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As a result, data discovery and exploration are necessary, but often time consuming, steps in a data analysis workflow. Data discovery is the process of identifying datasets that may meet an information need. Data exploration is the process of understanding the properties of candidate datasets and the relationships between them. Data discovery and data exploration often go hand in hand and benefit from tool support. This article surveys research areas that can contribute to data discovery and exploration, particularly considering dataset search, data navigation, data annotation and schema inference. For each of these areas, we identify key dimensions that can be used to characterize approaches and the values they can hold, and apply the dimensions to describe and compare prominent results. 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