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.

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.

Derived Text Formats (DTFs) are the result of a strategic transformation of textual materials that are protected by copyright in their original form, such that the resulting data is useful for computational analyses and can be openly shared following best practices of Open Science without infringing copyright law. This paper aims to provide insights into several key aspects of this concept that is closely related to concepts such as corpus masking, non-consumptive research and extracted features. The paper establishes the motivation for using DTFs, discusses several foundational aspects of the concept and practice, describes ongoing research on issues including copyright, reconstructibility, evaluation and standardization of DTFs, and concludes with a roadmap for future work on DTFs. In this way, this paper provides a broad but concise overview of work on DTFs as a contribution to Open Science practices, with a focus on work in the Digital Humanities.

We are presenting LODinG – Linked Open Data in the Humanities (abbreviated from Linked Open Data in den Geisteswissenschaften), a recently launched research initiative exploring the intersection of Linked Open Data (LOD) and a range of areas of work within the Humanities. We focus on effective methods of collecting, modeling, linking, releasing and analyzing machine-readable information relevant to (digital) humanities research in the form of LOD. LODinG combines the sources and methods of digital humanities, general and computational linguistics, digital lexicography, German and Romance philology, translatology, cultural and literary studies, media studies, information science and law to explore and expand the potential of the LOD paradigm for such a diverse and multidisciplinary field. The project’s primary objectives are to improve the methods of extracting, modeling and analyzing multilingual data in the LOD paradigm; to demonstrate the application of the linguistic LOD to various methods and domains within and beyond the humanities; and to develop a modular, cross-domain data model for the humanities.

In this paper we present the wikification of the ELTeC (European Literary Text Collection), developed within the COST Action “Distant Reading for European Literary History” (CA16204). ELTeC is a multilingual corpus of novels written in the time period 1840—1920, built to apply distant reading methods and tools to explore the European literary history. We present the pipeline that led to the production of the linked dataset, the novels’ metadata retrieval and named entity recognition, transformation, mapping and Wikidata population, followed by named entity linking and export to NIF (NLP Interchange Format). The speeding up of the process of data preparation and import to Wikidata is presented on the use case of seven sub-collections of ELTeC (English, Portuguese, French, Slovenian, German, Hungarian and Serbian). Our goal was to automate the process of preparing and importing information, so OpenRefine and QuickStatements were chosen as the best options. The paper also includes examples of SPARQL queries for retrieval of authors, novel titles, publication places and other metadata with different visualisation options as well as statistical overviews.