Ginevra Bellini
- @ginevrabellini
- | Dignissim sed
Artificial Intelligence Meets Biodiversity Science: Mining Museum Labels
Artificial intelligence (AI) methods hold great potential for the digitization and data extraction of natural history collections, as well as for generating collection-based, FAIR research data. Realizing this potential requires robust technical workflows, interdisciplinary expertise, and appropriate funding structures.
As part of the pilot project KIEBIDS โ a collaboration between the Museum fรผr Naturkunde Berlin and the ECO AI LAB (โKI-Ideenwerkstatt fรผr Umweltschutzโ), an initiative of the German Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety (BMUKN) โ an open-source framework was developed to extract biodiversity-relevant information from collection labels. The goal is to facilitate the use of historical data resources in biodiversity research and environmental protection.
The webinar will present the project and its key outcomes. Short talks will provide context on data extraction from natural history collections and collection-based biodiversity research. Participants are invited to discuss requirements, application areas, and future perspectives for AI-driven extraction of environmentally relevant information from historical documents.
Programme overview
- Welcome and introduction of the project
- Brief presentation of the ECO AI LAB
- Expert input from the museum, AI development and collection management
- Project presentation: The KIEBIDS framework in detail
- Open discussion on perspectives, challenges and potentials
- Dialogue with participants
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