Open data, open science and transparency in the time of COVID 19.

Ball, William P.

Abstract

A novel coronavirus now known as SARS-CoV-2 was first reported in Wuhan, China in December 2019. It targets the respiratory system, with a wide range of symptom severity and results in a comparatively high level of mortality. Crucially, it has rapidly spread across the globe, affecting people living on all the majorly populated continents. The rapid spread, high mortality, range of severity and other unknown factors, have resulted in huge uncertainty. This means there is an urgent necessity to understand the characteristics of the virus and develop strategies to reduce its impact. To make the best decisions in this developing situation, we need information. COVID-19, the disease caused by the SARS-CoV-2 virus, has fundamentally changed the way society interacts with health data. It has become a huge and dominating focus for both public and media interest and now guides a large proportion of research effort. Our media (both traditional and social) is dominated by daily updates on new figures, visualisations and discussion. At the same time, many academics have shifted or pivoted their work towards studying the ongoing pandemic, as funding calls from major sources are seeking to invest large sums of money into COVID-specific research projects. As interest and concern have escalated, our requirement for information to learn more and inform decision-making has also increased. Concurrently, the production and use of Open Data and wider Open Science practices has accelerated at an incredible rate.

Citation

BALL, W.P. 2020. Open data, open science and transparency in the time of COVID-19. Radical statistics [online], 127, pages 4-9. Available from: https://www.radstats.org.uk/no127/Ball127.pdf

• Journal Article Type: Article
• Online Publication Date: Sep 1, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: Sep 14, 2023
• Publicly Available Date: Mar 29, 2024
• Journal: Radical statistics
• Print ISSN: 0268 6376
• Publisher: Radical Statistics Group
• Peer Reviewed: Not Peer Reviewed
• Issue: 127
• Pages: 4-9
• Keywords: Open data; Open science; Transparency; COVID-19
• Public URL: https://rgu-repository.worktribe.com/output/2078971
• Publisher URL: https://www.radstats.org.uk/journal/issue127/