In this article, we introduced CIME4R, an open-source, interactive tool that allows scientists to explore and understand chemical reaction optimization data. Users can navigate and analyze large and high-dimensional reaction parameter spaces to find well-performing experiment conditions, understand the influence of various parameters on the target objective, and make informed decisions to propose the next set of experiments. For AI-guided reaction optimization, users can explore and understand predictions made by an AI model. Scientists can use this information and their own experience to make informed decisions for their next experiments and therefore aid human-AI collaboration. We carefully designed the tool to accommodate the tasks and challenges that come with reaction optimization data. We tested CIME4R's usefulness in aiding domain experts with (i) understanding and learning from RO campaigns in retrospect, and (ii) human-AI collaborative decision-making with three case studies. We found that domain experts successfully used CIME4R to complete the case studies and produced valuable insights from the datasets. Despite the steep learning curve of the tool, we are confident that scientists benefit from using CIME4R to analyze their chemical RO data and that with the use of CIME4R AI-guided RO can be better integrated.