<p>Smart Contract (SC) vulnerabilities are programming errors or design flaws that can lead to financial loss or functional failure, making accurate detection essential. Although Machine Learning (ML) is widely applied to SC vulnerability detection, existing datasets are often small, imbalanced, inconsistently labeled, or nonstandardized, and frequently rely on limited feature representations that do not account for different contract lifecycle stages, restricting generalization and degrading benchmark reliability. This study introduces DIVE, a multi-label dataset that addresses these structural and feature-level limitations. DIVE includes 22,330 real-world SCs deployed between 2016 and 2024, and spanning major Solidity compiler versions, annotated for eight vulnerability types aligned with the Decentralized Application Security Project (DASP) Top 10 taxonomy. It provides 221 pre-deployment and 176 post-deployment features and employs a standardized multi-tool labeling pipeline based on Power-based voting and post-hoc filtering, which corrected 14.3% false positives in DoS and 24.9% in Time Manipulation. Unlike prior datasets, DIVE offers two lifecycle-specific feature sets and an open-source framework enabling reproducible benchmarking and periodic reconstruction aligned with evolving vulnerability patterns.</p>