The rapid diffusion of Large Language Models (LLMs) is fundamentally changing how Mining Software Repositories (MSR) research is conducted, particularly for studies that rely on unstructured textual artifacts such as commit messages, issue discussions, pull request reviews, and practitioner-generated content. While recent work has demonstrated the potential of LLMs to support classification, summarization, and qualitative analysis tasks, the majority of existing approaches rely on interactive or API-based executions [2, 3, 6]. Such execution models are poorly suited for large-scale empirical MSR studies, where thousands or hundreds of thousands of artifacts must be processed in a controlled, reproducible, and cost-aware manner.

Researchers in the microservices community often resort to demonstrating the impact of their proposed advancements on custom-made microservices projects. This is a possible source of bias that can reduce the trustworthiness of the results. Moreover, it is hard to compare advances in small projects, often developed due to lack of time. It is common across disciplines to recognize benchmarks that mitigate bias and unify the advancements' impact. To facilitate the identification of available open-source microservice projects (OSS-MS), we performed a comprehensive study to identify, curate, and catalog OSS-MS. We started with 389559 projects and filtered them down to 3804 projects that we manually labeled. After manual labeling, our dataset contains 378 projects with three or more microservices and with over 100 commits. We document the projects from many perspectives, including project size, platform, number of contributors, project purpose, and foundation support. This dataset can serve researchers as a roadmap to identify benchmarks, as our dataset can be used to answer questions such as whether the number of services impacts the issue count.