4. Quality Criteria and Standards


Leadership


The overall aim of this Task Area is to devise and implement a uniform concept assuring the quality of data and metadata. With the following key objectives, both the different requirements in the various domains and the different facets of quality management are taken into account:

  • Consolidation and dissemination of overall and subject-specific recommendations for the collection, storage, processing and documentation of data.
  • Development of method-specific quality criteria for raw data, aggregated data, and metadata.
  • Evaluation and implementation of procedures for scientific quality assurance and reviews of research data.
  • Recommendation of concepts for responsibilities for data quality.
  • Demonstration of the use of quality criteria and qualification procedures for the support of norms and standards.

With regard to research data, the focus is on storage formats in particular. There is a high demand for open binary formats that can be written and read not only by proprietary software and at the same time allow efficient access to large amounts of data. Efforts are being made to ensure that device and software manufacturers also commit to using such open formats. The elaboration of recommendations and the definition of best practices for data acquisition and processing as well as uncertainty analysis is also on the agenda of this task area and closely coordinated with Task Area “FAIR Laboratory” .

Quality criteria for the annotation and documentation of research data by appropriate metadata are worked out in close collaboration with the Task Area “Metadata and Ontologies” . According to the requirements in the different domains, it is envisaged to define application profiles based on established metadata standards and ontologies or those emerging in Task Area “Metadata and Ontologies” .

The implementation of the measures in the various domains and use cases is supported by means of the definition of Domain Data Protocols (DDP) as suggested by the Science Europe association. This finally enables the conception and exemplarily implementation of review systems for data and metadata. The review systems involve machine-aided tools for completeness of bibliographic data, use of metrological services, maturity levels, etc. Journal publishers and funding organisations are intended to be involved into the implementation of such review systems, and responsibility concepts for data quality assurance are to be designed.

The implementation of quality criteria for data and metadata will finally result in the deduction of norms and standards for specific use cases, e.g. DIN standards for data acquisition as a way to force commercial providers of measurement devices and software to avoid proprietary data formats.


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