Abstract

Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data, for what purpose) and quality (how the data was generated, using which protocol, with which controls), as part of good research output management, is necessary to maximise the re-use potential and value of the data. The concept of Instance Maps has been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers. While Instance Maps can be created for completed studies or publications, they are most effective when applied at the study design stage to associate the workflow with the nanomaterials, environmental conditions, method descriptions, protocols, biological and computational models to be used and the data flows arising from study execution. Application of the  Instance Map Tool (described herein) to research workflows of increasing complexity is presented to demonstrate its utility, starting from assessment of nanomaterials transformations in complex media, and documentation of nanomaterial synthesis, functionalisation and characterisation, to description of the culturing of ecotoxicity model organisms Daphnia magna and their use in acute and chronic standardised tests for nanomaterials ecotoxicity assessment, and visualisation of complex workflows in human immunotoxicity assessment using cell lines and primary cellular models. These examples showcase the use of the Instance Map approach for coordination of materials and data flows in complex multi-partner collaborative projects, and to support demonstration case studies. Finally, areas for future development of the Instance Map approach and tool are highlighted.

Keywords: Study design; experimental workflow visualization; data collection and quality control; FAIR; nanomaterial life cycle stages; data provenance

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Punz, B.; Brajnik, M.; Dokler, J.; Amos, J. D.; Johnson, L.; Reilly, K.; Papadiamantis, A. G.; Green Etxabe, A.; Walker, L.; Martinez, D. S.; Friedrichs, S.; Weltring, K. M.; Günday-Türeli, N.; Svendsen, C.; Ogilvie Hendren, C.; Wiesner, M. R.; Himly, M.; Lynch, I.; Exner, T. E. Beilstein Arch. 2024, 202426. doi:10.3762/bxiv.2024.26.v1

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