COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

COVIDiSTRESS Global Survey Consortium

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This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.

Original languageEnglish
Article number3
JournalScientific Data
Publication statusPublished - 04 Jan 2021
Externally publishedYes

Bibliographical note

Funding Information:
The COVIDiSTRESS consortium would like to acknowledge the additional contributions of numerous friends and collaborators in translating and sharing the COVIDiSTRESS survey, even if contributions were small or the person did not wish their name included as a member of the consortium. All funding information is listed in the supplementary material (Figure S1). We also want to address thanks to the IFB (Institut Français de Bioinformatique, for hosting the server Shiny illustrating our results. This research was supported by JSPS KAKENHI Grants JP17H00875, JP18K12015, JP20H04581, JP20K14222, Czech Science Foundation GC19-09265J, Consejo Nacional de Ciencia y Tecnologia (Conacyt), Full National Scholarship - MSc degree (CVU: 613905), Research Foundation Flanders (FWO) postdoctoral fellowship, and The HSE University Basic Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021, The Author(s).

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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