When data are scarce, the critical request is, that they must be highly accurate, precise, and well characterized. This is summarized by the request that experimental data must be 'clean', but it is not often achieved in materials science and rarely fulfilled in heterogeneous catalysis. The 'clean-data concept' for experimental studies is described in contribution (3.2) by Trunschke et al. Advancements in obtaining high-quality data from electronic-structure theory are described by Kokott et al. in . The general challenge to find the best-suited AI method for a certain application is severe, and the reproducibility of published AI studies is often problematic. The NOMAD concept is described in contribution . A strategy to overcome the bottleneck of scarce data in deep learning is the augmentation of a small, accurate data set by synthetically generated data. This is discussed by and exemplified by generating synthetic Hamilton Matrices for deep learning applied to multiphotoabsorption. Spatiotemporal models like random fields and Gaussian processes have demonstrated promising outcomes in integrating data from multiple sources and guiding scientific discovery in various disciplines. Contribution by discusses their application to materials science and hints at further directions to be explored to leverage their full potential in materials discovery. When trying to apply machine learning methods that have already proved successful in "hard matter physics" to soft matter, several technical obstacles need to be overcome, including the intrinsic multiscale nature of this part of condensed matter. Bereau and Kremer argue that when this can be achieved, it would usher soft matter in a new era, where poor scale separation can be efficiently addressed, and insight will be gained for phenomena that are currently too complex for traditional methods (contribution 3.7). In contribution , show that significant computational gains can be achieved in the numerical simulation of microstructure continuum mechanics models when traditional direct numerical simulation is replaced by modern deep-learning based methods when the AI models are informed by physical insight. Digitalizing the entire workflow in data-rich imaging techniques in material science from synthesis, sample preparation, data acquisition and post-processing in an integrated way is the topic of contribution (3.9) by Freysoldt et al. There, it is discussed that machine learning techniques can leverage the data science approach by removing the human inspection as the limiting factor to digest larger and larger amounts of data in order to discover relevant, but possibly rare patterns. Recently, large-language models (LLMs) have also entered the field of materials science. Raabe et al. provide an overview and perspective in contribution Section 4 then addresses several applications of data-centric materials science, typically paired with methodological developments. Experimental methods cover photoemission, electron microscopy, and atom-probe tomography. In contribution (4.1), Purcell et al. consider the role of AI in high-throughput materials discovery using computational workflows while Liebscher et al. as well as Schloz et al. discuss the roadmap to AI and ML driven data analytics in scanning transmission electron microscopy (STEM) in contributions (4.2) and (4.3), respectively. Atom probe tomography is another imaging-based technology to analyze the composition of materials at the near-atomic scale. Its enhancement using ML is the topic of contribution (4.4) by In contribution (4.5), Logsdail et al. investigate the potentials of a datadriven approach for heterogeneous catalysis. Finally, in contribution (4.6), Fratzl discusses recent advancements of x-ray scattering and diffraction for materials at the nanoscale with respect to the retrieval and analytics of large amounts of data.