Tracking and Mining the COVID-19 Research Literature

Tech mining: a revisit and navigation

This mini-review arrays the pertinent tools and purposes of "Tech Mining" - shorthand for empirical analyses of Science, Technology and Innovation (ST&I) data. The intent is to introduce the range of tools, and show how they can complement each other. Tech Mining aims to generate powerful intelligence to help manage R&D and innovation processes. We offer a 5-part array to help relate the analytical elements. An overview of a case study of Hybrid and Electric Vehicles illustrates the complexities involved and the potential to generate valuable "intel."

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Citation advantage of COVID-19-related publications

With the global spread of the COVID-19 pandemic, scientists from various disciplines responded quickly to this historical public health emergency. The sudden boom of COVID-19-related papers in a short period of time may bring unexpected influence to some commonly used bibliometric indicators. By a large-scale investigation using Science Citation Index Expanded and Social Sciences Citation Index, this brief communication confirms the citation advantage of COVID-19-related papers empirically through the lens of Essential Science Indicators’ highly cited paper. More than 8% of COVID-19-related papers published during 2020 and 2021 were selected as Essential Science Indicators highly cited papers, which was much higher than the set global benchmark value of 1%. The citation advantage of COVID-19-related papers for different Web of Science categories/countries/journal impact factor quartiles was also demonstrated. The distortions of COVID-19-related papers’ citation advantage to some bibliometric indicators such as journal impact factor were discussed at the end of this brief communication.

The long COVID research literature

While the COVID-19 pandemic morphs into less malignant forms, the virus has spawned a series of poorly understood, post-infection symptoms with staggering ramifications, i. e., long COVID (LC). This bibliometric study profiles the rapidly growing LC research domain [5,243 articles from PubMed and Web of Science (WoS)] to make its knowledge content more accessible. The article addresses What? Where? Who? and When? questions. A 13-topic Concept Grid presents bottom-up topic clusters. We break out those topics with other data fields, including disciplinary concentrations, topical details, and information on research "players" (countries, institutions, and authors) engaging in those topics. We provide access to results via a Dashboard website. We find a strongly growing, multidisciplinary LC research domain. That domain appears tightly connected based on shared research knowledge. However, we also observe notable concentrations of research activity in different disciplines. Data trends over 3 years of LC research suggest heightened attention to psychological and neurodegenerative symptoms, fatigue, and pulmonary involvement.

The role of blogs and news sites in science communication during the COVID-19 pandemic

We present a brief review of literature related to blogs and news sites; our focus is on publications related to COVID-19. We primarily focus on the role of blogs and news sites in disseminating research on COVID-19 to the wider public, that is knowledge transfer channels. The review is for researchers and practitioners in scholarly communication and social media studies of science who would like to find out more about the role of blogs and news sites during the COVID-19 pandemic. From our review, we see that blogs and news sites are widely used as scholarly communication channels and are closely related to each other. That is, the same research might be reported in blogs and news sites at the same time. They both play a particular role in higher education and research systems, due to the increasing blogging and science communication activity of researchers and higher education institutions (HEIs). We conclude that these two media types have been playing an important role for a long time in disseminating research, which even increased during the COVID-19 pandemic. This can be verified, for example, through knowledge graphs on COVID-19 publications that contain a significant amount of scientific publications mentioned in blogs and news sites.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology

Background

Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.

Method

We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.

Results

We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.

Conclusion

In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Visibility, collaboration and impact of the Cuban scientific output on COVID-19 in Scopus

INTRODUCTION

COVID-19 is a disease with worldwide impact that has fully caught attention of researchers. The Cuban scientific output, after one year of confronting this pandemic, has not been studied from a bibliometric perspective.

OBJECTIVE

To characterize the output of original scientific articles and review articles on COVID-19 published by Cuban authors in the journals included in the Scopus bibliographic database, the collaborations in these publications and their impact, according to the citation of the research in the world literature.

MATERIALS AND METHODS

A cross-sectional, descriptive and observational study was performed, using a bibliometric approach. A search strategy was used to retrieve articles on the subject and bibliometric indicators of output, visibility, leadership, collaboration and impact were studied.

RESULTS

Cuba contributed 2.5% of the Latin American output and 0.2% of the world output. Of the national scientific output (133 articles, 111 original and 22 reviews), 84.2% were authored by a Cuban corresponding author (Cuban leadership). However, the majority (n = 20; 71.4%) of articles with international collaboration (n = 28; 21.1%) had foreign corresponding authors. Of the total, 33.8% (n = 45) corresponded to articles without collaboration. Only 13.5% of the articles (n = 18) were published in journals with high visibility (Q1). Of all the output, 68.4% (n = 91) was in Cuban journals. The output in English represented 29.3% (n = 39) and achieved greater impact than the articles in Spanish in terms of citations. As the visibility of the journals increased according to the quartiles where they are, the percentage of articles published in English and cited articles increased too, but Cuban scientific leadership decreased.

CONCLUSIONS

The greater the leadership in Cuban research, the lower its impact, and the lower the indexes of international collaboration. Cuban researchers are not yet able to systematically generate research that has a significant impact on the international scientific community.

Determination of Factors Driving the Genome Editing Field in the CRISPR Era Using Bibliometrics

Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing.

Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.

Big Data Research in Fighting COVID-19: Contributions and Techniques

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.

Impact of Coronavirus Outbreaks on Science and Society: Insights from Temporal Bibliometry of SARS and COVID-19

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both scientific and social standpoints. Despite several documented similarities, the coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20,000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. Unlike the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic to study changes in thematic foci and societal pressures dictating research over the course of a crisis.

Topic evolution, disruption and resilience in early COVID-19 research

The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.

Topic evolution, disruption and resilience in early COVID-19 research

The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.