This thesis presents the design, implementation, and evaluation of the Protein Crystallization Data Extraction (PCDE) pipeline, a Python 3-based software tool developed during the Master in Data Management and Curation (MDMC) 2025–2026 at the Research and Technology Institute, Area Science Park, Trieste, Italy. The PCDE pipeline addresses a critical bottleneck in structural biology: the identification of suitable crystallization conditions for proteins of interest, which traditionally relies on lengthy and resource-consuming trial-and-error experimentation. By leveraging the wealth of experimental metadata archived in the RCSB Protein Data Bank (PDB), the tool implements a homology-guided approach to crystallization condition retrieval.