The expected impacts from NanoCommons, according to the pillar of activity, are summarised as follows:

Joint Research Activities (JRA)

1. 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)
2. integration and interoperability of tools will facilitate benchmarking of tools and models as first step towards validation and regulatory acceptance.
3. new models and tools will enable further enrichment of datasets and support determination of risks from NMs
4. data quality concepts will support generation of highest quality data suitable for modelling and regulatory risk assessment and decision making.
5. integration of data management and capture into experimental workflows will enhance data quality, data availability and data sustainability.

Networking Activities (NA)

1. Community building around best practice in data generation to maximise the modelling capability will lead to reduced need for experimental testing over time
2. 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
3. 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”
4. Demonstration case studies will provide practical and experiential learning on the limitations and applicability of the tools, further driving their uptake and utilisation.
5. 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)

1. 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
2. Integration of data capture into experimental workflows will reduce data-loss and embed knowledge management practice in next generation of nanosafety researchers
3. 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
4. Enhanced capability of researchers in new member states supported via dedicated TA actions / training / calls.