Applying Metadata Management: Extraction, Mapping & Visualisation

This workshop introduces and demonstrates methods to ease data and metadata management workflows. These can often be challenging in real word scenarios. Oftentimes, a certain amount of manual labour is still involved, making the management of research data a burden for scientists. This workshop demonstrates a different approach, showcasing tools for metadata extraction, mapping, and visualization, greatly lessening this burden. The workshop consists of three separate demos:
1. Semantic Metadata Extractor
Manual metadata annotation is often a very tedious task for researchers, especially if done semantically according to existing ontologies. To facilitate this task, we propose a semantic metadata extraction solution to improve researcher’s productivity and their data quality. With many implemented extractors, it is easily adaptable to various formats and is generally usable by researchers, due to its domain independent nature.
2. Metadata Extraction & Mapping
The Mapping Service is a tool to automatically extract metadata from research images in the materials science domain. It is an easy-to-use solution to extract metadata in a standardized way, conforming to well-defined schemas and corresponding mappings. ... mehrThe use of schemas enables scientists to define which metadata is most important for their specific use case. This greatly improves efficiency and facilitates management of large data sets. The Mapping Service is in active development at KIT in close collaboration with the researchers. Unlike the Semantic Metadata Extractor, it is intended for specific file formats with well-defined schemas, their corresponding mappings, and use-cases. In particular, the example of metadata extraction from Scanning Electron Microscopy/Focused Ion Beam (SEM/FIB) Tomography will be demonstrated.
3. Visual Analyzer of complex metadata of SEM/FIB Tomography TIFF images
The metadata embedded in SEM/FIB Tomography images is a record of the relevant information during the course of the measurement, like the parameters set in the machine or software at acquisition time. Reading all the metadata from even one single dataset and having an overview of all of them in a meaningful way is a time-consuming process: the acquisition dataset taken as an example contains 719 images, corresponding to the number of tomography slices. We developed the Visual Analyzer, a tool written in Python and offered as a Jupyter notebook, that allows for an initial, quick overview of these metadata, facilitating further analyses and the planning of follow-up experiments. This presentation shows how scientists can use this tool to analyse and visualize metadata easily and comprehensively. The tool is still in development and currently limited to SEM images, but it can be easily customized according to the scientist's needs. It will be made publicly available on GitHub in the near future. At the end of the workshop, the participant will get an idea of how metadata extraction, mapping and visualization tools can be used for effective metadata management with real use cases. The participant will also get the opportunity to discuss and ask questions to see how these tools can be applied in their individual use-cases.

Acknowledgement: This work has been supported by NFDI-MatWerk (DFG – n. 460247524),
NFDI4Ing (DFG – n.442146713), NFFA-Europe-Pilot (EU H2020 – n. 101007417), the research program ‘Engineering Digital Futures’ and the Helmholtz Metadata Collaboration (HMC) platform by the Helmholtz Research Association.