Filtern
Dokumenttyp
Sprache
- Englisch (4)
Schlagworte
- Advanced materials (2)
- 2D materials (1)
- Characterisation (1)
- Data management (1)
- Data readiness level (1)
- FAIR (1)
- Governance (1)
- Graphene (1)
- Graphene-related 2D materials (GR2M) (1)
- Image analysis (1)
Organisationseinheit der BAM
As part of the European Horizon ACCORDs project, advanced methods are being developed for the image-based characterisation of 2D nanomaterials. Given the complexity of this task, robust nd wellorganised data management is critical to ensuring high-quality outcomes. To support this, we have established a knowledge infrastructure that serves as the central repository for protocols, images and experimental data which are stored in a standardised, harmonised manner and in accordance with the FAIR principles – Findable, Accessible, Interoperable and Reusable and open science. This machine-readable framework enables the systematic and computationally automated correlation of image features with experimental descriptors, facilitating accurate material characterisation and transparent reporting which is all integrated in the ACCORDs KI.
The ACCORDs project, funded through Horizon Europe, is pioneering a novel approach to investigate Graphene Family Materials (GFMs) through image analysis. Our aim is to unveil how these materials might influence health and the environment. To achieve this, we are developing a platform designed for the easy retrieval, access, sharing, and utilisation of GFM data and the coordination between biological and physico-chemical data formats. Integral to this platform is an OMERO-based library for image storage, alongside data collection forms and image analysis tools. Efforts are underway to streamline the process for researchers to upload and disseminate their findings, manage information within a database and navigate the data with ease. Adhering to REMBI guidelines, which set the standard for annotating biological images with metadata, we ensure our data collection is comprehensive and adheres to established best practices. Initially, we are employing thresholding and basic machine learning techniques for image segmentation, laying the groundwork for advanced analysis through deep learning to gain more profound insights. Upon completion, the project will deliver a comprehensive platform facilitating efficient data and image management concerning GFMs. This platform will enable the straightforward discovery and use of protocols and results, all organised in accordance with the FAIR principles – Findable, Accessible, Interoperable, and Reusable.
This initiative is poised to significantly impact materials science, enhancing our comprehension of the safety and environmental implications of 2D materials.
Ensuring data quality, completeness, and interoperability is crucial for progressing safety research, Safe‐and‐Sustainable‐by‐Design approaches, and regulatory approval of nanoscale and advanced materials. While the FAIR (Findable, Accessible, Interoperable, and Re‐usable) principles aim to promote data re‐use, they do not address data quality, essential for data re‐use for advancing sustainable and safe innovation. Effective quality assurance procedures require (meta)data to conform to community‐agreed standards. Nanosafety data offer a key reference point for developing best practices in data management for advanced materials, as their large‐scale generation coincided with the emergence of dedicated data quality criteria and concepts such as FAIR data. This work highlights frameworks, methodologies, and tools that address the challenges associated with the multidisciplinary nature of nanomaterial safety data. Existing approaches to evaluating the reliability, relevance, and completeness of data are considered in light of their potential for integration into harmonized standards and adaptation to advance material requirements. The goal here is to emphasize the importance of automated tools to reduce manual labor in making (meta)data FAIR, enabling trusted data re‐use and fostering safer, more sustainable innovation of advanced materials. Awareness and prioritization of these challenges are critical for building robust data infrastructures.
The obvious benefits derived from the increasing use of engineered nano-, new, and advanced materials and associated products have to be weighed out by a governance process against their possible risks. Differences in risk perception (beliefs about potential harm) among stakeholders, in particular nonscientists, and low transparency of the underlying decision processes can lead to a lack of support and acceptance of nano-, new, and other advanced material enabled products. To integrate scientific outcomes with stakeholders needs, this work develops a new approach comprising a nine-level, stepwise categorization and guidance system entitled “Knowledge, Information, and Data Readiness Levels” (KaRLs), analogous to the NASA Technology Readiness Levels. The KaRL system assesses the type, extent, and usability of the available data, information, and knowledge and integrates the participation of relevant and interested stakeholders in a cocreation/codesign process to improve current risk assessment, communication, and governance. The novelty of the new system is to communicate and share all available and relevant elements on material related risks in a user/stakeholder-friendly, transparent, flexible, and holistic way and so stimulate reflection, awareness, communication, and a deeper understanding that ultimately enables the discursive process that is needed for the sustainable risk governance of new materials.
