The impact of digitalized data management on materials systems workflows
The basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state-of-the-art materials research needs to consider...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 August 2023
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| In: |
Advanced functional materials
Year: 2024, Volume: 34, Issue: 20, Pages: 1-12 |
| ISSN: | 1616-3028 |
| DOI: | 10.1002/adfm.202303615 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/adfm.202303615 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202303615 
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| Author Notes: | Frank Tristram, Nicole Jung, Patrick Hodapp, Rasmus R. Schröder, Christof Wöll, and Stefan Bräse |
| Summary: | The basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state-of-the-art materials research needs to consider the integration of research data management (RDM) workflows and, in the end, the implementation of process automation concepts for all parts of the main modules. Here, the state-of-the-art methods of RDM in academia are described and a perspective on the future of digitalized molecular material systems workflows is given. The different elements of an integrated research data management strategy are described, and examples of automated processes are depicted. As such, the use of electronic lab notebooks for comprehensive documentation, the use of data-integration and data-conversion strategies, and the establishment of two platforms that enable the automated synthesis of chemical components for materials and the analysis of materials by electron microscopy, are highlighted. Two examples of beneficial effects of successful RDM strategies are presented, showing a sophisticated tool for data prediction based on machine learning and options for creating community-driven databases by extracting and re-using data from different scientific projects. |
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| Item Description: | Gesehen am 16.10.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1616-3028 |
| DOI: | 10.1002/adfm.202303615 |