A methodology for the automatic evaluation of Data Quality and Completeness of nanomaterials for Risk Assessment purposes

A methodology to assess the completeness and quality of physicochemical and hazard datasets is proposed to support more robust risk assessment of nanomaterials, including similarity assessment and grouping of different nanoforms. The goal of this approach, developed in the EU projects GRACIOUS and Gov4Nano, is to assess data quality in such a way that all the steps are automatized, thus reducing as much as possible the need of expert judgment. The evaluation starts from available (meta)data as provided in the data input templates of the eNanoMapper databasethat were developed in EU projects such as NANoREG and GRACIOUS. The methodology is implemented in the templates as a traffic light system – the providers of the data can see in real time the completeness scores calculated by the system for their datasets (as traffic lights – green, yellow, or red). This feature is intended as feedback to motivate data providers inserting data into the database to deliver more complete and higher-quality datasets. The users of the data can also see this information both in the data entry templates and on the database interface, which enables them to select better datasets for their risk assessments. The proposed methodology has been partially implemented in the GRACIOUS eNanoMapper database and in a Weight of Evidence approach for the regulatory classification of nanomaterials. It has been fully implemented in a publicly available online R tool.

BASEI Gianpietro;  RAUSCHER Hubert;  JELIAZKOVA Nina;  HRISTOZOV Danail; 

2022-06-16

TAYLOR & FRANCIS LTD

JRC128589

1743-5390 (online),   

https://doi.org/10.1080/17435390.2022.2065222,    https://publications.jrc.ec.europa.eu/repository/handle/JRC128589,   

10.1080/17435390.2022.2065222 (online),