(Session A) fand parallel zu Session B) und Session C) parallel zu Session D) statt, CM besuchte Session B) und D) )<\/p>\n

A) Data workflows & data pipelines<\/strong><\/p>\n

Using the research data life cycle as a model, we will demonstrate how researchers can use GeRDI services to support their individual data handling strategies.<\/p>\n

We would like to understand if this workflows and APIs can be formulated in a generic way covering a wider range of research disciplines or \u2013 alternatively \u2013 if \"data pipelines\" should model discipline-specific data handling strategies. By thinking together with you about specific use cases we will create design sketches for possible new GeRDI functions.<\/p>\n

B) Extending GeRDI Metadata Schema with disciplinary metadata elements<\/strong><\/p>\n

This session will address disciplinary metadata elements. Participants will discover the current state of the GeRDI Metadata Schema and how it is used in different GeRDI services. In an exemplary mapping demonstration the participants will be exposed to the process of extending the metadata schema. In a discussion we will try to identify high-priority metadata elements from participating communities. Other discussion topics include the usage of controlled vocabularies and the re-use of metadata elements across communities.<\/p>\n

C) How to design: Submit service<\/strong><\/p>\n

During this session we will work together on design the new GeRDI service. We will go through discussing the existing use case, extend and value them. Then will bring our idea to paper discuss them and find the possible solutions for the future implementation. We eager to find out, how such service could be supported through GeRDI, which use cases, are most important and what challenges there are. To get the result we will dive into the different techniques of design thinking method.<\/p>\n

D) Improving data quality in research data infrastructures<\/strong><\/p>\n

This session will address the term data quality and its relevance in research. Participants will explore criteria describing and measuring data quality that datasets can be effectively compared and ranked. Learn common criteria and how the scientific domains differ in their quality requirements for data. In a discussion, we will analyze the incentives to motivate data creators as well as the interventions needed to increase the quality of datasets. In the end, the elaborated measures will be assessed and recommendations for research data infrastructures will be made.<\/p>\n

Zu Session B): high-priority metadata<\/strong><\/p>\n

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