NFDI4Ing - Archetyp DORIS

The National Research Data Infrastructure for Engineering Sciences

 

Team                                            Research Focus                                            Links

 

Team

Group leader: apl. Prof. Dr.-Ing. habil. Christian Stemmer

Team:

Partner:

 

Research Focus

NFDI and NFDI4Ing

The National Research Data Infrastructure (NFDI) has the objective to systematically index, edit, interconnect and make data from science and research available. A central goal is to establish a research data management in accordance with the FAIR principles:

  • Findable
  • Accessible
  • Interoperable
  • Reusable

NFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR.

 

Archetyp DORIS: High-performance measurement and computation with very large data

NFDI4Ing has taken on the task of structuring the individual needs in engineering research data management. A broad consensus on typical methods and workflows in engineering research has been established and different archetypes are harmonisising the methodological needs. The Chair of Aerodynamics and Fluid Mechanics is in charge of the archetype DORIS: High-performance measurement and computation.

The main goal is making HPC research data findable, accessible, interoperable, reusable. Due to the high storage demands (hundreds of TB or even PB) data are currently immobile and too large to be copied to work stations. The (post-) processing generally is done on HPC systems. Individual and custom fit software solutions that can be transferred to other systems are developed and tested within DORIS.

The main measures are:

  1. Accessibility and access rights, data security and souvereignty.
  2. Support for third-party users and community-based training, provision of post-processing algorithms and modules.
  3. Metadata definitions and terminologies, support to data-generating groups.
  4. Storage and archive for very larga data.
  5. Reproducibility on large-scale high-performance systems.

 

Links