The expected impacts from NanoCommons, according to the pillar of activity, are summarised as follows:
Joint Research Activities (JRA)
- integration and organisation of disparate datasets and tools will facilitate enhanced querying, robust gap analysis and better data-driven decision making (leading to successful regulation and industry buy-in)
- integration and interoperability of tools will facilitate benchmarking of tools and models as first step towards validation and regulatory acceptance.
- new models and tools will enable further enrichment of datasets and support determination of risks from NMs
- data quality concepts will support generation of highest quality data suitable for modelling and regulatory risk assessment and decision making.
- integration of data management and capture into experimental workflows will enhance data quality, data availability and data sustainability.
Networking Activities (NA)
- Community building around best practice in data generation to maximise the modelling capability will lead to reduced need for experimental testing over time
- Communication around the benchmarked tools will provide clarity to industry and regulators as to which tools to apply for which purpose / question, facilitating uptake and utilisation
- Training on the different tools will support best practice in their application, further enhancing data quality, contributing to the “community acceptance” and leading to “regulatory acceptance”
- Demonstration case studies will provide practical and experiential learning on the limitations and applicability of the tools, further driving their uptake and utilisation.
- Enhanced interoperability and accessibility of data will provide strong basis for development of innovation governance, enabling safety-by-design, public engagement in nano-safety research, data ethics etc.
Transnational Access (TA)
- Enhanced quality of experimental data, and enhanced usefulness of datasets for modelling and regulatory decision-making purposes, through engaging modellers in experimental design stage – e.g. small changes such as including more timepoints/concentrations etc. will lead to datasets that meet modelling requirements
- Integration of data capture into experimental workflows will reduce data-loss and embed knowledge management practice in next generation of nanosafety researchers
- Documentation of tools and their relative advantages and disadvantages, and data requirements, will support the hand-over of routine nanosafety assessment from research labs to Contract research organisations
- Enhanced capability of researchers in new member states supported via dedicated TA actions / training / calls.