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Technical University of Munich
Technical University of Munich

NFDI4Ing - Archetyp DORIS

The National Research Data Infrastructure for Engineering Sciences

 

Team                                            Research Focus                                            Links

 

News and events

26. / 27. October 2022 - NFDI4Ing Conference (online): https://nfdi4ing.de/conference/

06. April 2022 - workshop (online): Research Data Management for Data from High Performance Computing (Tier 1) - Best-Practice and Applications

Slides

27. & 28. September 2021 - NFDI4Ing conference (online): Stand und Zukunft des Forschungsdatenmanagements in den Ingenieurwissenschaften

Slides

22. July 2021 - workshop (online, German): Forschungsdatenmanagement für Daten aus High Performance Computing (HPC) - Best-Practice-Beispiele und Anwendungen

Slides & Video

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.

 

NFDI4Ing Archetypes and Task Areas (NFDI4Ing)
NFDI4Ing Archetypes and Task Areas (NFDI4Ing)
Storage demand, performance and complexity of CFD (Selent et al 2019)
Storage demand, performance and complexity of CFD (Selent et al. 2019).
Exemplary data set (Schembera et al. 2019)
Exemplary data set (Schembera et al. 2019).
Object model for engineering metadata (Selent et al 2019)
Object model for engineering metadata (Selent et al. 2019).
Possible new workflows within CFD using FAIR data (LS AER).
Possible new workflows within CFD using FAIR data (LS AER).
DORIS Flowchart (LS AER).
DORIS Flowchart (LS AER).
DORIS Poster (April 2022 / LS AER).
DORIS Poster (April 2022 / LS AER).

Links

Publications

2022

Journal Articles

  • Arndt, Susanne; Farnbacher, Benjamin; Fuhrmans, Marc; Hachinger, Stephan; Hickmann, Johanna; Hoppe, Nils; Horsch, Martin Thomas; Iglezakis, Dorothea; Karmacharya, Ashish; Lanza, Giacomo; Leimer, Sophia; Munke, Johannes; Terzijska, Džulia; Theissen-Lipp, Johannes; Wiljes, Cord: Metadata4Ing: An ontology for describing the generation of research data within a scientific activity. Zendo, 2022 more… BibTeX Full text ( DOI )
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