This is the second Research Topic of a series. The first volume can be found
here.
The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. As we face the unprecedented level of uncertainty, doctors, patients, policy makers, and many more urgently need answers to questions that can help them make decisions and guide them to take the most appropriate course of action. The novel coronavirus behind the COVID-19 is new, but scientists have published a mountain of research findings on coronavirus in a broad context. What can we learn from the vast amount of studies in the coronavirus research landscape? How can we draw inspirations from collective knowledge as a whole?
The number of open datasets of the scholarly literature on coronavirus is rapidly increasing, notably including the
COVID-19 Open Research Dataset (CORD-19) from the Allen Institute for AI and its partner institutions, the COVID-19 Datasets of scholarly works and patents released by
The Lens, and an increasing number of openly accessible Special Collections of
Cochrane Reviews. The increasingly accessible scholarly literature needs to be digested, systematically reviewed, and translated into actionable answers. Furthermore, it is important for findings and insights derived from the study of these newly available scholarly works to reach everyone who can benefit from it.
The purpose of the Research Topic on Coronavirus Research Landscape is to provide a forum and a gateway to make the collective knowledge more accessible, timely and effective. We will welcome contributions that can shed lights on our understanding of the COVID-19 disease and research in a broader context of coronavirus. Welcome contributions to the Research Topic include, but are not limited to:
• new resources of scholarly datasets on the topic
• novel utilities and enabling tools that may make these datasets more accessible and understandable
• systems and tools for analyzing available scholarly literature datasets
• applications of text mining and literature-based discovery techniques to the study of the relevant scholarly literature
• applications of machine learning and AI techniques to the study of the relevant scholarly literature
• meta-analyses, systematic reviews, and scientometric reviews of the landscape of the scholarly works, patents, clinical trials, grants, and other integral parts of scientific inquiries.
Our aim, in the long run, is to establish a sustainable platform for researchers to share resources and results of studies that utilize these resources.
