QLKNN7D-edge training set This dataset contains a large-scale run of ~15 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The dataset is in a parameter regime typical of the L-mode near edge (pedestal forming region). QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with QuaLiKiz 2.8.4, which includes numerical improvements increasing the robustness of strongly driven (high gradient) calculations typical of the L-mode near-edge. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4 for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv and the older dataset on Zenodo. For an application example, see Van Mulders et al 2021, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at https://zenodo.org/record/8017522. Neither the QLKNN10D or QLKNN11D datasets include L-mode near-edge parameters. For any learned surrogates developed for QLKNN7D-edge, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation QuaLiKiz/QLKNN input/output variables naming scheme Training, plotting, filtering, and auxiliary tools QuaLiKiz related tools FORTRAN QLKNN implementation with wrapper for Python and MATLAB Weights and biases of 'hyperrectangle style' QLKNN