Ulrich Kerzel

2papers

2 Papers

DBNov 3, 2025
Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Khalil Rejiba, Sang-Hyeok Lee, Christina Gasper et al.

Defect phase diagrams provide a unified description of crystal defect states for materials design and are central to the scientific objectives of the Collaborative Research Centre (CRC) 1394. Their construction requires the systematic integration of heterogeneous experimental and simulation data across research groups and locations. In this setting, research data management (RDM) is a key enabler of new scientific insight by linking distributed research activities and making complex data reproducible and reusable. To address the challenge of heterogeneous data sources and formats, a comprehensive RDM infrastructure has been established that links experiment, data, and analysis in a seamless workflow. The system combines: (1) a joint electronic laboratory notebook and laboratory information management system, (2) easy-to-use large-object data storage, (3) automatic metadata extraction from heterogeneous and proprietary file formats, (4) interactive provenance graphs for data exploration and reuse, and (5) automated reporting and analysis workflows. The two key technological elements are the openBIS electronic laboratory notebook and laboratory information management system, and a newly developed companion application that extends openBIS with large-scale data handling, automated metadata capture, and federated access to distributed research data. This integrated approach reduces friction in data capture and curation, enabling traceable and reusable datasets that accelerate the construction of defect phase diagrams across institutions.

LGFeb 9, 2020
Cyclic Boosting -- an explainable supervised machine learning algorithm

Felix Wick, Ulrich Kerzel, Michael Feindt

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.