Mechanical and Electrical Properties dataset of Cu-Cr-X alloys generated with automated figure data extraction

Extracting data from scientific literature and constructing machine learning-ready (ML-ready) datasets is crucial for data-driven materials discovery. However, material properties under various compositions and processing conditions are often summarized in figures, making manual data extraction time-consuming and labor-intensive. In this study, we propose an automated figure data extraction tool to rapidly generate datasets from figures in materials science literature on Cu-Cr-X alloys used in high-end lead frames, aiming to support the development of next-generation high-strength, high-conductivity copper alloys. We collected 251 figures containing mechanical and electrical performance data from 146 papers and automatically extracted 3,018 high-quality Cu-Cr-X data records. Each record includes article metadata and composition/processing information manually extracted from text, enabling construction of a domain-specific dataset linking alloy composition, processing, and properties. The dataset summarizes 20 microalloying elements and mechanical and electrical performance under different processing conditions. This tool can significantly improve the efficiency of large-scale data accumulation in materials science.