ABSTRACT
COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Drawing on network portfolio literature and resource dependence theory, this study investigates how a nonprofit's N2B partnership portfolio configurations (i.e., size and industry diversification), reliance on individual donations, and reliance on government grants influence the nonprofit's transparency in disclosing N2B partnerships on Twitter. We manually coded the level of transparency reflected in 911 tweets sent by 81 leading COVID-19 NPOs mentioning 501 companies from March 1 to July 19, 2020. Social network analysis and regression models were performed to answer the research inquiries. Findings indicate that maintaining a large number of business connections is associated with lowered transparency in N2B communication on Twitter, whereas keeping diverse connections with different business industries relates to increased transparency in N2B communication. NPOs with a stronger reliance on government grants signaled more transparency in N2B parentships on Twitter, but the reliance on individual donations did not influence N2B transparency signaling. © 2023 Elsevier Inc.
ABSTRACT
COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The Covid-19 pandemic, as Henry Kissinger mentions, will not only "forever alter the world order,"but also potentially transform the ever-changing higher education world. The recent increase in technological innovations in information, communications, and computer technology has profoundly transformed traditional teaching-learning processes and peer-to-peer interactions for knowledge transfer. One such radical change in technology that researchers are continually working on is motivating collaborative learning and student interactions to improve their learning experiences. Collaborative Learning (CL), where students work in groups to achieve a specific learning objective, can facilitate a deep learning activity that promotes student participation. However, the potential of discussion forums is limited due to their unstructured nature in LMSs like Canvas.We propose and develop a structured discussion forum that can offer a platform to communicate and discuss problems and receive feedback, discuss solutions, and suggestions online. Students who participate in these discussion forums can benefit in multiple ways, including increased class preparedness and more active learning. The twofold objectives and outcomes include 1) analyzing discussion board data to reveal students' interaction and their degree of participation in the course, and 2) developing a toolset to draw useful inferences from such collaboration networks. Specifically, our schema-based model can help students visualize the discussion board networks creating an engaged learning environment. Furthermore, the model can help draw valuable inferences of the patterns of student interactions and assess student participation and belonging in the course with greater precision.This paper demonstrates a schema-based discussion board model that can allow researchers to collect better-formatted discussion data and more reliable information about the posts, such as the type of posts and the relationships of each post with others. The reimagined discussion boards include the ability to classify discussion posts using various parameters, visualize the posts' patterns of interactions, identify their relationships with other discussion posts, and precisely evaluate student participation in discussions to monitor the major topics of discussion. We believe that the result of increased participation in discussions with other students will have the effect of increasing students' sense of belonging to the community of scholars. © 2022 IEEE.
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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.
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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.
Subject(s)
COVID-19 , Sexually Transmitted Diseases , Tuberculosis , COVID-19/epidemiology , Contact Tracing/methods , Humans , PandemicsABSTRACT
Collaborative work leads to better organizational performance. However, a team leader’s view on collaboration does not always match reality. Due to the increased adoption of (online) collaboration systems in the wake of the COVID pandemic, more digital traces on collaboration are available for a wide variety of use cases. These traces allow for the discovery of accurate and objective insights into a team’s inner workings. Existing social network discovery algorithms however, are often not tailored to discover collaborations. These techniques often have a different view on collaboration by mostly focusing on handover of work, resource profile similarity, or establishing relationships between resources when they work on the same case or activities without any restrictions. Furthermore, only the frequency of appearance of patterns is typically used as a measure of interestingness, which limits the kind of insights one can discover. Therefore we propose an algorithm to discover collaborations from event data using a more realistic approach than basing collaboration on the sequence of resources that carry out activities for the same case. Furthermore, a new research path is explored by adopting the Recency-Frequency-Monetary (RFM) concept, which is used in the marketing research field to assess customer value, in this context to value both the resource and the collaboration on these three dimensions. Our approach and the benefits of adopting RFM to gain insights are empirically demonstrated on a use case of collaboratively developing a curriculum. © 2022, Springer Nature Switzerland AG.
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Compound disasters are highly complex and can involve different types of disasters. Since the beginning of the COVID-19 pandemic, compound disasters of public health emergencies, accident disasters, and natural hazards have occurred frequently all over the world; therefore, it is important to establish effective compound disaster emergency collaboration networks. Thus, this study examined the 7 March building collapse in Quanzhou City as a case study. This case was a typical compound disaster involving a public health emergency and an accident disaster during COVID-19. Based on the network analysis, the overall response and dynamic characteristics of the emergency collaboration for compound disasters were examined in this study. A compound disaster emergency collaboration network (ECN) was constructed by identifying the interactional relationships between emergency organizations. After applying time slices, the dynamic evolution of network structure, organizational-functional relations, organizational attributes, and cross-organizational relationships were discussed. The research results showed the following: (1) The density and connectivity of the compound disaster ECN first decreased before increasing. Meanwhile, the evolution of the network structure followed a path from decentralized to concentrated and from being uneven to an equilibrium. (2) The characteristics and practices of compound disasters during different periods indicated varied emergency needs for emergency organizations. We found that the formation of emergency tasks not only involved the passive adaptation to match the practice for compound disasters, but also the active choices of emergency organizations when facing compound disasters according to their collective experiences and decisions. (3) The national emergency management departments, the government emergency rescue organizations, and the local governments were the core organizations of the ECN. Public health management departments and social organizations were also required to participate in the ECN to improve the diverse and heterogeneous distribution of resources. (4) With increased demands during a compound disaster emergency, the number of cross-organizational collaborative relationships gradually increased. This study explored compound disaster emergencies from the perspective of network analysis to improve our understanding of the current and developing organizational relationships and practices during a compound disaster event. The dynamic characteristics of compound disasters require efficient adaptation and improvements of the collaborative mechanisms involved during emergencies.
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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.