This dataset provides a large-scale, multi-alloy, multimodal collection of spatially resolved microstructural states, local plastic deformation fields, and corresponding macroscopic mechanical properties for metallic materials. It is designed to enable material spatial intelligence, that is, data-driven learning of relationships between heterogeneous microstructure, plastic deformation resolved down to the nanometer scale over large fields of view, and bulk mechanical response. The dataset combines high-resolution microstructure characterization, such as electron backscatter diffraction (EBSD) measurements, with full-field plasticity measurements obtained using high-resolution digital image correlation. These measurements are acquired over large fields of view and multiple deformation states and are systematically paired with macroscopic mechanical properties such as yield strength, hardening behavior, tensile strength, ductility, fatigue strength, and creep strength. Multiple alloys and processing conditions are included, capturing a wide range of chemistry, structure, and microstructural heterogeneity, deformation modes, and property envelopes. The dataset spans alloys with face-centered cubic, body-centered cubic, and hexagonal close-packed crystal structures, and includes both wrought and additively manufactured processing routes. Mechanical properties and deformation fields are captured at both room temperature and elevated temperature. By explicitly linking local microstructural features, localized plastic deformation, and macroscopic mechanical behavior within a unified spatial framework, this dataset provides a foundational resource for developing, benchmarking, and validating machine-learning and physics-informed models aimed at microstructure-aware property prediction, mechanism discovery, and data-driven alloy design.