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Ensuring data quality, completeness, and interoperability is crucial for progressing safety research, Safe-and-Sustainable-by-Design approaches, and regulatory approval of nanoscale and advanced materials. While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles aim to promote data re-use, they do not address data quality, essential for data re-use for advancing sustainable and safe innovation. Effective quality assurance procedures require (meta)data to conform to community-agreed standards. Nanosafety data offer a key reference point for developing best practices in data management for advanced materials, as their large-scale generation coincided with the emergence of dedicated data quality criteria and concepts such as FAIR data. This work highlights frameworks, methodologies, and tools that address the challenges associated with the multidisciplinary nature of nanomaterial safety data. Existing approaches to evaluating the reliability, relevance, and completeness of data are considered in light of their potential for integration into harmonized standards and adaptation to advance material requirements. The goal here is to emphasize the importance of automated tools to reduce manual labor in making (meta)data FAIR, enabling trusted data re-use and fostering safer, more sustainable innovation of advanced materials. Awareness and prioritization of these challenges are critical for building robust data infrastructures.
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