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Like many other scientific disciplines, psychological science has felt the impact of the big-data revolution. This impact arises from the meeting of three forces: data availability, data heterogeneity, and data analyzability. In terms of data availability, consider that for decades, researchers relied on the Brown Corpus of about one million words (Kučera & Francis, 1969). Modern resources, in contrast, are larger by six orders of magnitude (e.g., Google’s 1T corpus) and are available in a growing number of languages. About 240 billion photos have been uploaded to Facebook, and Instagram receives over 100 million new photos each day. The largescale digitization of these data has made it possible in principle to analyze and aggregate these resources on a previously unimagined scale. Heterogeneity refers to the availability of different types of data. For example, recent progress in automatic image recognition is owed not just to improvements in algorithms and hardware, but arguably more to the ability to merge large collections of images with linguistic labels (produced by crowdsourced human taggers) that serve as training data to the algorithms. Making use of heterogeneous data sources often depends on their standardization. For example, the ability to combine demographic and grammatical data about thousands of languages led to the finding that languages spoken by more people have simpler morphologies (Lupyan & Dale, 2010). The ability to combine these data types would have been substantially more difficult without the existence of standardized language and country codes that could be used to merge the different data sources. Finally, analyzabilitymust be ensured, for without appropriate tools to process and analyze different types of data, the “data” are merely bytes. Our main goal in assembling this special issue was to highlight work that makes new types of data available or more accessible, that demonstrates creative merging of different types of data, and that describes new techniques that make it possible to draw useful inferences from the data, with a focus on advancing psychological theory. The call for contributions to this volumewas broad, mentioningmining data from online databases and other “naturally occurring datasets” (Goldstone & Lupyan, 2016), the construction of new linguistic corpora, methods for analyzing diverse data sources, and the creation of environments for gamifying data collection (i.e., by making experiments fun, subject enrollment can be increased at no additional cost; Salganik, 2017). We deliberately omitted any restrictions on the number of subjects, stimuli, or observations, lest the concept of “big data” be reduced to “large files” (E.J. Ward, personal communication). We favored contributions that emphasized the impact of the new data sources/ techniques on advancing psychological theory rather than viewing big data as an end in itself. Finally, we tried to maximize the usefulness of any new datasets and analytic techniques, by emphasizing the importance of open data, open experimental materials, and open code for analysis. Below we describe the contributions to this special issue, grouped into three broad clusters: uses of naturalistic or crowdsourced data, methodological advances (with an emphasis on improving data analyzability), and the creation of new data resources. We were pleased that many contributions could be placed in more than one cluster.

Introduction to special issue. Beyond the lab: Using big data to discover principles of cognition