¶ 2 Leave a comment on paragraph 2 0 This chapter has covered a lot of ground: we have moved from the relatively simple province of word clouds, to the more sophisticated world of Overview, regular expressions, and hinted at even more advanced techniques that lurk in the shadows. They all share the same goal, however: how to take a lot of information and explore it in ways that a person could not. How can we use our computers – these powerful machines sitting on our desks or laps – for more than just word processing, but tap their computational potential? As we will note in the next chapter, however, a potential pitfall of this is that we – in most of these cases – still needed to know what we were looking for. Data do not ‘speak’ for themselves: they require interpretation, and – as we will see in more depth in the chapter after next – they require conscientious visualization.
¶ 3 Leave a comment on paragraph 3 0 Scholars often learn by reading and sifting: looking through archival boxes, reading literature, not with an eye on a particular research outcome but with a goal to holistically understand the field, approached from a particular perspective (theory). We can do the same with Big Data repositories, trying to get a macroscopic view of the field, through methods including topic modeling and network analysis. In the next chapters, we build on our more targeted investigations here with a full-scale implementation of our historian’s macroscope.