Research data management and machine learning in engineering sciences

  • place:

    Achat Plaza Karlsruhe
    Mendelssohnplatz        
    76131 Karlsruhe

  • Zeit:

     19. – 20.11.2019

  • In:

    Karlsruhe

Registration

The registration page for KIT members can be found here.

The registration page for all NON-KIT members can be found here.

A contribution towards expenses of 150,00€ per participant will be charged. Information about booking a hotel stay (contingent reserved until 12.11) will be provided during the registration. Please note that the number of participants is limited.

Programme booklet

The program of the event can be found here.

Research data management and machine learning in engineering sciences

Research data management in the engineering sciences is of increasing essential importance. This applies not only, but especially in the material sciences, as research into new materials is becoming increasingly complex. Storing, exchanging and making available such data sets, including metadata, in a structured way is a great challenge. This is where research data management comes into play, the aim of which is to make digital research data accessible and usable. Closely related to this is the topic of data analysis, especially with regard to very large data sets, which is facilitated by this. A central role is played by the topic of machine learning, which enables data-driven analyses to establish a deeper connection between experiments and simulations.
In the workshop both topics will be discussed, related difficulties and hurdles will be worked out, existing solutions or solutions in progress will be presented and requirements for new or existing solutions will be taken up.


About NFDI4Ing

In 2017, NFDI4Ing was founded as a self-initiated consortium for the development and establishment of a National Research Data Infrastructure (NFDI) for the engineering sciences. The aim is to develop and/or expand services and structures in accordance with the needs of the engineering sciences, which are necessary for a long-term
sustainable work with research data is helpful or necessary.


About FestBatt

The aim of the competence cluster FestBatt is to develop basic knowledge for solid state batteries as well as to understand their function in detail, to describe them scientifically and to develop functional prototypes. In addition to researching the required materials, the production of such batteries also plays an important role. Within the framework of the method platform for theory and data, a research data infrastructure for solid state battery systems is also being created.