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We introduce MANTRA, an annotated dataset of 4869 transient and 71207non-transient object lightcurves built from the Catalina Real Time TransientSurvey. We provide public access to this dataset as a plain text file tofacilitate standardized quantitative comparison of astronomical transient eventrecognition algorithms. Some of the classes included in the dataset are:supernovae, cataclysmic variables, active galactic nuclei, high proper motionstars, blazars and flares. As an example of the tasks that can be performed onthe dataset we experiment with multiple data pre-processing methods, featureselection techniques and popular machine learning algorithms (Support VectorMachines, Random Forests and Neural Networks). We assess quantitativeperformance in two classification tasks: binary (transient/non-transient) andeight-class classification. The best performing algorithm in both tasks is theRandom Forest Classifier. It achieves an F1-score of 96.25% in the binaryclassification and 52.79% in the eight-class classification. For theeight-class classification, non-transients ( 96.83% ) is the class with thehighest F1-score, while the lowest corresponds to high-proper-motion stars (16.79% ); for supernovae it achieves a value of 54.57% , close to the averageacross classes. The next release of MANTRA includes images and benchmarks withdeep learning models.

MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition