Comparing FAIR Assessment Tools and their Alignment with FAIR Implementation Profiles Using Digital Humanities Datasets

FAIR principles serve as guidelines for implementing data and metadata to improve Findability, Accessibility, Interoperability, and Reusability. In recent years, numerous tools have been developed to assess how well datasets adhere to each FAIR principle. However, due to their diverse designs, these tools interpret the FAIR principles differently and provide varying assessment results, which can be confusing. Many communities publish datasets that follow similar data management practices, and some of these common practices have recently been compiled into community standards known as FAIR Implementation Profiles (FIPs). This paper compares the metrics of FAIR assessment tools with FIP. We illustrate these differences by analyzing the assessment results of two datasets in the Digital Humanities domain and further explore how these results compare with their corresponding FIPs.