Research Data Management

Research Data Management (RDM) is part of the research process and aims to make the research process as efficient as possible, meeting expectations and requirements of the university, research funders, and industry as well.
Research Data Management Mehrdad Jalali, KIT

Material Science Lab Equipment (MSLE) Metadata

In order to describe the relevant features and properties of a sample and to ensure utmost traceability and correlation, both the specimen and the measurements need to be annotated with meta-data. A descriptive meta-data scheme for electron microscopy is under development. The organizational structure can develop from two starting points: first, the instrumental point of view, where the focus lies on the technical structure. The instruments are sorted into categories according to their basic functions, imaging, spectroscopy, or diffraction. Then the various parts making up one instrument are described in detail. In cases where one such equipment part is used in several instruments, the description does not need to appear multiple times. It is simply described once, and this description then linked in the appropriate place. Secondly, the organization can be approached from the data side. The resulting type of data, for example, images, spectra, diffraction patterns are placed at the beginning, and then the techniques are described which yield the appropriate result. Although approaching the problem from different points of view, both strategies are complementary. A connection between those two descriptive systems enables searching for data or conversely for technical items.

Material Science Lab Equipment (MSLE) Ontology

An ontology is a formal description defined as a knowledge graph, which consists of a set of concepts within a domain together with the relationships between those concepts. Moreover, for further description, it is needed to formally specify components such as individuals (instances of objects), classes, attributes, and relations, as well as restrictions, rules, and axioms.

A large number of lab equipment with different specifications are generally available at KIT. When planning experiments and analysis, interpretation and documentation of the results, creating a suitable ontology is still quite challenging. Selection of appropriate devices, detailed knowledge of the device specifications, the parameters selected during the measurement, and inferring some more hidden knowledge through ontology is an aims of the MSLE ontology.

STREAM Project

This project addresses challenges regarding the creation, quality, and interconnection of research data in materials science and materials engineering. The focus is on ensuring the completeness, coherence, and consistency of material and context data and their cross-portal retrieval and usability. The possibility of validations by standardized tests and data analysis with machine learning methods is another objective of the STREAM project. By demonstrating the project results in several semantically interconnected data portals, the project aims to support research data management processes throughout the entire research cycle. As a result, data can be interconnected, searched, and explored across portals. The sharing and reuse of material data and context information, support throughout the research cycle.

The STREAM project is a BMBF collaboration project between KIT and the Fraunhofer Institute for Mechanics of Materials (IWM), Fritz Haber Institute of the Max Planck Society (FIB), Leibniz Information Center for Technology and Natural Sciences (TIB), Institute for Applied Computer Science (InfAI).