Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations

TL;DR

This field report addresses the challenge of managing highly heterogeneous, high-dimensional materials data across multiple labs for CCSS electrocatalysis. It presents a unified data-centric solution comprising a relational RDMS (MatInf), an ontology-based semantic layer (PMDco), and a Knowledge Graph to connect samples, measurements, and workflows. The authors demonstrate AI-ready workflows, including text mining for composition–property trends and active learning to optimize experimental measurements, all anchored in FAIR principles and reproducible data handling. Together, these components enable cross-institutional data sharing and data-driven discovery in CCSS electrocatalysis, offering a scalable blueprint for similar ambitious materials research programs.

Abstract

The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in form of thin-film materials libraries, various materials characterization and simulations techniques are used to explore the materials data space of the problem. Machine learning and artificial intelligence techniques guide its exploration and navigation. The effective use of the combined heterogeneous data requires more than just a simple research data management plan. Consequently, our research data management system maps different data modalities in different formats and resolutions from different labs to the correct spatial locations on physical samples. Besides a graphical user interface, the system can also be accessed through an application programming interface for reproducible data-driven workflows. It is implemented by a combination of a custom research data management system designed around a relational database, an ontology which builds upon materials science-specific ontologies, and the construction of a Knowledge Graph. Along with the technical solutions of research data management system and lessons learned, first use cases are shown which were not possible (or at least much harder to achieve) without it.

Figures (11)

• Figure 1: Approximate resolution ranges of compositional and/or structural Figure \ref{['fig:chem_char_res']} and electrochemical Figure \ref{['fig:electrochem_char_res']} characterization techniques for surfaces employed in CRC1625. Only the compositional/structural techniques also probe to some appreciable level of depth (Y-axis). The electrochemical characterization technique's depth is estimated. Refer to Table \ref{['tab:char_techniques']} for the explanation of the abbreviations.
• Figure 2: Schematic of the architecture of the MatInf Research Data Management System. The system follows a modular three-tier design, comprising application, data access through an API, and storage layers. It integrates RESTful APIs for communication with users and agents, ensuring extensibility through external services such as validation, data extraction, and visualization. From Dudarev2025, licensed under http://creativecommons.org/licenses/by/4.0/.
• Figure 3: Screenshot of graphical user interface's search function with a periodic table for chemical system and additional filtering options for object type, search phrase, property type, name, creator (author), and time range.
• Figure 4: Left: Real space origin of characterization location on materials library (ML), measurement area (MA), micro measurement area (MMA); right: hierarchical, multidimensional and multimodal data structure.
• Figure 5: Sample-centric workflow within the CRC1625 RDMS, showing relationships between planning, synthesis, characterization, and reporting Dudarev2025a.