Atmospheric model data: Data Quality, Curation Criteria and DOI Branding



Dr. Angelina Kraft


Dr. Anette Ganske


Federal Ministry of Education and Research (BMBF)


1 June 2019 – 31 May 2022


Data quality and data curation standards established in meteorology and climate research are primarily applied in large internationally coordinated model comparison studies. In this project, these standards are systematically adapted to the specific requirements of two research communities: small model comparison studies and urban climate research. The latter is characterized by high resolution data and does not have an established data standard.

In this project, standards are developed and applied to existing atmospheric model data and evaluated for their universal usability, which will significantly increase the reusability of these data. Based on these adapted standards, an extension of the DataCite metadata schema to domain-specific data will increase the value of DataCite DOI's as a quality feature. A sustainable application of the universal data standard as well as the allocation of subject-specific DataCite DOIs will be ensured by the service offers of the two infrastructure service providers..


The work plan planned for the TIB is divided into two scopes. Firstly, the DataCite DOI metadata scheme will be extended and adapted for a quality assured 'Atmospheric Model Data DOI (AMD DOI) Branding' for the data sets of urban climate research and for small meteorological model comparison studies, so that the re-usability of the data can be optimized. Suitable metadata parameters are selected and described. The discipline-specific adaptations will be harmonized with generic DataCite requirements. Secondly, the development of a discipline-specific vocabulary based on DCAT is planned in order to optimize the machine reusability of the data sets according to the FAIR Data Principles. For the data quality standards developed, the curation processes will then be adapted for operationalization so that the desired DOI branding can be incorporated into the workflows and business processes of the TIB's DOI Services.


  • German Climate Computing Center
  • University of Hamburg
  • University of Leipzig


All results will be published on the homepage of the project.

Back to list