  Data races pose a significant threat in multi-threaded parallel applications
due to their negative impact on program correctness. DataRaceBench, an
open-source benchmark suite, is specifically crafted to assess these data race
detection tools in a systematic and measurable manner. Machine learning
techniques have recently demonstrated considerable potential in
high-performance computing (HPC) program analysis and optimization. However,
these techniques require specialized data formats for training and refinement.
This paper presents the latest update to DataRaceBench, incorporating new data
race contributions from Wu et al. \cite{wu2023model}, and introduces a derived
dataset named DataRaceBench-ML (DRB-ML) \cite{drbml}. DRB-ML aligns with the
emerging trend of machine learning and large language models. Originating from
DataRaceBench, this dataset includes detailed labels that denote the presence
of a data race and provides comprehensive details of associated variables, such
as variable names, line numbers, and the operation (read/write). Unique to
DRB-ML, we have also integrated a series of tailored prompt-response pairs
specifically designed for LLM fine-tuning.
