Challenges for FAIR Digital Object Assessment

A Digital Object (DO) "is a sequence of bits, incorporating work or portion other information in which party has rights interests, there is value". DOs should have persistent identifiers, meta-data and be readable by both humans machines. FAIR DO able to interact with automated data processing systems (De Smedt et al. 2020) while following the (Findable, Accessible, Interoperable Reusable principles) principles (Wilkinson 2016). Although was originally targeted towards artifacts, new initiatives emerged adapt research digital resources such as software (Katz 2021) (Lamprecht 2020), ontologies (Poveda-Villalón virtual environments even (Collins 2018). In this paper, we describe challenges when assessing level compliance (i.e., its FAIRness), assuming that contains multiple captures their relationships. We explore different methods calculate an evaluation score, discuss challeneges importance providing explanations guidelines for authors. assessment tools There are growing number used assess FAIRness DOs. Community groups like FAIRassist.org compiled lists resources. These range from self questionnaires checklists semi-automated validators (Devaraju 2021). Examples validation include F-UJI Automated Data Assessment Tool Huber Evaluator Checker datasets individual DOs; HowFairIs (Spaaks code repositories; FOOPS (Garijo ontologies. When it comes FDOs, find two main challenges: Resource score discrepancy : Different same type resource produce scores. For example, recent study over showcases differences scores due how interpreted authors (Dumontier 2022). Heterogeneous FDO metadata Validators tests object. However, no agreed schema represent metadata, complicates operation. addition, may specific certain domain 2020). To address challenge, need i) agree on minimum common set measure ii) propose framework extensions specialized objects (datasets, software, ontologies, VRE, etc.). 2019), community-driven objects. This based on: collection maturity indicators, principle tests, module apply those The proposed indicators starting point define needed each (de Miranda Azevedo Dumontier Aggregation metrics Another challenge best way FDO, independently run it. four dimensions Reusable) usually associated tests. If final presented then default some more than others. Similarly, not all (e.g., cases having license considered important full documentation). our consider aggregation resources, therefore face additional creating aggregated whole FDO. scores: Global calculated formula (see Fig. 1-1). It represents percentage total passed doesn’t take into account test belongs. average 1- 2). ratios plus ratio evaluate Research itself. Both agnostic kind analyzed. they ranges [0 - 100]. Discussion records Some DOs, others DO. makes difficult "F2: "data described rich metadata". Therefore, believe discussion minimal addressed community. can change significantly depending aggregating metrics. key explain users method provenance score. communities scoring mechanism e.g., adding weight figuring out right principle, give objective system ranking, but become improve