Static Code Analysis (SCA) tools, while invaluable for identifying potential coding problems, functional bugs, or vulnerabilities, often generate an overwhelming number of warnings, many of which are non-actionable. This overload of alerts leads to ``alert fatigue'', a phenomenon where developers become desensitized to warnings, potentially overlooking critical issues and ultimately hindering productivity and code quality. Analyzing these warnings and training machine learning models to identify and filter them requires substantial datasets, which are currently scarce, particularly for Java. This scarcity impedes efforts to improve the accuracy and usability of SCA tools and mitigate the effects of alert fatigue. In this paper, we address this gap by introducing a novel methodology for collecting and categorizing SCA warnings, effectively distinguishing actionable from non-actionable ones. We further leverage this methodology to generate a large-scale dataset of over 1 million entries of Java source code warnings, named NASCAR: (Non-)Actionable Static Code Analysis Reports. To facilitate follow-up research in this domain, we make both the dataset and the tools used to generate it publicly available.