# Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
## 4 Perspectives  
According to our analysis, the NR, eNanoMapper, and PubVINAS databases contain more nanomaterials, as well as structure characterizations and physical chemical properties, and are useful in designing new nanomaterials and studying physicochemical properties of nanomaterials. Regarding biological experiments and relevant protocols, caNanoLab, eNanoMapper, and PubVINAS include more data, which provide rich information for risk assessment of nanomaterials and safety evaluation of nanomaterial-containing products. Undoubtedly, increasing data abundancy in the nano-field has driven the effort in database development within the scientific community. Publicly accessible databases are key resources for learning and retrieving field-specific knowledge. Additionally, databases may promote the development of modern computational nanotechnology, such as nano-informatics modeling studies that target rational nanomaterial design. However, the sizes of current nanomaterial databases are relatively small compared to the abundance of data generated in the nanoscience field, with only a few thousand entries at best as we can see from Table 1. This phenomenon reflects the inefficiency of data sharing after data generation in different laboratories, showing more efforts are required in the assistance of data collection and deposition into public databases. Therefore, more user-friendly tools should be provided by each database to promote data sharing. In addition, literature data mining would also be a good way to collect and analyze nano-related data. For example, a meta-analysis approach that employed decision trees with feature selection algorithms was developed to assemble and generalize the published nanoparticle cytotoxicity data [55]. Similar works by combining data mining and machine learning algorithms to predict the cytotoxicity of nanoparticles were also published [56; 57; 58; 59; 60]. Disadvantages to data mining also exist. For example, there is strong dependence on the historical data and the quality of the knowledge obtained through data mining. Thus, solving issues like the inconsistencies coming from different data resources would be of great significance. Unlike PubChem (pubchem.ncbi.nlm.nih.gov, accessed on 16 February 2021) and PDB (www.rcsb.org, accessed on 16 February 2021), which are two big, well-structured databases in the fields of chemistry and biology, to date there is no comparable nanomaterial database. In the PubChem database, information such as physicochemical properties, structural annotation, and available bioactivities of chemicals are provided [61]. PDB provides 3D structures for a large number of biological macromolecules [62]. To fill gaps in the nanomaterial databases, one of the necessary steps is to provide nanostructure annotation. Furthermore, data completeness and compliance should be evaluated by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **caNanoLab** & **eNanoMapper** & **NR** & **NBIK** & **NIL** & **NKB** & **PubVINAS** \\ \hline Browse & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Search & Yes & Yes & Yes & Yes & Yes & Yes & \\ \hline Filter & Yes & Yes & Yes & Yes & & Yes & Yes \\ \hline Export & Yes & Yes & Yes & Yes & & Yes & Yes \\ \hline Upload & Yes & Yes & & & & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Nanomaterials in the seven databases. setting the proper annotation and deposition standards. In short, large nanomaterial databases such as PubChem and PDB or specific databases such as EADB [63] are needed to facilitate nanoscience research. Data quality is the heart of science, and many efforts have been made to ensure and improve data quality in other fields such as genetics [64; 65], genomics [66; 67; 68], and food science [69].