CoronaNet

This is the permanent home for data and resources related to CoronaNet, a project to create a policy dataset for government actions in response to COVID-19. Data collection for this project has finished and current efforts are aimed at cleaning and systematizing the data. Currently there is coverage for most countries in the world and for hundreds of sub-national units, including provinces and cities, from January of 2020 through December of 2021. The most complete coverage is for the European Union and Russia thanks for a EU Horizons Initiative grant. Coverage of other countries has been increased through data integration from other COVID-19 policy projects, including the Oxford COVID-19 Government Response Tracker and COVID-AMP.

At present, the project is being led by PIs Cindy Cheng and Robert Kubinec.

Data Access

For questions about the data and accessing it or using it, please direct them to Robert Kubinec at rkubinec@mailbox.sc.edu.

Publications

Some of the papers that have come out of this project include:

Barceló, Joan, Robert Kubinec, Cindy Cheng, Tiril Høye Rahn, and Luca Messerschmidt. 2022. “Windows of Repression: Using COVID-19 Policies Against Political Dissidents?” Journal of Peace Research Forthcoming. https://osf.io/preprints/socarxiv/yuqw2/.
Cheng, Cindy, Joan Barcelo, Allison Spencer Hartnett, Robert Kubinec, and Luca Messerschmidt. 2020. “COVID-19 Government Response Event Dataset (CoronaNet v.1.0).” Nature Human Behavior. https://doi.org/https://doi.org/10.1038/s41562-020-0909-7.
Cheng, Cindy, Luca Messerschmidt, Isaac Bravo, Marco Waldbauer, Rohan Bhavikatti, Caress Schenk, Vanja Grujic, Tim Model, Robert Kubinec, and Joan Barceló. 2024. “A General Primer for Data Harmonization.” Scientific Data 11 (1): 152. https://doi.org/10.1038/s41597-024-02956-3.
Kubinec, Robert, Joan Barceló, Rafael Goldszmidt, Vanja Grujic, Timothy Model, Caress Schenk, Cindy Cheng, et al. 2024. “Cross-National Measures of the Intensity of COVID-19 Public Health Policies.” Journal of Politics Forthcoming. https://doi.org/10.31235/osf.io/rn9xk.