Although literature-derived data on engineered nanomaterials (ENMs) offer significant potential for informatics applications, their integration is constrained by heterogeneity in experimental protocols, material systems, and analytical approaches across studies.
To address these challenges, a comprehensive dataset was compiled from various scientific studies focusing on ENM dissolution. This compilation included data from numerous peer-reviewed articles and reports to ensure wide representation of existing knowledge in the field. The dataset was evaluated using an adapted Total Data Quality (TDQ) framework to systematically assess measurement quality and representational coverage.
The evaluation identified several critical limitations: incomplete reporting of essential parameters such as pH levels, temperatures, and ionic strengths; inconsistent data formats complicating comparisons across different studies; insufficient methodological documentation hindering reproducibility and interpretation; and biases in the types of materials studied, leading to an unbalanced representation of ENM dissolution data.
To enhance transparency and facilitate data reuse, the quality assessment was aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles. This alignment ensured that the dataset could be more easily accessed, integrated, and utilized in future research by adhering to standards for metadata documentation, persistent identifiers, interoperability protocols, and data management practices.
The structured evaluation provided clarity on the dataset’s structure, context, and overall quality, laying a solid foundation for subsequent analysis. The adapted TDQ framework offers systematic guidelines for evaluating and harmonizing literature-derived dissolution data, thereby enhancing their integration and usability in future studies and applications. This approach addresses immediate research needs while contributing to long-term sustainability by fostering consistent and robust data practices within the ENM community.
