Speaker
Description
The Alfred Wegener Institute has accumulated a large and heterogeneous body of marine and polar observation data including multi-decadal time series from long-term observatories, passive acoustic recordings, environmental DNA sequencing, and seafloor imagery.
We use AI to turn this data into knowledge. For example, we use detection models to extract different types of whale call events from multi-year passive acoustic recordings, simplifying the analysis of seasonal marine mammal presence. We developed MetaParse to segment seafloor scenes containing many co-occurring, visually entangled species and derive descriptive metadata from image content alone; it is built so that the approach transfers to segmentation problems beyond seafloor imagery. We also built BioCUDA for NMR imaging of marine organisms, which yields stacks of blurred, low-contrast greyscale slices. BioCUDA assembles these into a three-dimensional volume and segments organs and muscle tissue, making morphological change under changing climate conditions measurable.
Each application runs into the same limit: a model is only as good as the data behind it, and FAIR compliance alone does not make data usable for machine learning and AI. We are therefore developing the Data2AI companion, which combines a deterministic quality-control engine, computing a broad set of readiness metrics with an agentic layer that explains the resulting assessment and recommendations on how to act on it.
We also run a set of agents that support submission into the systems that manage our data: a sample management system, a sequence and metadata management system, and a chatbot for documentation.
We present results from MetaParse, Whale Call Detection and BioCUDA as well as the Data2AI companion and other agents.