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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.
In typical material oriented research projects numerous types and sources of data need to get connected to form the final outcome. The data-structures have to be described in a manner semantically concise enough so that all participants share the meaning and at the same time flexible enough to allow connecting findings and facts that have not been connected before.
The present contribution describes the approach that is being taken in a public funded research project with two cooperating research institutes. The aim of the data-management solution is to describe and interlink three main entity types: material entities like a test specimen that is under observation, immaterial entities like a fatigue life model and processes that generate and transform the immaterial and material entities.
The shared understanding of what is described by the data is achieved by referring to existing linked data resources. In addition a common thesaurus that is subject to ongoing discussion is used for concepts and terms that need precision beyond publicly available concept descriptions. The data associated with the entities and processes and entities is rather small and well structured. The entities and processes covered by the vocabularies are typical to laboratories performing fatigue testing and microstructure analysis. Through the interlinking of the entities and processes it becomes possible to intuitively understand the knowledge that is accumulating in the database. For example it is easy to trace the history of a creep test specimen starting from the chemical composition that has been analyzed for the heat, continuing with the heat treatment of the material, going into the creep test itself with the associated creep data and finally finding the transmission electron microscopy images that have been taken from the crept sample.
The presented approach is prototypical way to tackle the sustainable management of structured open research data. Open questions remain for example with the management of attributions, the acceptance of an increased effort in data creation and the access control to specific regions of the data. On the other hand the approach sketches what added value can be created, if the community adopts and contributes to the linked data principles.
The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.
The data were generated in the frame of the digitization project Innovationplatform MaterialDigital (PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.
A brief introduction is given into our data collection and organization procedure, and why we have settled on the HDF5-based NeXus format for describing experimental data.
The links between NeXus and the SciCat data catalog is also provided, showing how the NeXus metadata is automatically added as searchable metadata in the catalog.
Introduction
A good laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration). In the MOUSE, we have combined: a) a comprehensive laboratory workflow with b) a heavily modified, highly automated X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, with a well-documented data flow (akin to what is found at the more automated beamlines). With two full-time researchers, the lab collects and interprets thousands of datasets, on hundreds of samples for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
While these numbers do not light a candle to those achieved by our hardworking compatriots at the synchrotron beamlines, the laboratory approach does allow us to continually modify and fine-tune the integral methodology. So for the last three years, we have incorporated e.g. FAIR principles, traceability, automated processing, data curation strategies, as well as a host of good scattering practices into the MOUSE system. We have concomitantly expanded our purview as specialists to include an increased responsibility for the entire scattering aspect of the resultant publications. This ensures full exploitation of the data quality, whilst avoiding common pitfalls.
Talk scope
This talk will present the MOUSE project as implemented to date, and will introduce foreseeable upgrades and changes. These upgrades include better pre-experiment sample scattering predictions to filter projects on the basis of their suitability, exploitation of the measurement database for detecting long-term changes and automated flagging of datasets, extending the measurement range through an Ultra-SAXS module, and enhancing MC fitting with sample scattering simulations for better matching of odd-shaped scatterers.
To ensure a high operational reliability of offshore wind turbines (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring Systems will be an essential part of such concepts. Because of the fact, that during Operation there will be static and dynamic interaction between the components ‘structure’, ‘machinery’ and ‘blades’ it is necessary to develop the monitoring techniques in an overall concept. These monitoring Systems are supposed to be applied for the design and testing as well as for the Operation and maintenance phases. The knowledge of the dynamic behavior of wind turbines is important both for the design and for a safe Operation. The available monitoring data from a period of three years, allow first conclusions on the long-term Operation of such Systems in terms of quality requirements to the instrumentation to the structure and the rotor blades
Following a modular concept an integrated monitoring system has been developed that includes all components of offshore wind turbines (OWEC). Using commercially available measurement equipments of the involved partners this system was tested at the wind turbine Multibrid M5000_2 in Bremerhaven. Additional results of the research project are findings on the use of embedded FBG sensors in rotor blades that allow condition assessments based on local information. Newly developed algorithms for the identification of external loads and damage characteristics of the structural components have been tested in operation. Developed methods for monitoring based structural assessment were used, which will be suitable to optimize structural maintenance works.
The concept and technical details of the implementation of the developed integrated monitoring system within the IMO-WIND project are presented. The tasks of the components of the system and its requirements are described. Selected results from the continuous monitoring during operation of the plant M5000_2 regarding the task design verification and dynamic structural analysis are given.
Wind turbines on offshore sites (OWECs) are subjected to combined loads from wind and waves. These dynamic loads, with a frequency content within the range of the natural frequencies of the structures, cause fatigue-effective stresses in the substructures of wind turbines. Therefore, the examination of natural frequencies is an important part within the design process of wind turbines. The quality of the numerical models for such calculations is of great importance, since the certification guidelines permit only small uncertainties in modal analysis results. The accuracy of the parameters of the numerical model can only be achieved through a comparison of simulation results with corresponding test results. Therefore, it is necessary to measure the dynamic behaviour of all components of the wind turbines simultaneously. This is true not only for the design verification, but also for monitoring the OWECs in operation. The potential of integrated systems for monitoring-based maintenance optimisation should thus be used.