We did our best to ensure the FAIRness of all of the components of the framework. First, all the code is made available under open-source licenses on GitHub. The repositories were preserved using Zenodo, and a DOI is minted for each one of them, which keeps track of versioning. The MIs have nanopublications associated with them with persistent identifiers through the w3id.org organization. The assessment reports themselves are also assigned globally unique identifiers using w3id.org URLs and UUID generated by the NSDRA assessment web application. Moreover, the assessment web page is annotated with schema.org "Review" entity, and software agents can parse the report and extract lists of passed and failed tests along with provenance information about the assessed resource. Adopting such a framework can be advantageous in several ways, including 1) Aiding in data curation efforts like the Nanomaterial Data Curation Initiative (NDCI) and constructing big databases like PubVinas . In such scenarios, identifying the coverage and the content of a dataset in an automated way can increase the efficiency and quickly identify good candidate data sources and categorize them into groups for the next step in the curation process. 2) Data-driven modeling of nanotoxicology and Risk assessment strategies . In those cases, it is of utmost importance to know what variables have been measured in the available data source and guide the selection process to build up the dataset for the modeling approach. For example, one can check for the datasets that have measured the targeted output variable and select only those for supervised learning model development.3) Facilitate integration among databases and datasets , which can be pushed forward by adopting a unified vocabulary to represent metadata. It will help overcome the challenges of using different terminologies and formats to represent metadata. Moreover, it allows integration with data sources from other disciplines using the same metadata format and vocabulary. Also, It helps to combine knowledge to gain better insights and facilitate deeper data analysis. 4) Enhancing and developing new material safety data sheets. Eastlake et al. have shown that 67% of the material safety data sheets (MSDS) obtained in 2010-2011 still provided insufficient data for communicating the potential hazards of engineered nanomaterials. Using the community standards in the nanosafety domain and an interoperable representation format can substantially benefit developing MSDSs. For example, tools can be developed to retrieve machine-readable MSDS released by governments and regulatory bodies to be used by industries in selecting ingredients for their products or by inspectors to check the compliance of industrial products with minimum safety standards. 6) Support quality evaluation and data completeness frameworks and platforms (e.g.: GUIDEnano ). Checking for data quality and completeness is an inevitable step for any risk assessment or nanotoxicity modeling task. Checking for datasets that comply with community standards regarding their measured variables and the ability to do that in an automated way is a clear advantage and a valuable feature to have in such platforms and frameworks.