CANARY: Jenkins Plugin Vulnerability Forecasting — Dataset and Model Artifacts (v0.1.15)

Labeled plugin-month datasets, trained model artifacts, and analysis outputs for CANARY, a public-data-first framework for forecasting security advisories in the Jenkins plugin ecosystem. The master dataset covers 2,053 plugins as monthly observations; each row carries features constructed only from information available at the observation date, with a binary label indicating whether a security advisory was published for the plugin within the following 180 days. Feature families span advisory history, plugin ecosystem metadata, GitHub Archive activity signals, repository governance indicators, and historically anchored Software Heritage repository and revision features. The bundle includes per-family dataset variants used in ablation experiments, the full saved model suite (XGBoost, LightGBM, Random Forest, logistic regression across feature-family configurations and split strategies) with per-model metrics and test predictions, and the analysis result files behind the reported figures and tables. Code, pipeline, and documentation: https://github.com/timmybx/canary (tag v0.1.15). All data derives from public sources. See DATASET_README.md for file inventory, checksums, and reproduction steps.