Understanding biological systems requires resolving their structure and organization across scales, from tissues to individual molecules. Advances in imaging and molecular profiling now generate vast multimodal datasets that capture biological architecture and dynamics with unprecedented fidelity. Unlocking insights from this data demands computational approaches capable of linking observations across spatial, temporal, and molecular dimensions.

At the Chan Zuckerberg Imaging Institute (CZII), we are building the infrastructure, datasets, and community connections to enable this transformation. Our cryo-electron tomography (cryoET) processing pipeline supports high-throughput reconstruction and standardized metadata integration, forming the foundation for reproducible, machine-learning–ready datasets. The CryoET Data Portal (cryoetdataportal.czscience.com) provides open access to raw data, reconstructions, and curated annotations contributed by leading structural biology labs worldwide. Its programmatic API tools support segmentation, particle picking, and model benchmarking, creating a foundation for AI-driven structural discovery.

To catalyze progress in automated molecular identification, the CZ Imaging Institute recently organized a Kaggle challenge inviting participants to develop models for detecting and labeling macromolecular complexes in real-world cryoET data. Building on this success, upcoming challenges organized by the CZI & CZ Biohub Network will extend this approach to datasets spanning different biological scales, from tissue architecture and cellular organization to subcellular and molecular structure.

Together, these efforts form an open, interoperable ecosystem for machine learning in biological imaging. By combining standardized data infrastructure, scalable computation, and community-driven innovation, we aim to bridge the worlds of imaging and AI and accelerate the discovery of life’s organization across all scales.