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DDI study level documentation for study 10.7802/2729 Replication data for: Between Urgency and Data Quality: Assessing the FAIRness of Data in Social Science Research on the COVID-19 Pandemic Replication data for: Between Urgency and Data Quality: Assessing the FAIRness of Data in Social Science Research on the COVID-19 Pandemic Replication data for: Between Urgency and Data Quality: Assessing the FAIRness of Data in Social Science Research on the COVID-19 Pandemic 10.7802/272910.7802/272910.7802/272910.7802/2729 Batzdorfer, Veronika Batzdorfer, Veronika Zenk-Möltgen, Wolfgang Zenk-Möltgen, Wolfgang Young, Laura Young, Laura Katsanidou, Alexia Katsanidou, Alexia Breuer, Johannes Breuer, Johannes Bishop, Libby Bishop, Libby GESIS Data Archive for the Social SciencesGESIS Datenarchiv für Sozialwissenschaften social sciencehealthdata accessepidemicdata qualitydata capturedata collection methodmethodological researchcomparison of methodssocial sciencehealthdata accessepidemicdata qualitydata capturedata collection methodmethodological researchcomparison of methods 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.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. cross-sectioncross-section Non-probability Sample - Availability SampleNon-probability Sample - Availability Sample Field observationObservation.FieldFeldbeobachtungObservation.Field Free access (with registration) - The research data can be downloaded by registered users. CC BY 4.0: Attribution (https://creativecommons.org/licenses/by/4.0/deed.de)Freier Zugang (mit Registrierung) - Die Forschungsdaten können von allen registrierten Nutzerinnen und Nutzern heruntergeladen werden. CC BY 4.0: Attribution (https://creativecommons.org/licenses/by/4.0/deed.de) http://dbkapps.gesis.org/dbkoai/oai.asp oai:dbk.gesis.org:SDN/10.7802_2729 2024-06-24 ddi:codebook:2_5