The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a
new strain of coronavirus, causing the COVID-19 pandemic, and radically
changing our lives and work conditions. Many scientists are working tirelessly
to find a treatment and a possible vaccine. Furthermore, governments,
scientific institutions and companies are acting quickly to make resources
available, including funds and the opening of large-volume data repositories,
to accelerate innovation and discovery aimed at solving this pandemic. In this
paper, we develop a novel automated theme-based visualisation method, combining
advanced data modelling of large corpora, information mapping and trend
analysis, to provide a top-down and bottom-up browsing and search interface for
quick discovery of topics and research resources. We apply this method on two
recently released publications datasets (Dimensions' COVID-19 dataset and the
Allen Institute for AI's CORD-19). The results reveal intriguing information
including increased efforts in topics such as social distancing; cross-domain
initiatives (e.g. mental health and education); evolving research in medical
topics; and the unfolding trajectory of the virus in different territories
through publications. The results also demonstrate the need to quickly and
automatically enable search and browsing of large corpora. We believe our
methodology will improve future large volume visualisation and discovery
systems but also hope our visualisation interfaces will currently aid
scientists, researchers, and the general public to tackle the numerous issues
in the fight against the COVID-19 pandemic.

本文提供了一种结合大型数据建模、信息制图和趋势分析的新型自动化主题可视化方法，用于快速发现 COVID-19 疫情下的主题和研究资源，结果显示了有关社交隔离、医疗话题、病毒在不同地区的演变轨迹等信息，同时也表明了迫切需要快速自动化地进行大规模数据的搜索和浏览。