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We introduce a new multivariate data set that utilizes multiple spacecraftcollecting in-situ and remote sensing heliospheric measurements shown to belinked to physical processes responsible for generating solar energeticparticles (SEPs). Using the Geostationary Operational Environmental Satellites(GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998-2013),we identify 252 solar events (flares) that produce SEPs and 17,542 events thatdo not. For each identified event, we acquire the local plasma properties at 1au, such as energetic proton and electron data, upstream solar wind conditions,and the interplanetary magnetic field vector quantities using variousinstruments onboard GOES and the Advanced Composition Explorer (ACE)spacecraft. We also collect remote sensing data from instruments onboard theSolar Dynamic Observatory (SDO), Solar and Heliospheric Observatory (SoHO), andthe Wind solar radio instrument WAVES. The data set is designed to allow forvariations of the inputs and feature sets for machine learning (ML) inheliophysics and has a specific purpose for forecasting the occurrence of SEPevents and their subsequent properties. This paper describes a dataset createdfrom multiple publicly available observation sources that is validated,cleaned, and carefully curated for our machine-learning pipeline. The datasethas been used to drive the newly-developed Multivariate Ensemble of Models forProbabilistic Forecast of Solar Energetic Particles (MEMPSEP; see MEMPSEP I(Chatterjee et al., 2023) and MEMPSEP II (Dayeh et al., 2023) for associatedpapers).

MEMPSEP III. A machine learning-oriented multivariate data set for  forecasting the Occurrence and Properties of Solar Energetic Particle Events  using a Multivariate Ensemble Approach