About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ESWC 2024
Workshop paper
An Appraisal of Automated Tools for FAIRness Evaluation
Abstract
The FAIR Principles were introduced to address data challenges and improve the Findability, Accessibility, Interoperability, and Reusability of digital resources, following several Semantic Web standards. 'FAIRness' corresponds to a percentage grade indicating how close a digital object is to fully abiding by those principles. Several tools have been developed to assess the FAIRness of data digital objects in support of enacting the FAIR Principles. This work offers an appraisal of tools that evaluate the FAIRness of such objects, focusing on fully automated solutions. We conduct a literature review about existing tools, extract from it a set of requirements they aim to fulfill, and assess how each one fares considering this ensemble. Our results help researchers and data stewards with an overview of the tools, including an analysis of the fulfillment of the requirements and existing gaps.