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FAIR nanosafety data for nanoinformatics

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FAIR nanosafety data for nanoinformatics
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10
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CC Attribution 4.0 International:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2023
Production PlaceFrankfurt am Main

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Abstract
The scale and opportunities of nanoinformatics depends on Findable, Accessible, Interoperable and Reusable (FAIR) data. The FAIR guiding principles were designed by the informatics community to harmonize and advance the ability of machines to find and reuse data, while simultaneously supporting human readability. The complexity and wide variety of data derived from nanomaterials and their interactions with their surroundings has brought diverse challenges for harmonized naming, describing, and representation of the data, in order to make it findable. In addition, challenges for accessibility, interoperability and overall reusability involves needs for cultural change in terms of how researchers and other data generators see the ownership of their data, domain-relevant community standards, and solutions for licensing and provenance to avoid legal uncertainties. Here we present lessons learnt and recommendations for advancing implementation of the FAIR guiding principles in nanosafety data management plans. Implementation depends on broad efforts to raise awareness and provide support for widespread adoption of solutions with the overarching long-term aim to avoid unnecessarily extensive and costly disruption of research and other data generating environments. Brief overviews of aspects relating to FAIRification of toxicogenomic data and the benefits of FAIR high-throughput screening data will be provided. Finally, the newly established FAIR Implementation Network (IN), the AdvancedNano IN, which unites diverse stakeholders of FAIR data in a joint action towards implementation of FAIR systems and solutions, will be introduced. Overall, FAIR data provides far-reaching opportunities to advance development and implementation of nanoinformatics built on animal-free New Approach Methodologies for data-driven prediction of nanomaterials interactions in support of various safety assessment strategies, including both regulatory risk assessment and novel Safe and Sustainable by Design approaches performed during technological innovation.
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