 Obtaining digitized station data, however, is just the first step. As Edwards (2010: 321) emphasizes, “…if you want global data, you have to make them”. To construct global temperature datasets that are useful for climate research, thousands of station records, amounting to millions of individual observational records, are merged, subjected to quality control, homogenized and transformed to a grid. Records come from multiple sources, and merging aims to avoid redundancies while maximizing comprehensiveness in station coverage (Rennie et al. 2014). Procedures for quality control seek to identify and remove erroneous data. For example, production of the Global Historical Climate Network—Daily (GCHN-Daily) database involves 19 automated quality assurance tests designed to detect duplicate data, climatological outliers and spatial, temporal and internal inconsistencies (Durre et al. 2010). Homogenization attempts to remove jumps and trends in station time series that are due to non-climatic factors, e.g., because an instrument is replaced with a new one, a building is constructed nearby, or the timing of observations changes. Homogenization methods rely on station metadata, when it is available, as well as physical understanding and statistical techniques, such as change-point analysis (Costa &amp; Soares 2009). Finally, for many purposes, it is useful to have datasets that are gridded, providing temperature values at points on a grid, where each point is associated with a spatial region (e.g., 2° latitude × 2° longitude). Transforming from a collection of stations to a grid involves further methodological choices: which stations should influence the value assigned to a given grid point, what to do if the associated region contains no reporting stations over a period, etc. In practice, scientific groups make different methodological choices (Hartmann et al. 2013).