 To try to overcome these challenges, climate scientists employ a rich collection of data modeling practices (Edwards 2010; Frigg et al. 2015b). These are practices that take available observations and apply various procedures for quality control, correction, synthesis and transformation. Some of these practices will be highlighted below, in the course of introducing three important types of climate dataset: station-based datasets  (Section 3.1),  reanalyses  (Section 3.2),  and paleoclimate reconstructions  (Section 3.3).  Because of the extensive data modeling involved in their production, these datasets are often referred to as data products. As the discussion below will suggest, an interesting feature of data modeling practices in climate science is that they tend to be dynamic and iterative: data models for nearly the same set of historical observations are further and further refined, to address limitations of previous efforts.