The spread of the Sars-COV-2 pandemic in 2020 had a huge impact on the life
course of all of us. This rapid spread has also caused an increase in the
research production in topics related to COVID-19 with regard to different
aspects. Italy has, unfortunately, been one of the first countries to be
massively involved in the outbreak of the disease. In this paper we present an
extensive scientometric analysis of the research production both at global
(entire literature produced in the first 2 years after the beginning of the
pandemic) and local level (COVID-19 literature produced by authors with an
Italian affiliation). Our results showed that US and China are the most active
countries in terms of number of publications and that the number of
collaborations between institutions varies according to geographical distance.
Moreover, we identified the medical-biological as the fields with the greatest
growth in terms of literature production. Furthermore, we also better explored
the relationship between the number of citations and variables obtained from
the data set (e.g. number of authors per article). Using multiple
correspondence analysis and quantile regression we shed light on the role of
journal topics and impact factor, the type of article, the field of study and
how these elements affect citations.

该研究对全球和意大利的科研产出进行了广泛的科学计量分析，发现美国和中国是最活跃的国家，在疫情期间，医学 - 生物领域的文献生产增长最快，并探索了期刊主题和影响因子，文章类型和研究领域对引用的影响。

COVID-19 对科研产出传播影响的科学计量分析

A scientometric analysis of the effect of COVID-19 on the spread of  research outputs

The number of scientific publications continues to rise exponentially,
especially in Computer Science (CS). However, current solutions to analyze
those publications restrict access behind a paywall, offer no features for
visual analysis, limit access to their data, only focus on niches or
sub-fields, and/or are not flexible and modular enough to be transferred to
other datasets. In this thesis, we conduct a scientometric analysis to uncover
the implicit patterns hidden in CS metadata and to determine the state of CS
research. Specifically, we investigate trends of the quantity, impact, and
topics for authors, venues, document types (conferences vs. journals), and
fields of study (compared to, e.g., medicine). To achieve this we introduce the
CS-Insights system, an interactive web application to analyze CS publications
with various dashboards, filters, and visualizations. The data underlying this
system is the DBLP Discovery Dataset (D3), which contains metadata from 5
million CS publications. Both D3 and CS-Insights are open-access, and
CS-Insights can be easily adapted to other datasets in the future. The most
interesting findings of our scientometric analysis include that i) there has
been a stark increase in publications, authors, and venues in the last two
decades, ii) many authors only recently joined the field, iii) the most cited
authors and venues focus on computer vision and pattern recognition, while the
most productive prefer engineering-related topics, iv) the preference of
researchers to publish in conferences over journals dwindles, v) on average,
journal articles receive twice as many citations compared to conference papers,
but the contrast is much smaller for the most cited conferences and journals,
and vi) journals also get more citations in all other investigated fields of
study, while only CS and engineering publish more in conferences than journals.

本论文使用计量学方法分析计算机科学的元数据，并使用交互式 Web 应用程序 CS-Insights 进行数据可视化，发现了计算机科学领域在过去两十年中出现了大量新的作者和出版物，会议论文的受欢迎程度下降，期刊文章的引用次数更高。