FAIRification of computational models in biology

Abstract

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a model's features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

Figure 1:

Workflow of the FAIR COMBINE Archive Indicators project: an iterative and integrative community-level process to adapt the RDA FAIR indicators to the specificities of COMBINE models and archives. We extended the FAIRplus template of RDA FAIR indicators and performed FAIR assessment for 5 types of COMBINE models and archives from major model repositories (BioModels and PMR) at different curation stages.

Figure 2:

Example: FAIR assessment results for one of the model types considered during the workshops and hackathons - https://models.physiomeproject.org/e/48/elowitz_leibler_2000.cellml. Results are shown for the resource itself (Model or Archive) and the metadata component (namely Model metadata or Archive metadata), for each FAIR indicator. The result illustration is made using the FAIR-Viz tool. Legend: Findability (blue), Accessibility (orange), Interoperability (green), Reusability (red).