AIMS:
AI for metadata extraction in sequence databases / NFDI4Biodiversity
- Duration:
- 01.03.2026 - 30.04.2027
- Project coordinated by:
- Helmholtz-Zentrum für Umweltforschung (UFZ)
- Contact (IOW):
- Dr. Christiane Hassenrück
- Funding:
- DFG - Deutsche Forschungsgemeinschaft
- Research area:
- Partners:
-
Universität Kassel
Machine-readable, standards-compliant metadata are the foundation for the automated reuse of scientific data. In recent years, petabytes of sequencing data have been generated worldwide and made available through public archives. For biodiversity research, these data hold enormous potential—yet the associated metadata are often minimal and not interoperable. Crucial contextual information is frequently available only in free-text descriptions, making it difficult to access and process automatically. At the same time, advances in AI models open up new possibilities for systematically analyzing such unstructured text and extracting or enriching metadata. Numerous projects are developing corresponding tools, but often without close coordination. This Topic Table aims to foster exchange, create synergies, advance common standards, and address key challenges such as quality assurance, interoperability, and long-term sustainability.