Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders
Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders
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DOI：
10.1007/s11192-020-03587-2
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发表时间：
2020-06-25
期刊：
影响因子：
3.9
通讯作者：
Virag, D.
中科院分区：
文献类型：
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作者：
Homolak, J.;Kodvanj, I.;Virag, D.
由SARS-CoV-2引起的传染病COVID-19大流行促使科学界共同努力，收集、组织、处理和分发有关这种新型生物医学危害的数据。在这里，我们通过量化与COVID-19相关的学术信息的分布和可用性模式，分析了科学界如何应对这一挑战。这项研究的目的是评估信息流和科学合作的质量，我们认为这两个因素对于为当前的大流行找到新的解决办法至关重要。RISmed R包和自定义Python脚本用于获取PubMed索引文章的元数据，并在Rxiv预印本服务器上发布。手动搜索Scopus，导出BibTex文件。在r中分析了发表率和发表状态，每篇文章的隶属关系和作者数量，以及提交到出版时间。使用Biblioshiny应用程序创建了世界合作地图。初步数据表明，2019冠状病毒病大流行导致了大量科学数据的产生，并显示了大流行早期在信息速度、可用性和科学协作方面存在的潜在问题。更具体地说，结果表明标准出版系统的危险超载，数据可用性的重大问题和明显缺乏合作。总之，我们认为科学界本可以更有效地利用这些数据，为寻找COVID-19大流行的新解决方案奠定适当的基础。此外，我们相信我们可以从中吸取教训，采用开放科学原则和更谨慎的方法处理与covid -19相关的数据，以加速发现更有效的解决方案。我们借此机会邀请我们的同事以最大的透明度发表他们的发现，为这一全球科学合作做出贡献。
The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, andsubmission-to-publicationtime were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.