Synthetic Engineer\u2013Task Dataset for Covariance\u2013Eigenvector Projection Framework

This dataset contains synthetic engineer and task records generated for the paper \u201cCovariance\u2013Eigenvector Projection Framework for Sprint-based Task Allocation\u201d. It includes JSON-formatted objects for engineers and tasks, along with Python scripts for reproducible data generation.The engineer records capture attributes such as skill, availability index, impact, module exposure, collaboration ability, and efficiency. The task records capture skill requirement, due date (epoch), priority, dependency level, module knowledge, and task type.All data are artificially generated using parameterized random distributions (uniform, normal) to mimic Agile sprint planning scenarios. No real-world or sensitive data are included.The dataset is intended for reproducibility, benchmarking of assignment algorithms (greedy vs. Hungarian), and extension by the research community in workforce planning, Agile software engineering, and interpretable AI.