Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioral measures and compliance with measures to contain the pandemic, social science research has played a key role for policymaking during this global crisis. This study addresses two key research questions: How FAIR (findable, accessible, interoperable, and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n=1,131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019-2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n=45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research. The replication data contains the manual and automatic coded values for FAIR criteria and the complete code to re-produce the results for the article.

Nicht-Wahrscheinlichkeitsauswahl - Willkürliche Auswahl

Field observationObservation.Field

FeldbeobachtungObservation.Field