National projects

The goal of this BMBF-funded project is to develop new methods for reducing data traffic between compute nodes with distributed memory and storage in file systems on supercomputers. For this purpose, a co-design approach will be used to develop solutions for variable-precision computation, data compression and novel data formats. These solutions will be used to improve GENE, a program used worldwide for the simulation of plasma turbulence, and will be validated using GENE.
MPCDF coordinates the project.
 

Scientific data are a significant raw material of the 21^st century. To exploit their value, a FAIR – Findable, Accessible, Interoperable, and Re-purposable – data infrastructure (DI) is a must. Making data Findable and AI Ready (an alternative interpretation of the acronym) will change the way in which science is done today. For the wider field of condensed-matter physics and the chemical physics of solids, FAIRmat sets out to make this happen. Integrating synthesis, experiment, theory, computations, and applications, it will substantially further the basic physical sciences, reaching out to chemistry, engineering, industry, and society.

 

Base4NFDI presents a unique chance for the German science system. Through broad cooperation of scientific domains and infrastructure-providers we set out to identify and exploit synergies in the scientific data infrastructure.  NFDI-wide basic services will have the potential to serve most or all consortia and thus have a significant impact on the efficiency of the German research community. To this end, Base4NFDI will support services in a three-phase process: their initialization, integration and ramping up for service operation. In Base4NFDI a “service” is understood as a technical-organisational solution, which typically includes storage and computing services, software, processes and workflows, as well as the necessary personnel support for different service desks.
The MPCDF is partner in this DFG-funded project, and specifically contributes to the integration and ramp-up for operation of forthcoming service candidates.

Particles, Universe, NuClei and Hadrons for the NFDI -  PUNCH4NFDI - is the NFDI consortium of particle, astro-, astroparticle, hadron and nuclear physics, representing about 9.000 scientists with a Ph.D. in Germany, from universities, the Max Planck society, the Leibniz Association, and the Helmholtz Association. PUNCH physics addresses the fundamental constituents of matter and their interactions, as well as their role for the development of the largest structures in the universe - stars and galaxies.

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

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.