Motivation

In a laboratory, the same instrument is used to measure different samples. In particular, Electron Microscopy is routinely used at many facilities as a primary or complementary method for materials characterization before or alongside other techniques. In addition to the common FAIR-compliant approach, which emphasizes standardized sample descriptions as a key aspect of data provenance, we propose to extend the focus on the information flow at the facility itself. By describing samples and their treatments following the same structure and adopting shared semantics, the benefits extend beyond the reproducibility of data, and include also the reuse of methods: this approach enables the scientists at the facility to leverage collective experience, contribute custom scripts, and enhance the efficiency of existing workflows.

This Infrastrucure Use Case (IUC20) will focus on providing a description of physical samples prepared for Electron Microscopy measurements, aligning with existing results (e.g., the PRIMA taxonomy, the EM Glossary, the Sample Description Vocabulary) to ensure harmonization. 

Overview

Main requirements:

  • Vocabulary service to store controlled vocabularies
  • Ontology for physical sample description as well as for material and sample processing
  • Provenance workflow linking samples, treatments and processing, and EM measurements 
Main Task Area: TA-SAI, TA-MDI
Other related Task Areas: TA-WLE
Possible connections within NFDI: NFDI4Ing, FAIRmat, NFDI4Chem
Material/Data: Metallic alloys / Physical laboratory samples for Electron Microscopy (EM) measurements 
Main Success Scenario: Users can describe their physical samples using a common structure and semantics. This information can thus be reused at the facility to gain knowledge about materials and to improve the efficiency of the experimental and data analysis workflows.  
Added value for the MatWerk community: The structured and semantic description of physical laboratory samples enables consistent documentation, improving reproducibility and supporting workflow and provenance tracking across facilities. This approach serves as a blueprint for describing other types of samples and measurements within the MSE community, establishing a best practice use case for FAIR sample documentation exemplarily for EM. 

Exemplary Case Studies

  • TA-SAI: This Infrastrucuture Use Case (IUC20) provides an ontology for the description of physical samples, tested in the context of Electron Microscopy, enabling reproducibility and semantic interoperability in experimental workflows. The development and alignment of the vocabulary with existing standards (i.e., the PRIMA taxonomy, EM Glossary initiative) is envisioned. Future extensions may connect this ontology to broader contexts (such as other measurement techniques or computational samples) or narrower ones (such as crystalline defects). Furthermore, by linking sample descriptions with processing and measurement data, the ontology supports comprehensive provenance tracking and facilitates the reuse of procedures across facilities. It also enables the integration of structured metadata with analysis pipelines, ensuring that microscopy data is well-documented and ready for AI-based approaches.
  • TA-MDI: The knowledge representation provided by the ontology should remain closely aligned with users, keeping a balance between their practical needs and a FAIR-compliant structure. This will be achieved with continuous feedback and iterative refinement. From the infrastructure perspective, this IUC provides an exemplary case study of a vocabulary service to store, manage, and access controlled vocabularies.

Goals

  • Establish a standardized, FAIR-compliant structure for the physical description of samples in electron microscopy, focusing on harmonized semantics and information flow at the facility level.
  • Align and connect existing description schemes (e.g., PRIMA taxonomy, EM Glossary, Sample Description Vocabulary) to enable interoperability and reuse across facilities.
  • Enable structured, semantic annotation of samples and workflows to improve reproducibility, provenance tracking, and method reuse.
  • Support integrated provenance tracking from initial sample conditions through preparation, processing, and measurement steps.
  • Facilitate AI-ready metadata and interoperability with automated workflows.

Related Participant Projects

All Infrastructure Use Cases

NFDI-MatWerk
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524.

Sign up for our newsletter

Subscribe to our newsletter for regular updates about materials science topics!

After subscribing, you will receive an email from us with a confirmation link.
Only after clicking this link your registration is completed.