We present DIN 19461:2026-06 (E), a German draft national standard that defines categories, terminology, and process requirements for Derived Text Formats (DTFs) created from text documents in natural language. The standard specifies enrichment and information reduction operations, requirements for combining multiple DTFs, and documentation obligations for publication, archiving, and reuse. Its aim is to enable legally compliant sharing and analysis of texts–especially where copyright or data protection prevents distributing originals–while maintaining scientific utility and reproducibility through explicit process and parameter recording. We outline the scope, the key concepts, the four core reduction operations (retain, delete, replace, randomise), together with examples across token-, structure-, and vector-based DTFs, and implications for infrastructures (e.g., ISO 24622-based metadata). Finally, we discuss limitations, open questions (e.g., reconstruction risks with modern ML models), and next steps for adoption and maintenance.

This paper explores the limitations of reconstructing scrambled text within the context of Derived Text Formats (DTFs). While previous research has treated reconstruction as a technical challenge, this study shifts the focus to investigating the causes of reconstruction failure. Through a detailed analysis of outputs generated by language models on non-literary (IMDb reviews) and literary (Gutenberg texts) datasets, several systematic patterns were identified. First, reconstructed texts are generally shorter than the originals, indicating that the generated results are often incomplete. Second, models simplify expressions by omitting specific modifiers, thereby producing more general outputs. Third, high similarity at the string level does not guarantee semantic equivalence, revealing fidelity-related issues in text reconstruction. In literary texts, chunk-based segmentation poses additional challenges; this approach disrupts syntactic and contextual coherence, leading to sentences that are structurally correct but semantically distorted. These findings suggest that reconstruction difficulty is not merely a matter of model performance but also reflects the importance of higher-level textual organization. This study highlights the fundamental limitations of current language models and reframes reconstruction failure as an analytical perspective for understanding how meaning is constructed in text.

Text and Data Mining (TDM) methods are often used in order to analyse large amounts of text for scientific research. If the analysed text is protected by copyright, the use of such TDM methods has copyright implications. The existing copyright exceptions facilitate TDM within a narrow framework which limits the storage, publication and re-use of datasets. This paper examines the legal framework of converting the source text into a derived text format (DTF) which is no longer protected by copyright in order to allow the use of TDM without legal restrictions. First, the creation itself of a DTF is being examined: it entails copyright relevant acts which are covered by the TDM exception. In a second step the copyright status of the created DTF has to be evaluated based on three criteria: the DTF may not contain elements which are an expression of the intellectual creation of the author of the source material, the source material may not be easily reconstructable based on the DTF and the source material may not be recognizable.

Derived Text Formats (DTFs) have been proposed as a solution to enable text and data mining while avoiding copyright infringement. Building on a review of recent empirical studies of DTFs on topic modeling, authorship classification, and sentiment analysis, this paper argues that DTFs should not be treated as static formats, but as variable and task-dependent representations shaped by multiple interacting factors. In response, we propose a multi-dimensional framework that conceptualizes DTFs as configurations within a structured space defined by both internal representation parameters and external constraints. The framework includes four internal representation dimensions—feature level, degree of reduction, transformation strategy, and aggregation level—as well as two external constraining forces: legal requirements and task-specific information needs. By emphasizing the interdependence of these dimensions, the proposed framework provides a systematic way to describe, compare, and design DTFs across different analytical contexts. Therefore, this paper contributes to a more theoretically grounded understanding of DTFs and offers guidance for their responsible and effective use in text and data mining in Digital Humanities.