TY - CONF A1 - Muth, Thilo T1 - Better data quality for man and machine in materials research and testing N2 - Applying data-driven AI systems makes it possible to extract patterns from given data, generate predictions and helps making decisions. Material research and testing holds a plethora of AI-based applications, for example, for the automatized search and synthesis of new materials, the detection of materials defects, or the prediction of process and materials parameters (inverse problems). However, AI algorithms can often only be as good as the training data from which the corresponding models are learned. Therefore, it is also indispensable to develop measures for the standardization and quality assurance of such data. For this purpose, we develop and implement methods from transferring data from various sources into a homogeneous data repository with uniform data descriptions. Through the standardization and corresponding machine-readable interfaces, research data can be made usable and reusable for further data analyses. In addition to the technical implementation of integrative platforms, it is crucial that quality-assured research data management is recognized and implemented as an integral part of daily scientific work. Finally, we provide a vision of how the Federal Institute for Materials Research and Testing can benefit from data-driven AI systems. We discuss early applications and take a peek at future research. T2 - BR50 AI Workshop CY - Online meeting DA - 13.12.2021 KW - Data quality KW - Machine learning KW - Research data management PY - 2021 AN - OPUS4-56681 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -