Developing FAIR, Machine-Actionable Metadata Templates for Materials, Nanomaterials, Chemicals, and Biological Assays

The lack of standardised, machine-actionable metadata remains a major barrier to the FAIRification (findability, accessibility, interoperability, and reusability) of data generated in materials, nanomaterials, and chemicals research. This is especially impactful in cases where the substances’ physicochemical characterisation is linked to biological and ecotoxicological outcomes. To address this, a set of comprehensive, machine-actionable and domain-specific metadata templates have been developed and implemented in nanodash and aligned with the FAIR data principles.

We designed and validated structured templates covering six key domains: i. physicochemical characterisation of chemicals, ii. physicochemical characterisation of materials and nanomaterials, iii. in vitro biological assays, iv. in vivo invertebrate biological assays, v. plant assays, and vi. in vivo vertebrate assays. Each template integrates controlled vocabularies, mapped to the I-ADOPT framework ontology, and picklists for materials descriptors, test species, exposure routes, and endpoints, alongside provenance, versioning, and persistent identifier metadata to support data reuse and computational integration.

To evaluate usability and efficiency, we conducted case studies in which researchers annotated and published datasets using both nanodash templates and traditional spreadsheet-based workflows with repository deposition. Results show a consistent reduction in metadata completion and publication time when using nanodash of up to 40%, while improving metadata consistency and machine readability.

The resulting templates comprise nearly 1,000 harmonised descriptors and endpoints and provide a reusable framework for FAIR data publication across experimental materials and chemical sciences. This work supports improved data stewardship, cross-domain integration, and readiness for automated analysis and regulatory reuse.

This work has received funding from the from the European Union’s Horizon Europe research and innovation programme under grant agreement 101178074 (AlChemiSSts).