Input files, data vectors and covariance matrices for the MCMC runs with CLOE v2.0.2 - nzTabSPV3.dat: n(z) for the 13 equipopulated redshift bins case- nuiTabSPV3.dat: fiducial values of the nuisance parameters (#bin centre, source number density, galaxy bias, logaritmic slope of the luminosity function, shift in bin centre, variance of the zp - z relation) - Cls_zNLA_PosPos_C00.dat: GCph data vector- Cls_zNLA_PosShear_C00.dat: GGL data vector (in the order GL)- Cls_zNLA_ShearShear_C00.dat: WL data vector - CovMat-3x2pt-Gauss-32Bins.npy: Gauss only 3x2pt covariance matrix - CovMat-3x2pt-GaussSSC-32Bins.npy: Gauss + SSC 3x2pt covariance matrix - CovMat-3x2pt-BNT-Gauss-32Bins.npy: Gauss only 3x2pt covariance matrix, BNT-transformed - CovMat-3x2pt-BNT-GaussSSC-32Bins.npy: Gauss + SSC 3x2pt covariance matrix, BNT-transformed - BNT-matrix.npy: BNT matrix used for transforming the covariance matrices - power_galaxies_EFT_benchmark_z1..fits: GCsp data vector for bin centred on z = 1.0 - power_galaxies_EFT_benchmark_z1.2.fits: GCsp data vector for bin centred on z = 1.2 - power_galaxies_EFT_benchmark_z1.4.fits: GCsp data vector for bin centred on z = 1.4 - power_galaxies_EFT_benchmark_z1.65.fits: GCsp data vector for bin centred on z = 1.65 - cov_power_galaxies_EFT_benchmark_z1..fits: GCsp Gauss only covariance for bin centred on z = 1.0 - cov_power_galaxies_EFT_benchmark_z1.2.fits: GCsp Gauss only covariance for bin centred on z = 1.2 - cov_power_galaxies_EFT_benchmark_z1.4.fits: GCsp Gauss only covariance for bin centred on z = 1.4 - cov_power_galaxies_EFT_benchmark_z1.65.fits: GCsp Gauss only covariance for bin centred on z = 1.65