IUC08 Interactive visual exploration for analyzing correlations in high dimensional materials data spaces

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Main Task Area: TA-WSD
Other related Task Areas: TA-OMS
Possible connections within NFDI: NFDI4Ing, NFDI4Chem and DataPlant
Material/Data: Metallic alloys / microstructural features and mechanical as well as physical properties
Main Success Scenario: Users can identify and validate correlations for highly complex, sparse materials data sets and can predict materials properties based on microstructural features.
Added value for the MatWerk community: Development interactive data and topology analysis tools based on a materials knowledge management system. This will be used to support and validate the materials ontology developed within the NFDI4-MatWerk.

Main requirements

  • Uniform (Meta)data formats to enable a utilization of heterogenous data obtained at different test devices
  • Metadata scheme to ensure comparable information for different test methods
  • Standardized units to facilitate data transformation
  • Electronic lab book for uniform data documentation and management
  • Integration of measurement devices to simplify utilization of different test devices
  • Visualization tools for interactive correlation analysis

Related Participant Projects


The mechanical properties obtained with a variety of testing methods (e.g. tensile and fatigue testing, (cyclic) indentation, etc.), microstructural features (e.g. grain size, phase contents, etc.) and physical properties (e.g. electrical conductivity, magnetic permeability, etc.) provide huge opportunities for exploring correlations. Interactive visual data exploration and topological data analysis are promising to find and evaluate correlations within materials datasets. The major challenges for data analysis are the lack of standardization on the data input, the lack of helpful guidelines for applying existing data analysis/visualization packages, and the complex nature of the data that is commonly not directly supported by these libraries. To address these challenges, the following support is required: (i) Standard data formats have to be defined and described by an ontology and the transformation from arbitrary data to this format has to be elaborated. (ii) Correlation analysis techniques need to be revisited and systematized along. (iii) Best practices need to be defined and tested as well as augmented with novel analysis techniques.