Can a Multi-Modular Ontology Aid Progress in Cognitive Neuroscience?

Establishing effective data management and sharing practices as hallmarks for progress in open science has gained increasing attention within the recent years. Cognitive Neuroscience, like many other disciplines, is still facing the challenge to integrate its conceptual knowledge with the vastly growing amounts of (neuroimaging) data. To fully exploit our data’s value and potential, we need to find a way to structure our scientific knowledge so that it can serve as a basis for rich data annotation and thus data (re-)usability. Seminal efforts towards this goal have already been made with collaborative knowledge building projects (e.g. Cognitive Atlas) and objective experiment description systems (e.g. CogPO). Given the latent and only indirectly observable nature of cognitive constructs, we aim to contribute to these efforts by introducing the idea of a theory-based, multi-modular cognitive ontology. I will give an overview of the general structure of our envisioned knowledge representation system, including functional, theoretical, experimental, and analytical aspects of neurocognitive research - providing maximal flexibility regarding conceptualisation, maintenance, and applicability from both epistemological and user perspectives. I will argue that incorporating cognitive theories and models as guiding principles for knowledge representation might bear the potential for comprehensive ontology-based data indexing and access.