Simulations used in SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification. Publication can be found in https://arxiv.org/abs/1901.06384 Supernova light-curves (1,983,213) simulated using SNANA and their SALT2 fit. Seven supernova templates are used: Ia, Ib, Ic, II-n, IIL1, IIL2, II-P. Data is similar to the SPCC data (Kessler et al. 2010). DES light-curves are built from supernova templates and use SALT2 SN Ia SED models (Guy et al. 2007) and the trained model from JLA (Betoule et al. 2014). Observing logs specifying the simulated cadence and conditions were included when available. Data format: SNANA format is structured in the following way: _HEAD.FITS provide the supernova light-curve ID (SNID) and global properties like redshift, coordinates, etc. _PHOT.FITS provides the photometry. It is order in the same way as _HEAD. Separators between light-curves are given by 'MJD'==-777.00 Other things to note: Subtypes can be identified by column SNTYPE in header "101": "Ia", "120": "IIP", "121": "IIn", "122": "IIL1", "123": "IIL2", "132": "Ib", "133": "Ic" To read and reformat to csv you can use SuperNNova or this utility SNANA_FITS_to_pd.py