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Organisationseinheit der BAM
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.
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.
