• Lowering entry barriers for research: Through coordinated engagement of researchers and funding bodies, the proposed collaborative platform lowers entry barriers by providing standardized datasets, benchmarked models, and reproducible pipelines. This facilitates its adoption by pig scientists while offering the AI community a domain-specific testbed for developing and validating vision-centric models tailored to pigs. • Enabling reproducible and cumulative scientific progress: Collaborative data contributions and standardized modeling frameworks allow training on large, multi-modal datasets. This supports reproducibility and comparability, fosters cumulative progress, and paves the way for robust, pig-specific foundation models. • Providing a scalable and transferable blueprint: Although focused on pigs, the proposed framework is transferable to other species facing similar challenges related to data fragmentation, annotation costs, and model generalizability.