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

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

Journal of Informetrics, 2024, vol. 18, issue 2

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.

Keywords: Open data; Knowledge flow; Major health emergencies; Data citation; Scientific datasets (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S175115772400018X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:18:y:2024:i:2:s175115772400018x

DOI: 10.1016/j.joi.2024.101505

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().