National projects

National projects and collaborations

Scientific data are a significant raw material of the 21st century. To exploit its value, a proper infrastructure that makes it Findable, Accessible, Interoperable, and Re-purposable – FAIR – is  a must. For the fields of computational and experimental materials science, chemistry, and astronomy, FAIR-DI e.V. sets out to make this happen. Such data infrastructure enables extensive data sharing and collaborations in data-driven sciences, including artificial intelligence, and it expands basic science and engineering. FAIR-DI engages with scientists across generations to promote innovations and further careers, and it reaches out to industry and society.
MPCDF is founding member of the association and supports all Max Planck Institutes involved includig the Fritz Haber Institute and the MPI for Polymer reseach. more
The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data. RDA has a grass-roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects.

MPCDF has been involved in RDA global from the onset. It is a founding and leading member of RDA Deutschland the German chapter of RDA.

RDA global | RDA Europe | RDA Deutschalnd more
The Munich School for Data Science (MUDS) aims at excellent graduates of mathematics, computer science, natural science and engineering, to train the next generation of data scientists at the interface of data science and four different application domain sciences: biomedicine, plasma physics, earth observation, and robotics. These domains are organized in the educational tracks of the research school. MUDS will strengthen domain-driven research by teaching methodological data science skills in an interdisciplinary and application-oriented fashion. Our main path to achieving this is to offer joint projects, each designed by two partners, a domain-specific application partner and a methodological partner, both supervising the PhD student and therefore ensuring methodological as well as application specific education within each of the four tracks.
The MPCDF is a partner of MUDS, in close collaboration with the IPP. more
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