Tracking and Mining the COVID-19 Research Literature

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.

[1]  D. Swanson Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[2]  Michael D. Gordon,et al.  Toward Discovery Support Systems: A Replication, Re-Examination, and Extension of Swanson's Work on Literature-Based Discovery of a Connection between Raynaud's and Fish Oil , 1996, J. Am. Soc. Inf. Sci..

[3]  R. Chandra Nutrition, immunity and infection: from basic knowledge of dietary manipulation of immune responses to practical application of ameliorating suffering and improving survival. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Michael D. Gordon,et al.  Toward Discovery Support Systems: A Replication, Re-Examination, and Extension of Swanson's Work on Literature-Based Discovery of a Connection between Raynaud's and Fish Oil , 1996, J. Am. Soc. Inf. Sci..

[5]  Neil R. Smalheiser,et al.  Artificial Intelligence An interactive system for finding complementary literatures : a stimulus to scientific discovery , 1995 .

[6]  N R Smalheiser,et al.  Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. , 1998, Computer methods and programs in biomedicine.

[7]  Michael D. Gordon,et al.  Literature-Based Discovery by Lexical Statistics , 1999, J. Am. Soc. Inf. Sci..

[8]  Alan L. Porter,et al.  Research profiling: Improving the literature review , 2002, Scientometrics.

[9]  Chaomei Chen,et al.  Tech Mining: Exploiting New Technologies for Competitive Advantage , 2005, Inf. Process. Manag..

[10]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Marc Weeber,et al.  Literature-based Discovery , 2008 .

[12]  Neil R. Smalheiser,et al.  Arrowsmith two-node search interface: A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE , 2009, Comput. Methods Programs Biomed..

[13]  Henry G. Small,et al.  Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy , 2010, Scientometrics.

[14]  Jeppe Nicolaisen,et al.  Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics , 2009, J. Assoc. Inf. Sci. Technol..

[15]  Alan Singleton,et al.  Bibliometrics and Citation Analysis; from the Science Citation Index to Cybermetrics , 2010, Learn. Publ..

[16]  Ronald N. Kostoff,et al.  Literature-related discovery: Potential treatments and preventatives for SARS , 2011, Technological Forecasting and Social Change.

[17]  Nils C. Newman,et al.  Emergence as a conceptual framework for understanding scientific and technological progress , 2012, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[18]  K. Fujita,et al.  Finding linkage between technology and social issues: A literature based discovery approach , 2012, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[19]  Min Song,et al.  Entitymetrics: Measuring the Impact of Entities , 2013, PloS one.

[20]  Ying Guo,et al.  Nano-enabled drug delivery: a research profile. , 2014, Nanomedicine : nanotechnology, biology, and medicine.

[21]  Jiancheng Guan,et al.  Measuring scientific research in emerging nano-energy field , 2014, Journal of Nanoparticle Research.

[22]  Alan L. Porter,et al.  Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells , 2015 .

[23]  Daniele Rotolo,et al.  Emerging Technology , 2001 .

[24]  Ronald N. Kostoff,et al.  Literature-related discovery and innovation: Chronic kidney disease , 2015 .

[25]  G. Davison,et al.  Nutritional and Physical Activity Interventions to Improve Immunity , 2016, American journal of lifestyle medicine.

[26]  Alan L. Porter,et al.  Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research , 2016 .

[27]  Thomas C. Rindflesch,et al.  Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery , 2016, Methods of Information in Medicine.

[28]  Alan L. Porter,et al.  Nano-Enabled Drug Delivery in Cancer Therapy: Literature Analysis Using the MeSH System , 2016 .

[29]  Zhong Lin Wang,et al.  Evolutionary trend analysis of nanogenerator research based on a novel perspective of phased bibliographic coupling , 2017 .

[30]  Jan L. Youtie,et al.  Tracking the emergence of synthetic biology , 2017, Scientometrics.

[31]  Eu-Gene Siew,et al.  Learning the heterogeneous bibliographic information network for literature-based discovery , 2017, Knowl. Based Syst..

[32]  Yi Zhang,et al.  Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics , 2017, J. Assoc. Inf. Sci. Technol..

[33]  Alan L. Porter,et al.  A hybrid method to trace technology evolution pathways: a case study of 3D printing , 2017, Scientometrics.

[34]  Sampo Pyysalo,et al.  Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches , 2018, BMC Bioinformatics.

[35]  Steven J. M. Jones,et al.  A collaborative filtering-based approach to biomedical knowledge discovery , 2018, Bioinform..

[36]  Alan L. Porter,et al.  An indicator of technical emergence , 2018, Scientometrics.

[37]  Alan L. Porter,et al.  Prevention and reversal of Alzheimer's disease: treatment protocol , 2018 .

[38]  David J. Schoeneck,et al.  Learning about learning: patterns of sharing of research knowledge among Education, Border, and Cognitive Science fields , 2019, Scientometrics.

[39]  Alan L. Porter,et al.  An approach to identify emergent topics of technological convergence: A case study for 3D printing , 2019, Technological Forecasting and Social Change.

[40]  Ying Huang,et al.  Collaborative networks in gene editing , 2019, Nature Biotechnology.

[41]  R. Kostoff Treatment Repurposing using Literature-related Discovery , 2019, J. Sci. Res..

[42]  Alan L. Porter,et al.  Emergence scoring to identify frontier R&D topics and key players , 2019, Technological Forecasting and Social Change.

[43]  Alan L. Porter,et al.  Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study , 2019, Technological Forecasting and Social Change.

[44]  Alan L. Porter,et al.  Application of Text-Analytics in Quantitative Study of Science and Technology , 2019, Springer Handbook of Science and Technology Indicators.

[45]  Alan L. Porter,et al.  Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles , 2019, Technological Forecasting and Social Change.

[46]  J. Youtie,et al.  Research addressing emerging technological ideas has greater scientific impact , 2019, Research Policy.

[47]  Alan L. Porter,et al.  Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures , 2019, Technological Forecasting and Social Change.

[48]  Mike Thelwall,et al.  Springer Handbook of Science and Technology Indicators , 2019, Springer Handbook of Science and Technology Indicators.

[49]  Wolfgang Glänzel,et al.  How scientific research reacts to international public health emergencies: a global analysis of response patterns , 2020, Scientometrics.

[50]  The potential of drug repositioning as a short-term strategy for the control and treatment of COVID-19 (SARS-CoV-2): a systematic review , 2020, Archives of Virology.

[51]  Mike Thelwall,et al.  Coronavirus research before 2020 is more relevant than ever, especially when interpreted for COVID-19 , 2020, Quantitative Science Studies.

[52]  R. Kostoff,et al.  COVID-19: Post-lockdown guidelines , 2020, International journal of molecular medicine.

[53]  Milad Haghani,et al.  Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature , 2020, Scientometrics.

[54]  D. Matchar,et al.  Coronavirus disease 2019 (COVID-19): an evidence map of medical literature , 2020, BMC Medical Research Methodology.

[55]  Exploring Genetic Basis for Diseases Through a Heterogeneous Bibliometric Network: Methodology and a Case Study , 2020 .

[56]  Yi Zhang,et al.  Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research , 2020, PloS one.

[57]  Jeffrey Brainard,et al.  New tools aim to tame pandemic paper tsunami. , 2020, Science.

[58]  J. Moran-Gilad,et al.  Scientometric trends for coronaviruses and other emerging viral infections , 2020, GigaScience.

[59]  R. Kostoff Combining Tactical and Strategic Treatments for COVID-19 , 2020 .

[60]  Vincent A. Traag,et al.  A scientometric overview of CORD-19 , 2020, bioRxiv.

[61]  Amy W. Ando,et al.  Ecology and economics for pandemic prevention , 2020, Science.

[62]  J. Homolak,et al.  Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders , 2020, Scientometrics.

[63]  Alan L. Porter,et al.  Measuring tech emergence: A contest , 2020 .

[64]  Antonio F. Hernández,et al.  COVID-19, an opportunity to reevaluate the correlation between long-term effects of anthropogenic pollutants on viral epidemic/pandemic events and prevalence , 2020, Food and Chemical Toxicology.

[65]  Alan L. Porter,et al.  Parallel or Intersecting Lines? Intelligent Bibliometrics for Investigating the Involvement of Data Science in Policy Analysis , 2021, IEEE Transactions on Engineering Management.

[66]  Science of the Pandemic , 2021, The Enablers.