Motivation

In this IUC a research data management for highly diverse experimental data is developed and established in order to match the special requirements of automated experimental characterization setups. The desired solution should continuously adapt data management workflows. Starting with a relatively limited meta data schema, a continuous adaption over the period of use is foreseen to develop more and more data procedures into dedicated data management workflows. By continuous adaptation and integration of analysis workflows and metadata schemes, the amount and the quality of the meta data improves. A prototype system for such a setup has been developed by the PP01 and will be integrated in the materials data infrastructure of NFDI-MatWerk.

Overview

Main requirements:
- Standardized formats for data and metadata
- Flexible storage of large amounts of data obtained in high-throughput measurements
- Workflow management systems that can be adapted during use
- Combination of automated experimental characterization and automated data analysis
- Device software and electronic lab books
Main Task Area: TA-OMS
Other related Task Areas: TA-MDI, TA-WSD
Possible connections within NFDI: FAIRmat, NFDI4Chem
Material/Data: Combinatorial thin films synthesis and automatized X-ray diffraction data analysis
Main Success Scenario: A toolkit for the automated handling of all metadata during sample characterization is set up. This allows for a high-throughput analysis of structural data for materials design and materials discovery.
Added value for the MatWerk community: Data management for automated experimental characterization techniques; concepts and software solutions for a continuous adaptation of data management workflows in conjunction with a developing metadata schema.

Goals

  • Develop Adaptive Data Management: Create data management workflows that continuously adapt to the needs of automated experimental characterization setups.
  • Standardize Data and Metadata: Implement standardized formats for data and metadata to support high-throughput measurements.
  • Combine Characterization and Analysis: Integrate automated experimental characterization with automated data analysis.
  • Ensure Flexible Data Storage: Provide flexible storage solutions for handling large volumes of data from high-throughput experiments.
  • Implement Device Integration: Develop software and electronic lab books to support device integration and metadata management.

Related Participant Projects

All Infrastructure Use Cases

NFDI-MatWerk
Funded by the German Research Foundation (DFG) under the National Research Data Infrastructure – NFDI 38/1 – 460247524

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