Towards an extensible FAIRness assessment of FAIR Digital Objects

The objective of the FAIR Digital Objects Framework (FDOF) is for objects published in a digital environment to comply with set requirements, such as identifiability, and use rich metadata record (Santos 2021, Schultes Wittenburg 2019, Schwardmann 2020). With increasing prevalence (Findable, Accessible, Interoperable, Reusable) principles, (FDO), used within different communities domains (Wise et al. 2019), there will be need evaluate whether FDO meets requirements ecosystem which it used. Without dedicated framework, develop isolated assessment systems from ground up (Sun 2022, Bahim 2020), cost them time, lead FAIRness assessments limited interoperability comparability. Previous work Metrics working group defined framework deploying individual metrics tests separate services endpoints (Wilkinson 2018, Wilkinson 2019). To accordance this each test should take subject URL input, return score, either 0 or 1, version, execution logs. A central service can then assess using collections assessments. Such could easily extended, but are currently no guidelines tools implement publish new complying framework. amend problem, we fair-test library python its documentation, help developing library, developers define their metric custom objects, guide provide all required attributes, evaluation logic function. also provides additional helper functions common tasks, retrieving URL, testing test. These deployed web API, registered supporting enough evaluator. Finally, users able quality publicly available objects. There many 47 that have been compliance various metrics, 25 including assessing if identifier persistent, attached object complies specific schema. This presentation introduces user-friendly extensible tool, met resource. Our contributions are: Developing publishing make development deployment independent easier. existing metrics: 23 generic covering most 2 domain-specific Rare Disease research community. We aim engage community explore potential use-cases an tool FDOs, discuss expectations related Insights would contribute further improving ecosystem. Among improvements being under consideration, cite collaborative aspect extraction, adding returned by tests.