The infrastructure developed within eNanoMapper project aims to support the data management in the area of nano safety research and to enable an integrated approach for the risk assessment of nanomaterials. To achieve these, eNanoMapper developed an ontology, a data infrastructure and modelling tools with applicability in risk assessment of nanomaterials. By merging the expertise of partners in statistical and data mining tools and in predictive toxicology, biology and nanotechnology research, eNanoMapper developed resources and tools for predicting toxicity of nanomaterials and worked towards improving the standards in risk assessment of nanomaterials. The ontology includes common vocabulary terms used in nanosafety research and aims to provide a clear explanation of nanostructures based on information relating to their characterization, relevant experimental paradigms, biological interactions, safety indications and the integration of data from existing nanotoxicology sources. To support a collaborative safety assessment approach an infrastructure for data management was developed, with a database which includes functionalities for data protection, data sharing, data quality assurance, search and interfaces for different needs and usages, comparability and cross-talk with other databases. Further, a collection of descriptors, computational toxicology models and modelling tools were developed, to enable the use and integration of nanosafety data from various sources. The project provides also a rich source of information and documentation (e.g. tutorials, webinars, publications) to support and guide the users. eNanoMapper is supported by European Commission 7 th Framework Programme for Research and Technological Development Grant (Grant agreement no: 604134). ; Other ; {"references": ["Hastings et al., eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment, Journal of Biomedical Semantics, March 2015, 10.1186/s13326-015-0005-5.", "Jeliazkova et al., The eNanoMapper database for nanomaterial safety information. Beilstein J Nanotechnol. 2015, 27;6:1609-34", "Tsiliki et al., RRegrs: an R package for computer-aided model selection with multiple regression models. Journal of Cheminformatics 2015, 7:46.", "Grafstr̆00f6m et al., Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures. Altern Lab Anim. 2015 Nov;43(5):325-32"]}