The computations required to generate these corpora were performed at Google using the MapReduce framework for distributed computing (Ref $5). Many computers were used as these computations would take many years on a single ordinary computer. Note that the ability to study the frequency of words or phrases in English over time was our primary focus in this study. As such, we went to significant lengths to ensure the quality of the general English corpora and their date metadata (i.e., Eng-all, Eng-1M, and Eng-Modern-1M). As a result, the accuracy of place- of-publication data in English is not as reliable as the accuracy of date metadata. In addition, the foreign language corpora are affected by issues that were improved and largely eliminated in the English data. For instance, their date metadata is not as accurate. In the case of Hebrew, the metadata for language is an oversimplification: a significant fraction of the earliest texts annotated as Hebrew are in fact hybrids formed from Hebrew and Aramaic, the latter written in Hebrew script. The size of these base corpora is described in Tables S3-S6. III. Culturomic Analyses In this section we describe the computational techniques we use to analyze the historical n-grams corpora. III.0. General Remarks III.0.1 On Corpora. There is significant variation in the quality of the various corpora during various time periods and their suitability for culturomic research. All the corpora are adequate for the uses to which they are put in the paper. In particular, the primary object of study in this paper is the English language from 1800-2000; this corpus during this period is therefore the most carefully curated of the datasets. However, to encourage further research, we are releasing all available datasets - far more data than was used in the paper. We therefore take a moment to describe the factors a culturomic researcher ought to consider before relying on results of new queries not highlighted in the paper. 1) Volume o