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This article was originally published by slate.com and can be viewed in full here

One way to think about all of this dream content (as Freud called it in the early 20th century) is as data. We dream up endless amounts of information a night that, for the most part, gets completely lost. Of the multiple dreams we can have a night, usually only one or two remain in mind by the next morning. And even then, those dreams can slip from our memories before we even consider writing them down. Because of this, dreaming has been hard to study. There just isn’t the amount of human data available that other scientific fields typically use to advance their theories. That’s left dream science in the dark.

But what if there was a big-data approach to capturing our dreams? What might data mining a collection of people’s dreams for patterns and similarities reveal about the collective human unconscious? And would it help us better understand why we dream in the first place?

One of the first researchers to start collecting and systematically organizing large sets of dream data was psychologist and “dream accountant” Mary Whiton Calkins. In 1893, she gathered hundreds of dreams and treated the set with statistical analysis. She found “that there existed a close connection between dream-life and the waking life.” This challenged the idea at the time that dreams were just “neural nonsense” that meant nothing.

The next big attempt at making an actual database of dreams was in the 1950s, when a group of social scientists tried to build “the largest database of sociological information ever assembled.”

However, because the researchers were inputting the contents of people’s dreams into a now-obsolete storage system known as the Microcard, the project never got off the ground.

Now, Bulkeley is leading the new frontier of dream data collections and analysis with his searchable online archive of thousands of people’s dreams. He and William Domhoff, a cognitive psychologist and torchbearer in big-data dreams with his site Dreambank.net, have brought the 1950s idea into the information age. According to Fast Company, Bulkeley and Domhoff’s keyword algorithms can “derive accurate information about a person’s waking life habits; relationships and emotional status.

Still, thousands of people have already uploaded their dreams in Shadow, which released an alpha build earlier this year and is now working out bugs and data-security measures to launch a beta build before the end of 2015.

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