  Science is and always has been based on data, but the terms "data-centric"
and the "4th paradigm of" materials research indicate a radical change in how
information is retrieved, handled and research is performed. It signifies a
transformative shift towards managing vast data collections, digital
repositories, and innovative data analytics methods. The integration of
Artificial Intelligence (AI) and its subset Machine Learning (ML), has become
pivotal in addressing all these challenges. This Roadmap on Data-Centric
Materials Science explores fundamental concepts and methodologies, illustrating
diverse applications in electronic-structure theory, soft matter theory,
microstructure research, and experimental techniques like photoemission, atom
probe tomography, and electron microscopy. While the roadmap delves into
specific areas within the broad interdisciplinary field of materials science,
the provided examples elucidate key concepts applicable to a wider range of
topics. The discussed instances offer insights into addressing the multifaceted
challenges encountered in contemporary materials research.
