Does open data have the potential to improve the response of science to public health emergencies?

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics Pub Date : 2024-02-10 DOI:10.1016/j.joi.2024.101505

Xiaowei Ma , Hong Jiao , Yang Zhao , Shan Huang , Bo Yang

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引用次数: 0

Abstract

Open data was recognized as essential to prevent and treat pandemic infection through sharing, disseminating, and using relevant information. This study explores how and to what extent open data influenced the response of science to such emergencies from a quantitative perspective. Based on the genetic datasets for viruses associated with Ebola, SARS, MERS, and COVID-19, we analyze the efficiency of data sharing and dissemination from a knowledge flow perspective: "datasets→papers", "datasets→patents", and "datasets→papers→patents". The results showed: (1) From the early Ebola outbreak to the recent COVID-19 pandemic, data sharing has been increasingly open and timely. (2) Basic research and the developments of vaccine and medicine related to the pandemics have increasingly relied on open data, providing more data-driven alternatives. (3) From Ebola to COVID-19, the citation lags of highly cited datasets have decreased in both papers and patents, demonstrating that open data can accelerate the development of science and technology to address the epidemics. In conclusion, open data can potentially improve science's response to public health emergencies by saving precious time. Therefore, much greater efforts by the scientific community to open data are well deserved.

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开放数据是否具有改善科学应对公共卫生突发事件的潜力？

开放数据被认为是通过共享、传播和使用相关信息来预防和治疗大流行病感染的关键。本研究从定量的角度探讨了开放数据如何以及在多大程度上影响了科学对此类突发事件的响应。基于埃博拉、SARS、MERS 和 COVID-19 相关病毒的基因数据集，我们从知识流的角度分析了 "数据集→论文"、"数据集→专利 "和 "数据集→论文→专利 "的数据共享和传播效率。结果显示(1) 从早期的埃博拉疫情到近期的 COVID-19 大流行，数据共享越来越开放和及时。(2) 与大流行相关的基础研究、疫苗和药物开发越来越依赖于开放数据，提供了更多数据驱动的替代方案。(3）从埃博拉到 COVID-19，高被引数据集在论文和专利中的引用滞后期均有所下降，这表明开放数据可以加快应对流行病的科技发展。总之，开放数据可以节省宝贵的时间，从而改善科学应对公共卫生突发事件的能力。因此，科学界理应在开放数据方面做出更大的努力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Informetrics Social Sciences-Library and Information Sciences

CiteScore

6.40

自引率

16.20%

发文量

期刊介绍： Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.