FAIRness of COVID-19-related research data

Description: This dataset is a comprehensive investigation into the FAIRness (Findable, Accessible, Interoperable, and Reusable) of open data related to COVID-19 research. With the COVID-19 pandemic prompting a surge in research and publications, this dataset assesses the adherence of these COVID-19-related research data to FAIR principles.

Using automated tools and validated methods, the dataset includes information on open-access articles, their associated data repositories, and their compliance with FAIR metrics. It covers data from January 2020 to June 2023 and provides insights into Findability, Accessibility, Interoperability, and Reusability.

The dataset comprises 5,700 URLs from general-purpose repositories, offering a mean FAIRness score of 9.4. It also highlights trends, variations across article types and journal categories, and influential factors affecting FAIRness.

This dataset contributes valuable insights into the status of data sharing and FAIR principles within the context of COVID-19 research, emphasizing areas for potential improvement in data quality and accessibility.