<anchor xmlns="http://www.tei-c.org/ns/1.0" xml:id="Hlk118913819" />Due to copyright law, Text and Data Mining (TDM) with copyrighted texts faces a lot of restrictions in terms of storage, publication and follow-up use of the resulting corpora, which, however, is against the spirit of open data in digital humanities (DH). As a solution to the problem, the concept of derived text formats (DTFs) has been suggested and discussed (see e.g., <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Lin et al. 2012</hi>, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Bhattacharyya et al. 2015</hi>, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Jett et al. 2020</hi>, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Schöch et al. 2020</hi>). In DTFs, although some information (primarily copyright-relevant features) has been removed from the texts, the texts can still be used for various relevant TDM tasks in DH, such as authorship attribution or topic modeling. <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Schöch et al. (2020)</hi> also provides a very detailed examination of several DTFs from the perspectives of Computational Literary Studies, Computer Science, memory institutions and law. DTFs are extremely meaningful for the DH community, because they match the spirit of open data and make it possible for researchers and libraries to provide more text data for DH research. It also supports the pursuit of open science by encouraging researchers to publish their research data without worrying about violating copyright laws.