Abstract text (incl. figure legends and references)
Software is a fundamental tool which is used in virtually all workflows where data of materials characterization experiments with electron microscopes gets transformed into insight and knowledge. The last years have seen an increased interest, awareness, and funding to support the electron microscopy community on a common goal: Namely, data and metadata of electron microscopy experiments should become easier findable, more transparently accessible, and fundamentally more interoperable; so that data can be processed with different tools to improve the reusability and thus value of electron microscopy data [1]. Clear vocabulary, transparently detailed data schemes and software tools are needed to achieve this aim. This is the essential idea behind the FAIR research data stewardship principles [2] and the key reason behind established national funding schemes like the German National Research Data Infrastructure [3], Platform Material Digital [4], and international activities like EOSC [5], and e-DREAM [6].
A large number of workflows are currently developed by scientists using specific software tools locally and running analyses on own computers and high-performance computing resources. Manual data transfer and documentation is still a frequent task. Usually, many file formats and data representations have to be mastered. However, inconsistencies can come with having multiple representations of similar or sometimes even the same type of data. Reference frame conventions or assumptions used are examples which have to be carefully documented and communicated across tools to ensure a correct and valid interpretation of data. For this task, often recent efforts in cloud computing, documenting data via electronic lab notebooks (ELNs), and using research data management systems (RDMS) are expected to support scientists. Clearly, these tools can be useful in that they offer data organization, analysis, and publication services. However, using all these tools without changing culture and and rigorous data documentation, does not necessarily leave us with FAIR data. We will show how we are addressing these topics within the FAIRmat [7] project using NOMAD OASIS as the RDMS [8]. Specifically, this talk pulls focus on two topics relevant for electron microscopy: First, we will show the services which NOMAD offers, usage of ELNs and cloud computing services as examples. We will briefly touch upon the data model that is behind this work [7, 8] and the value of working as a community on interoperable data models [9]. Second, we would like to discuss the specific topic of analyzing diffraction pattern, here with a focus on orientation microscopy. We suggest an approach which can describe the variety of representations of orientation microscopy data. We will show an initial implementation of such an interoperable description using ELNs and NOMAD that can also be used for description of geometrical data like interface network and crystallites.
[1] M. Scheffler et al. Nature 604 (2022) 635
[2] M. Wilkinson et al. Scientific Data 3 (2016) 160018
[3] https://www.nfdi.de
[4] https://www.materialdigital.de
[5] https://eosc-portal.eu
[6] https://e-dream-eu.org
[7] https://fairmat-experimental.github.io/nexus-fairmat-proposal
[8] https://nomad-lab.eu
[9] https://codebase.helmholtz.cloud/em_glossary
[10] https://arxiv.org/abs/2205.13510
The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 460197019.