Short-term seismicity forecasting models are increasingly developed and deployed for Operational Earthquake Forecasting (OEF) by government agencies and research institutions worldwide. To ensure their reliability, these forecasts must be rigorously tested against future observations in a fully prospective manner, allowing researchers to quantify model performance and build confidence in their predictive capabilities. The Collaboratory for the Study of Earthquake Predictability (CSEP) operated twenty-five fully automated M $\geq$ 3.95 seismicity models developed by nine research groups from Italy, California, New Zealand, the United Kingdom, and Japan. Between August 2007 and August 2018, these models produced over 50,000 daily forecasts for California, each specifying expected earthquake rates on a predefined space-magnitude grid over 24-hour periods. In this article, we describe the forecast database, summarize the underlying models, and demonstrate how to access and evaluate the forecasts using the open-source pyCSEP Python toolkit. The forecast data are publicly available through Zenodo, and the pyCSEP software is openly available on GitHub. This unprecedented dataset of fully prospective earthquake forecasts provides a critical benchmark for developing and testing next-generation OEF models, fostering advancements in earthquake predictability research.