Although the approach used here to create the dataset and the RDF-generated form has made the information more FAIR, it has limitations to consider. First, the number of papers analyzed is limited when compared to the increasing number of scientific papers on nanotoxicology that are published every year, which was close to 6,000 publications in 2020 . Only 21 papers were investigated as a proof of principle which restricts the scope of the findings. To obtain a more comprehensive understanding of how ENMs and their properties are linked to specific AOPs, additional papers should be included for data extraction. Second, data quality poses a significant challenge in the curation process. The adequacy, completeness, accuracy, reliability, relevance, and variability of the reported data in the literature all contribute to its overall quality. It is crucial to evaluate these parameters before proceeding with data analysis . Different scoring or weighing systems, such as the ToxRTool , the GUIDEnano tool or CRED evaluation methods, can be utilized for qualitative or quantitative data quality assessments. The third limitation relates to the quality of contents in the AOP-Wiki, where the same key events (KEs) often have different identifiers. AOPs and their KEs provide simplified descriptions of biological processes, but inconsistencies can arise due to overlapping or conflicting definitions across different ontologies. Therefore, careful consideration is required when utilizing AOP-Wiki for data analysis. Fourth, limitations regarding data accessibility present a challenge. Not all data dealing with ENMs toxicity testing, especially "no effect" data, are published or easily accessible. Even publishing the chemicals discussed in an article in a standardized manner is rare . Additionally, industry-held data are hardly accessible due to confidentiality. Although efforts are being made to promote open access and adherence to the FAIR guiding principles , depending on the type of data, repositories are not yet fully established and, when available, are not always completed. Finally, the labor-intensive nature of data curation from scientific literature presents difficulties in scaling up the process and covering a large volume of relevant papers. This challenge is not unique to extracting information from journal articles and is also observed in other initiatives focusing on scientific reporting of nanomaterials and characteristics . To overcome this limitation, the scientific community has explored automated methods, such as machine learning and text mining to extract information from unstructured text . These techniques aim to improve efficiency and reduce inconsistencies in data extraction. However, manual curation can also introduce discrepancies in reporting and interpretation due to the involvement of multiple individuals with different interpretations and terminologies. Efforts have been made to mitigate these inconsistencies by addressing synonyms and rephrasings. Overall, the automatic extraction of information about papers remains a task that requires further development and improvement. The NanoSafety RDF offers various applications and potential for future development. It demonstrates its utility through a use-case focused on the physicochemical properties of nanomaterials and their potential to cause DNA damage. The use of SPARQL queries on the developed RDF allows for the exploration of the relationships between nanomaterial properties and biological effects. However, the size of the dataset limits the avail of the conclusions that can be drawn from it. For example in the analysis of the effect of primary size on the ability to activate particular types of MIEs, key information (like dissolution rate, agglomeration) which is not included in the dataset are also required to refine the analysis . The availability of a SPARQL endpoint facilitates the usability of the data, allowing users to interact with it through user-friendly web interfaces or computational means. The presented RDF schema can serve as a starting point for reporting nanomaterials and their properties. Another valuable application is the ability to provide feedback to the AOP-Wiki. By linking KEs and MIEs, gaps in existing AOPs can be identified, guiding additional data and research. Moreover, the identification of KEs associated with nanomaterials, but not explicitly described as MIEs in AOP-Wiki, presents an opportunity to propose new AOPs, highlighting areas where the understanding of nanomaterial adverse effects needs improvement. The NanoSafety RDF also allows for similarity searching, where researchers can explore patterns and correlations between physicochemical and ontological characteristics of nanomaterials. This capability could overcome major challenges in nanomaterial similarity assessment and therefore aids in further research and analysis in the field . In future directions, process automation is a key area of focus. The current manual approach to extracting nanomaterial information from scientific papers hinders scalability. However, advancements in text-mining approaches and APIs can automate or assist in the extraction process, significantly improving efficiency and enabling the extraction of a larger volume of data. Maintenance of the NanoSafety RDF is vital as the literature on nanomaterial characterization and potentially harmful effects continues to expand. Employing more comprehensive literature search and selection methods can ensure the inclusion of a broader range of nanomaterials and their characteristics in the RDF. Expanding the RDF content is another important aspect. Currently, the RDF schema includes only six properties due to the restricted reporting of physicochemical characteristics in scientific papers. To capture a more exhaustive understanding of nanomaterials, it is necessary to expand the range of properties included in the schema, facilitating the exploration of correlations between physicochemical properties and molecular effects. One of the main take-home messages resulting from the described approach is the need for mutual agreement on minimal reporting requirements of physicochemical characteristics of ENMs and a standardized format for these data in future studies. Although it is well known that the physicochemical characteristics of ENMs determine their toxicokinetics and toxicodynamics , many studies on AO of ENMs provide not even basic data on ENMs size, shape, surface charge, surface functionalization or core composition. It is also crucial to consider the changes of ENMs properties such as aggregation, agglomeration, dissolution in different exposure conditions for the successful implementation of computational tools and models in risk assessment. Data reporting using templates and the use of identifiers for ENMs are recommended to improve data consistency. Additionally, comprehensive characterization of ENMs' physicochemical properties is essential for accurate risk assessment in nanotechnological applications .