Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of Science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures.
Coronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.
Thematic analysis based on a social network is one of the effective means in emergency management. To improve users’ understanding, the results of the thematic analysis are often displayed through visualization. However, the previous researches on text theme visualization rarely considered the unified representation of theme structure and theme evolution. Cognitive load leads to difficulty, and it happens due to the separate representation of structure and evolution relationship and it still increases because of the characteristics of urgency, uncertainty, etc. Therefore, a metaphor map is introduced in this study to overcome the limits of previous visualization tools in characterizing the structure and evolutionary relationship of emergency. On the one hand, different elements in the metaphor map represent the information of popularity and structure of the demand, respectively. On the other hand, the visual design based on the metaphor map strengthens the representation of the content and evolution states of the demand themes. At the theoretical level, a visualization method for emergency needs based on the metaphor map is proposed in this study, which enriches the theory of emergency information visualization. At the practical level, this study explores the design of a visualization system based on the metaphor map under crisis scenarios, which enhances the interaction between users and crisis information, and provides references for decision-making such as emergency material scheduling and emergency resource coordination.
There has been considerable growth in citizen science in academic contributions—researches by the paradigms of different disciplines and by the activities of citizens when undertaking data collecting, data processing, and data analyzing for disseminating results. These researches have proved the importance of data management practices—urgent to carry out the data life cycle. This study aims to analyze the scientific data contribution of citizen science under the data life cycle approach. It investigates 1,020 citizen science projects within the DataONE life cycle framework, which includes data management plan, data collection, data quality assurance, data documentation, data discovery, data integration, data preservation, and data analysis. As the major finding, the result of this study shows that the data management plan is developed with the leading of universities, which are the host of the majority of citizen science projects. The processes of data collection, data quality assurance, data documentation, data preservation, and data analysis are well organized with the systematic tool in the Information and Communications Technology (ICT) age; meanwhile the citizen science projects are cumulative. Data discovery has mostly linked with SciStarter (citizen science community site) and Facebook (social media). In data integration, it is found that most of the projects integrate with global observation. Finally, the study provides the process and procedure of citizen science data management in an effort to contribute the scientific data and the design of data life cycle to academic and governmental works.
The current coronavirus disease 2019 (COVID-19) pandemic is making fundamental changes to our life, our society, and our thinking. The substantial influx of information on disease updates, case analysis, suggestions, and recordings leads one to contemplate what information professionals and information scientists can contribute to shorten the pandemic, improve human lives, and build a more impactful profession. This viewpoint examines concepts related to misinformation and discusses the responsibilities of information scientists, especially in the context of independent thinking. It suggests that research on and education in information science could help to develop independent thinking and train independent thinkers.
The research on knowledge diffusion in the field of intellectual property is growing based on the current research techniques, but it is mostly focused on some of the subfields, such as patent documents and technology transfer. What is lacking in the literature is a comprehensive profile of the discipline. The paper uses bibliometric methods and visualization technologies to conduct a profiling analysis of Web of Science journal articles in the field of intellectual property from the three aspects of authorship network, geographical diffusion of collaboration, and subject cluster. Facilitated by visualization software and programming scripts, the paper presents the highly active scholars in the field through author co-citation analysis (ACA) and document co-citation analysis (DCA), the diffusion networks of collaboration through dynamic geographical graphs, and the five main clusters of disciplinary subjects, namely, Innovation, Judicature, Legislation, Information, and Market.
During the coronavirus global pandemic crisis, we have received information from authentic and inauthentic sources. Fake news, continuous rumors, and prejudiced opinions from digital platforms and social media have the capacity to disrupt social harmony, to stall personal development, and to undermine trust on all levels of human interaction. Despite the wide plurality of perspectives, the diversity of contents, the variety of voices, and the many often-conflicting reasons for publishing, our interactions with information on digital devices are progressively shaping such situations and affecting decisions on all levels. We look at the limitations of existing designs and guidelines in the current paradigm, and we ask to what extent researchers and developers can focus and contribute, through their innovations, to the reduction of uncertainty and cases of misdirection, how they can mitigate tensions between information and humans, and how they can contribute to the maintenance and enhancement of worthy human values. Human-engaged computing (HEC) calls for innate user capacities to be enhanced rather than displaced by digital technologies so that the human factor in interactions is fully exploited and truly efficient symbiotic relationships between humans and devices can be achieved. Under the framework of HEC, we propose 12 research agendas from the theoretical, principled, and practical aspects, in order to develop future synergized interactions between humans and information. The present crisis presents us with a good opportunity to reflect on the need to empower humans in relation to the tools they use and to consider the next paradigm shift for designing information interaction.
Information release is an important way for governments to deal with public health emergencies, and plays an irreplaceable role in promoting epidemic prevention and control, enhancing public awareness of the epidemic situation and mobilizing social resources. Focusing on the coronavirus disease 2019 (COVID-19) epidemic in China, this investigation chose 133 information release accounts of the Chinese government and relevant departments at the national, provincial, and municipal levels, including Ministries of the State Council, Departments of Hubei Province Government, and Bureaus of Wuhan Government, covering their portals, apps, Weibos, and WeChats. Then, the characteristics such as scale, agility, frequency, originality, and impact of different levels, departments, and channels of the information releases by the Chinese government on the COVID-19 epidemic were analyzed. Finally, the overall situation was concluded by radar map analysis. It was found that the information release on the COVID-19 epidemic was coordinated effectively at different levels, departments, and channels, as evidenced by the complementarity between channels, the synergy between the national and local governments, and the coordination between departments, which guaranteed the rapid success of the epidemic prevention and control process in China. This investigation could be a reference for epidemic prevention and control for governments and international organizations, such as the World Health Organization (WHO), during public health emergencies, e.g. the COVID-19 pandemic.
The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.