nanoPharos: A case study on FAIR (Nano)material (Meta)data management

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Accelerating the market deployment of novel and advanced materials, including nanomaterials (NMs), is crucial for industrial and societal progress.
• Conventional research and development (R&D) cycles for new materials are lengthy, often spanning decades.
• Digitizing material R&D requires high-quality, interoperable data to support safe, sustainable development and reduce animal testing.

Purpose of the Study:

• To present nanoPharos, a data repository designed to facilitate the modeling and development of nanomaterials.
• To provide a framework for implementing FAIR (Findable, Accessible, Interoperable, Reusable) Data Principles in the context of nanomaterial science.
• To capture the complexity of NMs from atomic to macroscopic scales, integrating diverse data types for comprehensive analysis.

Main Methods:

• Developed nanoPharos, a repository based on an adapted ChemBL schema to store NM datasets.
• Integrated physicochemical characterization, mechanistic toxicity, exposure, and risk assessment data.
• Enriched datasets with atomistic, structural, molecular, and periodic table-based descriptors, along with bibliographic, provenance, and scientific metadata.
• Demonstrated scalability through case studies and enhanced FAIRness via integration with nanodash and Zenodo.

Main Results:

• nanoPharos offers ready-for-modelling NM datasets covering a wide range of properties and assessments.
• The repository successfully captures the multi-scale complexity of NMs.
• Integration with external platforms and adherence to Joint Research Centre maturity indicators demonstrate strong FAIR compliance.

Conclusions:

• nanoPharos provides a scalable solution for managing and utilizing complex nanomaterial data.
• The repository supports the digitization of material R&D, promoting faster innovation and reducing reliance on traditional testing methods.
• Ongoing development aims to further enhance data accessibility and analytical capabilities through ontology integration and advanced APIs.