The diversification of product search aims to offer diverse products to satisfy different user intents. Existing diversified product search approaches mainly relied on datasets sourced from online platforms. However, these datasets often present challenges due to their restricted public access and the absence of manually labeled user intents. Such limitations may lead to irreproducible experimental results and unreliable conclusions, restricting the development of this field. To address these problems, this paper introduces a novel dataset JDivPS for diversified product search. To the best of our knowledge, JDivPS is the first publicly accessible dataset with human-annotated user intents. The dataset is collected from JD.com, a major Chinese e-commerce platform. It includes 10,000 queries, around 1,680,000 unique products, and an average of 10 human-labeled user intents for each query. We have extensively evaluated several diversified ranking models using the JDivPS dataset. The results of these models are recorded and presented, serving as a valuable benchmark for future research. More details about the dataset can be found in https://github.com/DengZhirui/JDivPS.