Speakers
Description
RADAR, the research data repository developed and operated by FIZ Karlsruhe, supports the secure archiving, publication and long-term availability of research data across disciplinary boundaries. Since going live in 2017, RADAR has been continuously developed to meet the increasing demands of Open Science.
The system offers comprehensive metadata support, persistent identifiers, semantic enrichment (e.g. Schema.org, FAIR Signposting), subject-specific terminologies via TS4NFDI and integrations with platforms such as GitHub, GitLab and WebDAV. Different operating variants (RADAR Cloud, RADAR Local) and subject-specific publication offerings (e.g. RADAR4Chem, RADAR4Memory) ensure a high degree of flexibility and broad connectivity to the needs of the scientific community.
As part of its continuous innovation work, we are currently testing AI-supported functions to further improve FAIR data practices. These include:
- AI-powered metadata enrichment, through automatic extraction of
relevant keywords from existing metadata and documents linked to the
dataset; - AI-based FAIRness checks that provide targeted feedback and
recommendations to optimize the FAIRness of datasets.
These functions are intended to support researchers and curators in meeting the growing demands on metadata quality and data responsibility - and at the same time reduce manual effort.
In the session, we will present our previous approaches and work and discuss them together with the participants. We are particularly looking forward to the exchange with participants who would like to contribute their own experiences in the use of AI in research data management.
