With the growth of popularity of facial micro-expressions in recent years,\nthe demand for long videos with micro- and macro-expressions remains high.\nExtended from SAMM, a micro-expressions dataset released in 2016, this paper\npresents SAMM Long Videos dataset for spontaneous micro- and macro-expressions\nrecognition and spotting. SAMM Long Videos dataset consists of 147 long videos\nwith 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded\nwith detailed Action Units (AUs). We compare our dataset with Chinese Academy\nof Sciences Macro-Expressions and Micro-Expressions (CAS(ME)2) dataset, which\nis the only available fully annotated dataset with micro- and\nmacro-expressions. Furthermore, we preprocess the long videos using OpenFace,\nwhich includes face alignment and detection of facial AUs. We conduct facial\nexpression spotting using this dataset and compare it with the baseline of MEGC\nIII. Our spotting method outperformed the baseline result with F1-score of\n0.3299.\n