Folded and sealed with a dollop of red wax, the will of Catharuçia Savonario lay in Venice’s State Archives for more than six and a half centuries. The document, written in 1351, was never opened. But to physicist Fauzia Albertin, the three-page document—six pages, folded—was the perfect thickness for an experiment.

Albertin, who now works at the Enrico Fermi Research Center in Italy, wanted to read the will without unsealing it. Her approach: X-ray vision. In a 2017 demonstration, Albertin and her team beamed X-rays at the document to photograph the text inside. Then, using algorithms, they digitally peeled apart the six pages to legibly reproduce handwritten words.

They haven’t figured out entirely what the document says. Savonario used an old form of Italian, which their archivist collaborators are still interpreting, says Albertin. (They did decipher part of it: In one passage, Savonario notes that her will is written on high-quality paper, possibly to remind the reader that she was wealthy.) But the technique should help historians study texts without damaging the physical objects themselves. “The only other way to read [the will] is to cut it open,” says Albertin.

Albertin is collaborating on a larger project known as the Time Machine, which aims to create a Google-like search engine spanning 2,000 years of European history. To do this, researchers plan to digitize and organize the archives of Europe’s cities into one database, says Frédéric Kaplan, a computer scientist at the École Polytechnique Fédérale de Lausanne, who leads the Time Machine collaboration. Eventually, Kaplan thinks that historians could scan libraries of closed tomes using Albertin’s X-ray techniques in a mostly automated process. They could then feed those scans to an AI-driven text recognition algorithm their team is developing, which would automatically enter the text into a database.

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In its grandest form, Kaplan envisions a maps function for the Time Machine, where you can zoom in on the street view of a 19th-century Parisian neighborhood, for example. They have high-quality aerial photographs of Paris during that era. To propagate the city further back in time, Kaplan thinks they can use AI, trained with historical urban planning information, to make educated guesses on how the street layouts evolved. Last month, the European Union awarded the team a million euros in seed funding to continue developing these methods, and the Time Machine is one of six scientific projects currently competing for a billion euros of European funding over the next decade.

But the Time Machine won’t just be flashy apps. Its huge database should allow historians to study societal patterns over longer time spans and geographical scales. The project is part of a recent trend in the last few years, where more historians have been trying to use data science to mine new information out of old texts. When historians propose projects for grant funding, “it’s almost a [requirement] that you make a database and do some network analysis,” says historian Johannes Preiser-Kapeller of the Austrian Academy of Sciences.

For example, Hilde De Weerdt, a historian at Leiden University, and her team have built a tool that automatically tags names, places, and times in digitized Chinese and Korean texts. They’ve designed the database so it can link up to map-plotting software, and they can more easily visualize how people and ideas migrate in space and time.

This data-based approach can offer a fresh perspective on the past. Traditionally, historians use narratives to understand the past and focus their study on “big men and big places,” says Preiser-Kapeller. This framework can lead to cherry-picking, where scholars highlight only the cases that support their narrative. Cherry-picking still happens often in historical scholarship, he says.