2020年，全球爆发了前所未有的SARS-CoV-2，这是一种新型冠状病毒，导致COVID-19大流行，并从根本上改变了我们的生活和工作条件。许多科学家正在不知疲倦地寻找治疗方法和可能的疫苗。此外，各国政府、科研机构和公司正在迅速采取行动，提供资金和开放大容量数据库等资源，以加速旨在解决这一流行病的创新和发现。在本文中，我们开发了一种新的自动化的基于主题的可视化方法，结合先进的数据建模的大型语料库，信息映射和趋势分析，提供一个自上而下和自下而上的浏览和搜索界面，快速发现的主题和研究资源。我们将这种方法应用于两个最近发布的出版物数据集（Dimensions的COVID-19数据集和Allen Institute for AI的CORD-19）。研究结果揭示了一些有趣的信息，包括在社交距离等主题上的努力增加;跨领域举措（例如心理健康和教育）;医学主题的不断发展的研究;以及通过出版物在不同地区展开的病毒轨迹。结果还表明，需要快速和自动启用搜索和浏览大型语料库。我们相信我们的方法将改善未来的大容量可视化和发现系统，但也希望我们的可视化界面目前将帮助科学家，研究人员和公众解决抗击COVID-19大流行的众多问题。
英文摘要：
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.
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Paul G. Allen School for Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Information School, University of Washington（华盛顿大学信息学院）；Microsoft Research（微软研究院）；University of California, Berkeley（加利福尼亚大学伯克利分校）
Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing
National Research Council Canada（加拿大国家研究委员会）；Concordia University（康考迪亚大学）；University of New Brunswick（新不伦瑞克大学）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究院）
Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
School of Electrical and Computing Engineering, UNICAMP（坎皮纳斯州立大学电气与计算机工程学院）；NeuralMind Inteligência Artificial（NeuralMind 人工智能（公司））；David R. Cheriton School of Computer Science, University of Waterloo（滑铁卢大学大卫·R·谢里顿计算机科学学院）
Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature
