Project description

Science fiction can play a key role in the acquisition of knowledge on the usability of new technologies besides providing inspiration for scientists and engineers. The inventors and founders of the tech industry were heavily influenced by science fiction, especially in Silicon Valley. Nowadays, large volumes of unstructured data can be systematically analysed with great efficiency using machine-based data processing workflows.

This project consists of two phases. During the first phase, the metadata of existing science fiction works will be examined to identify patterns and clusters. Particular attention will be paid to titles and classifications. This will yield an initial overview of the holdings. During the second phase, a topic model from the machine learning application field will be developed with the aid of a clearly definable theme so that the method can be tested using science fiction literature. Ageing and health are currently playing a particularly important role in society. The specialist literature contains no significant empirical data which could be examined more closely in this study. This project is initially concerned with the use of a small data set for test purposes. The German National Library's entire science fiction database could then be analysed in another phase outside this project.

The project was proposed and realised by Philipp Köbe.

Duration

November 2023 - February 2024

Contact

DH-Stipendien@dnb.de