We provide 100 samples, each containing 5000 instances, of discrete Bayesian Networks from bnlearn's Bayesian Network Repository. Specifically, the BNs, along with their characteristics, are: NETWORK #NODES #EDGES #PARAMETERS MAX. PARENTS MEAN DEGREE Cancer 5 4 10 2 2.00 Earthquake 5 4 10 2 2.00 Survey 6 6 21 2 2.00 Asia 8 8 18 2 2.00 Sachs 11 17 178 3 3.09 Child 20 25 230 4 2.50 Insurance 27 52 230 4 2.50 Water 32 66 10083 5 4.12 Mildew 35 46 540150 3 2.63 Alarm 37 46 509 5 2.49 Barley 48 84 114005 4 3.50 Hailfinder 56 66 2656 4 2.36 Hepar2 70 123 1453 6 3.51 Win95pts 76 112 574 7 2.95 Pathfinder 109 195 72079 5 3.58 Munin1 186 273 15622 3 2.94 Andes 223 338 1157 6 3.03 Diabetes 413 602 429409 2 2.92 Pigs 441 592 5618 2 2.68 Link 724 1125 14211 3 3.11 Munin2 1003 1244 69431 3 2.48 Munin4 1038 1388 80352 3 2.67 Munin3 1041 1306 71059 3 2.51 Munin 1041 1397 80592 3 2.68 Each dataset is sampled using Python and the bnlearn package. The BN structure is loaded from the .bif file using bif = bnlearn.import_DAG(path), and samples are generated with bnlearn.sampling(bif, n=5000, methodtype='bayes'). Post-processing is then applied to replace the generated numerical values with those categorical specified in the .bif structure file. Additionally, ten extra old database samples of most BNs can be found on OpenML.