Harvesting Data from the Nanotoxicology Literature to Support Computational Predictions of Nanomaterial Hazard

<p>This is a poster delivered at the 12th International Conference on Nanosciences & Nanotechnologies (NN15) conference, 7-10 July 2015, Thessaloniki, Greece.</p> <p>Disclaimers:</p> <p>(1) this presentation has not undergone peer review</p> <p>(2) this presentation may report preliminary results which may have been revised in subsequent publications</p> <p>(3) no endorsement by third parties should be inferred</p> <p>Presentation abstract:</p> <p>A growing number of computational models for nanomaterial hazard have been reported in the literature. These models could potentially facilitate safe-by-design approaches within industry. The development of these models requires the availability of suitable experimental data which, in principle, may be retrieved from the peer-reviewed scientific literature. However, various challenges are encountered when trying to develop data collections, based upon the nanotoxicology literature, which are suitable for modelling of nanomaterial hazards. These challenges include the following: (1) data quality considerations; (2) the need for datasets which are sufficiently large yet sufficiently experimentally consistent; (3) the need for data from diverse sources to be consistently organised in an electronic, structured format to facilitate integration and analysis. This presentation summarises work to collect data to support predictive nanotoxicology, carried out within the EU NanoPUZZLES project. This work included a literature survey, development of a data quality assessment scheme, and development of resources to support the creation of datasets based on the ISA-TAB-Nano specification. This latter specification has been proposed as a community standard and is being employed for data exchange purposes by a growing number of organisations. The NanoPUZZLES data collection will be made publicly available in the near future.</p> <p>The research leading to these results has received funding from the European Union 7th Framework Programme [FP7/2007-2013] under grant agreement n° 309837 (NanoPUZZLES project).</p>