We present a new dataset (currently 1998-2013) that uses observations from multiple spacecraft observing the Sun and the interplanetary space around it. This data is connected to the processes that create solar energetic particles (SEPs). SEP events pose threats to both astronauts and equipment in space. The dataset contains 252 solar events that caused SEPs and 17,542 that did not. For each event, we gather information about the local space environment around the sun, such as energetic protons and electrons, the conditions of the solar wind, the magnetic field, and remote solar imaging data. We use instruments from NOAA's Geo-stationary Operational Environmental Satellites (GOES) and the Advanced Composition Explorer (ACE) spacecraft, as well as data from the Solar Dynamic Observatory (SDO), the Solar and Heliospheric Observatory (SoHO), and the Wind solar radio instrument WAVES. This data set is designed to be used in machine learning, focusing on predicting the occurrence and properties of SEP events. We detail each observation obtained from publicly available sources and the data treatment processes used to validate the reliability and usefulness for machine-learning applications.