteristics of inter-KR find that the comprehensiveness has a significant promoting effect on GIA. When issuing relevant policies, the government should consider the balanced development of environmental governance and enterprise innovation, and improve the GIA performance by enabling enterprises to acquire an in-depth and extensive knowledge base. In summary, this paper provides new insights for research on knowledge recombination, the literature on collaboration innovation, and green innovation adoption research.

An overview of patients with haemophilia A in China: Epidemiology, disease severity and treatment strategies.

INTRODUCTION: Haemophilia A (HA) is a rare X chromosome-linked bleeding disorder resulting in missing or defective clotting factor VIII (FVIII) and causes large disease burden. AIM: As a member of World Federation of Hemophilia, China seeks to understand the current epidemiology, disease profile and treatment landscape of patients with HA through the Hemophilia Treatment Center Collaboration Network of China (HTCCNC). METHODS: The HTCCNC enabled data collection on patients with HA from 166 member hospitals (2007-2019) across China. The distribution of patients across 31 divisions was summarized using a heat map. Patient demographics, disease severity and clinical and treatment information were summarized using descriptive statistics. RESULTS: HTCCNC identified 17,779 patients with HA during 2007-2019. Patients were predominantly male (99.99%), and 28.3% had a known family history of haemophilia. Among patients with lab-measured disease severity (N = 13,116), 6,519 had severe HA (49.7%), 4,788 had moderate HA (36.5%), and 1,809 had mild HA (13.8%). Among patients with information on the delays, delays in diagnosis and in treatment initiation were observed in 1,437 (28.8%) and 1,750 (39.2%) patients, respectively. On average, those patients had an 8.4 years gap between the first bleed and HA diagnosis and a delay of 8.6 years from the first bleed to treatment initiation. Additionally, 44.33% of patients relied solely on episodic treatments, and 16.2% received any prophylaxis treatments. CONCLUSIONS: Using data from the largest haemophilia registry in China, this study indicated that delayed diagnosis and treatment, together with low utilization of prophylaxis, are key challenges for patients with HA.

Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis.

BACKGROUND: Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited. RESULTS: We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development. CONCLUSION: We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks.

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.

COVID-19 research progress: Bibliometrics and visualization analysis.

Background: Coronavirus primarily targets the human respiratory system, COVID-19 (Coronavirus disease 2019) triggered in China in the late 2019. In March 2020, WHO announced the COVID-19 pandemic. This study aims to analyze and visualize the scientific structure of the COVID-19 publications using co-citation and co-authorship. Methods: This is a scientometric study. Web of Science Core Collection (WoSCC) was searched for all documents regarding COVID-19, MERS-Cov, and SARS-Cov from the beginning to 2020. An Excel spreadsheet was applied to gather and analyze the data and the CiteSpace was used to visualize and analyze the data. Results: A total of 5159 records were retrieved in WoSCC. The structure of the network indicated that the network mean silhouette was low (0.1444), implying that the network clusters' identity is not identifiable with high confidence. The network modularity was 0.7309. The cluster analysis of the co-citation network on documents from 2003 to 2020 provided 188 clusters. The largest cluster entitled, "the Middle East respiratory syndrome coronavirus" had 255 nodes. The coauthorship network illustrated that the most prolific countries, USA, China, and Saudi Arabia, have focused on a specific field and have formed separate clusters. Conclusion: The present study identified the important topics of research in the field of COVID-19 based on co-citation networks as well as the analysis of clusters of countries' collaborations. Despite the similarities in the production behavior in prolific countries, their thematic focus varies so that a country like China plays a role in "Quantitative Detection" cluster, while USA is the leading country in the "Biological Evaluation" cluster.

International Scientific Collaboration on Pituitary Research: A Social Network Analysis.

Background: Social network analysis (SNA) evaluates the connections and behavior of individuals in social groups. The scientific collaboration network is a kind of SNAs. A social network could be defined as a collection of nodes (social existence) and links (connections) associated with the nodes. The aim of this study was to evaluate the scientific outputs and collaboration networks of the countries and authors using indicators of SNA in the field of pituitary disorders between 2000 and 2020. Methods: This is a practical study performed by applying a scientometric approach and SNA. We retrieved 31257 papers in the field of pituitary disorders between 2000 and 2020. Data were analyzed using scientific software, namely, VOSviewer, UciNet, and Netdarw. Results: Based on degree centrality, Colao and Pivonello in the world, Shimon and Kadioghlu in the Middle-East (ME), and Khamseh, Ghorbani in Iran achieved the top ranking. Based on the betweenness centrality, Pivonello, Colao, and Chanson in the world, Laws, and Kadioghlu in the Middle-East, and Larijani, Mohseni, and Khamseh in Iran were known as the top authors. According to closeness centrality, Pivonello, Colao, and Chanson in the world, Kadioghlu and Kelestimur in the Middle-East, and Mohseni, Khamseh, and Larijani in Iran were the top authors. The map of the authors' collaboration in the field of pituitary disorders consists of 92 nodes. A total number of 77313 authors had global collaboration. The global collaboration network was comprised of 129 nodes (country) and 2694 links (country's collaboration). The Middle-East collaboration network revealed 69 nodes and 1708 links. The collaboration network of the Middle-East countries consists of 13 nodes and 50 links. Conclusion: Authors with a higher degree, betweenness and closeness centrality have greater efficiency (the number of articles) and effectiveness (the number of received citations). Moreover, the authors and countries that published more scientific products received more citations. In addition, in the Middle-East countries, the interdisciplinary scientific collaboration between the researchers in the fields of endocrinology, neurosurgery, pathology, and radiology has a significant impact on improving scientific outputs.

Social network analysis of biomedical research collaboration networks in a CTSA institution.

BACKGROUND: The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing "influential" nodes and identifying potential new collaborations that are most rewarding. METHODS: We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations. RESULTS: By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github. CONCLUSIONS: Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community.

Frontiers and hotspots of high-intensity interval exercise in children and adolescents: text mining and knowledge domain visualization.

Background: During the past two decades, research on high-intensity interval exercise (HIIE) in children and adolescents has steadily accumulated, especially on the subthemes of improving cardiometabolic and cardiovascular health. However, there is still little scientific understanding of using scientometric analysis to establish knowledge maps. Exploring the relationship between known and new emerging ideas and their potential value has theoretical and practical implications in the context of a researcher's limited ability to read, analyze, and synthesize all published works. Objective: First, this study aims to provide extensive information on HIIE research in children and adolescents, including authors, institutions, countries, journals, and references. Second, the objective is to use co-occurrence, burst, and co-citation analyses based on hybrid node types to reveal hotspots and forecast frontiers for HIIE research in children and adolescents. Methods: Using the bibliographic data of the Web of Science Core Collection (WoSCC) as the data source, publications, authors, and journals were analyzed with the help of bibliometric methods and visualization tools such as CiteSpace, VOSviewer, Pajek, and Bibliometrix R package. Authorial, institutional, and national collaboration networks were plotted, along with research hotspots and research frontiers based on keyword bursts and document co-citations. Results: This study found that executive function, high-intensity interval training, heart rate variability, and insulin resistance are emerging research topics; high-intensity training, mental health, exercise intensity, and cardiometabolic risk factors are continual frontier research areas in the subthemes. Conclusion: Our study has three novel contributions. First, it explicitly and directly reflects the research history and current situation of the HIIE intervention strategy in children and adolescents. This approach makes it clear and easy to trace the origin and development of this strategy in specific groups of children and adolescents. Second, it analyzes the research hotspots of HIIE in the field and predicts the research frontiers and development trends, which will help researchers get a deeper understanding of HIIE and pediatric health research. Third, the findings will enable researchers to pinpoint the most influential scholars, institutions, journals, and references in the field, increasing the possibility of future collaborations between authors, institutions, and countries.

Critical Knowledge Gaps and Future Priorities Regarding the Intestinal Barrier Damage After Traumatic Brain Injury.

The analysis aims to provide a comprehensive understanding of the current landscape of research on the Intestinal barrier damage after traumatic brain injury (TBI), elucidate specific mechanisms, and address knowledge gaps to help guide the development of targeted therapeutic interventions and improve outcomes for individuals with TBI. A total of 2756 relevant publications by 13,778 authors affiliated within 3198 institutions in 79 countries were retrieved from the Web of Science. These publications have been indexed by 1139 journals and cited 158, 525 references. The most productive author in this field was Sikiric P, and the University of Pittsburgh was identified as the most influential institution. The United States was found to be the leading country in terms of article output and held a dominant position in this field. The International Journal of Molecular Sciences was identified as a major source of publications in this area. In terms of collaboration, the cooperation between the United States and China was found to be the most extensive among countries, institutions, and authors, indicating a high level of influence in this field. Keyword co-occurrence network analysis revealed several hotspots in this field, including the microbiome-gut-brain axis, endoplasmic reticulum stress, cellular autophagy, ischemia-reperfusion, tight junctions, and intestinal permeability. The analysis of keyword citation bursts suggested that ecological imbalance and gut microbiota may be the forefront of future research. The findings of this study can serve as a reference and guiding perspective for future research in this field.

Mapping and modeling of physician collaboration network.

Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to the rest 2 nodes), alternative k-star (subset of k nodes in which 1 node is connected to each of other k - 1 nodes), and alternative k - 2 path (subset of k nodes in which, between a specific pair of nodes, there exists k - 2 paths of length 2) parameters of ERG have impact on the hospital readmission rate. Our findings can have implications for healthcare administrators or managers who could potentially improve the practice cultures in their organizations by following these outcomes.

Carbon neutrality: a comprehensive bibliometric analysis.

In recent years, excessive emissions of carbon dioxide have further intensified global warming, which poses a great threat to human society. The best way to reverse this situation is to take action to equalize the "carbon emissions" with the "carbon absorption," i.e., carbon neutrality (CN). To better understand the evolution and display a broad panorama of CN research, this paper provides a comprehensive bibliometric analysis of publications in the field of CN from 2006 to 2021. We use the Web of Science Core Collection as the data source, and a total of 633 publications have been retrieved. In the paper, we measure the productivity and influence of publications through recognized bibliometric metrics, and visually analyze publications using VOS Viewer and CiteSpace. The analysis results show that China is the most productive country/region. The study illustrates that in order to reach the goal of CN, it is necessary to reduce carbon emissions such as full using renewable energy. Simultaneously, it is also essential to enhance the "negative emissions" of carbon such as taking advantage of carbon sink and carbon capture, utilization, and storage. This paper provides some references for scholars who are interested or research in this field.

Multidimensional proximities and interorganizational coinnovation performance: The roles of intraorganizational collaboration network inefficiency.

In a gradually more interlinked world, the formation of collaborations with partners is increasingly regarded as an important driver for generating innovation. Although multidimensional proximities are important factors influencing interorganizational coinnovation performance, relevant empirical studies have not reached consistent conclusions. By focusing on organizational dyad and including intraorganizational collaboration network inefficiency as a moderating variable, we explore the effects of multidimensional proximities on interorganizational coinnovation performance. By reference to 5G patent data collected in China between 2011 and 2020, the research results based on the quadratic assignment procedure (QAP) model show that geographical proximity, cognitive proximity, and institutional proximity all improve interorganizational coinnovation performance. In addition, the inefficiency of intraorganizational collaboration networks decreases the positive effect of geographical proximity but increases the positive effects of cognitive and institutional proximity in this context. These findings have both theoretical and practical implications for organizational partner selection.

In-depth bibliometric analysis and current scientific mapping research in the context of rheumatoid arthritis pharmacotherapy.

Rheumatoid arthritis (RA) is a chronic multifactorial disorder of autoimmune etiology with a complex pathophysiological mechanism that is not yet fully elucidated. RA pharmacotherapy includes active molecules of chemical or biological nature that offer symptomatic relief and a slowing of progression, but still in a context of incurability. Therefore, the development of continuous research and multidisciplinary collaboration is essential. Although the management of RA is a topic of significant scientific relevance, existing bibliometric analyses are insufficient to assess this vast field. Consequently, the present study examines numerous manuscripts indexed in the Web of Science database using the VOSviewer software to provide through statistical interpretation of the data a comprehensive description of RA pharmacotherapy in terms of scientific impact, current state of research, number and frequency of citations, most prolific journals, authors, and countries, along with their relationships and other useful data for the literature search/publication process. Furthermore, the use of bubble maps of term occurrence has applicability in identifying current research trends in the field of RA pharmacotherapy as well as their evolution over the years. The leader in this field in terms of published papers is the United States, and the most prolific journal is Annals of the rheumatic diseases. The global management of RA, which is characterized by extensiveness and depth due to many variables, suggests the need for a conceptual framework based on pharmacotherapy coupled with comprehensive bibliometric studies, and the results may be useful for researchers in setting specific objectives that contribute to improving RA outcomes.

The Role of Social Media on the Research Productivity of Neurosurgeons During the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic committees of all countries restricted face-to-face interactions. This study aimed to determine how the pandemic changed the research output for many neurosurgeons while highlighting how social media may have been used as a contactless platform to maintain research productivity during these times. METHODS: A cross-sectional, descriptive, 24-item, and non-randomized online survey was applied worldwide, and shared using social media platforms and emails. The questions mainly focused on comparing the results of the pre-pandemic period to the pandemic period (after March 2020). RESULTS: A total of 202 respondents from 60 different countries responded to the survey. Interest in neurosurgical education increased from 24% to 76%, while the topic of epidemiology gained interest from 28% to 72% when the pre-pandemic era was compared to the pandemic era. Preference for prospective studies decreased from 66% to 34%, while interest in retrospective studies increased from 39% to 61%. In evaluating publication types, the preference for reviews increased from 36% to 64%. Sixty-two percent of the respondents stated they had concerns over delays in individual contributions/lack of accountability. These concerns were followed by problems with theft of intellectual property/data and authorship disputes. Forty-one percent believed that the support of extra hands on a load-heavy project was the most powerful benefit of social media collaboration. Those who reported increased publications during the pandemic were also more likely to collaborate using social media (P = 0.030). CONCLUSIONS: During the pandemic, social media collaborations helped increase research output for neurosurgeons.

Multi-level network dataset of ten Swiss wetlands governance cases based on qualitative interviews and quantitative surveys.

The dataset of this paper originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

Contact Tracing Research: A Literature Review Based on Scientific Collaboration Network.

Contact tracing is a monitoring process including contact identification, listing, and follow-up, which is a key to slowing down pandemics of infectious diseases, such as COVID-19. In this study, we use the scientific collaboration network technique to explore the evolving history and scientific collaboration patterns of contact tracing. It is observed that the number of articles on the subject remained at a low level before 2020, probably because the practical significance of the contact tracing model was not widely accepted by the academic community. The COVID-19 pandemic has brought an unprecedented research boom to contact tracing, as evidenced by the explosion of the literature after 2020. Tuberculosis, HIV, and other sexually transmitted diseases were common types of diseases studied in contact tracing before 2020. In contrast, research on contact tracing regarding COVID-19 occupies a significantly large proportion after 2000. It is also found from the collaboration networks that academic teams in the field tend to conduct independent research, rather than cross-team collaboration, which is not conducive to knowledge dissemination and information flow.

Scientific Knowledge Graph and Trend Analysis of Central Sensitization: A Bibliometric Analysis.

BACKGROUND: Central sensitization refers to a state of hypersensitivity in the central nervous system and is associated with the development and maintenance of chronic pain. Central sensitization plays an essential role in various diseases. Nevertheless, there has been no bibliometric analysis before in this field. The purpose of this study was to provide critical themes and trends in the area of central sensitization, to build a network of knowledge, and to facilitate the future development of relevant basic and clinical research. METHODS: Publications on central sensitization were extracted from the Science Citation Index-Expanded. We used R software to systematically analyze the countries, institutions, authors, journals, references, and keywords of the publications. Besides, conceptual structure, intellectual structure, and social structure were constructed. RESULTS: A total of 4466 publications were included. Research in the field of central sensitization generally showed a steady upward trend. The three structural networks showed that the United States is the leading country in this field. Arendt-Nielsen L and Woolf CJ were the most productive and influential authors, respectively. "Pain" was the journal with the most studies. Most journals that published and cited articles about central sensitization were academically influential. Cluster analysis revealed that research in central sensitization contains three main conceptual clusters, and the themes of research evolve frequently. Current research focuses on the pathogenesis of central sensitization in neuropathic pain, the role of central sensitization in different diseases, and related clinical double-blind trials. CONCLUSION: Central sensitization received widespread attention. The United States led the way in academic activity. In this field, the current situation of cooperation and communication between different countries and institutions is positive. The present research hotspots were the pathogenesis of central sensitization in neuropathic pain, the role of central sensitization in different diseases, and related clinical double-blind trials.