Data rescue refers to systematic methods for identifying, retrieving, and preserving at-risk scientific data so they remain accessible, interpretable, and reusable. Methodologically, it involves surveying legacy or vulnerable datasets (e.g., on obsolete media or in proprietary formats), prioritizing them via risk and value assessment, and applying controlled workflows for physical stabilization, high-fidelity digitization, bit-level preservation, format migration, and comprehensive metadata capture. Data rescue protocols emphasize provenance documentation, quality control, recovery of contextual information (e.g., codebooks, instrument logs), and integration into trusted repositories using community standards, thereby mitigating data loss and enabling subsequent analysis, replication, and synthesis.