Until recently, no research has ever been done on user attitudes to Afrikaans taboo language. To address this shortcoming, a multidisciplinary research project was initiated to investigate, among others, user attitudes to swearwords. Online single-word surveys (SWSs) for individual swearwords have been posted periodically on the project website.1 Volunteer respondents are recruited through respondent-driven opportunistic sampling and snow-ball sampling via social media. Respondents firstly give their informed consent, and then once-off provide some sociodemographic information. Thereafter, each swearword is judged on at least seven attitudinal dimensions. All data are stored in a relational database, and then extracted to create a single UTF-8 encoded CSV file. The dataset holds great potential for perusal in numerous language-specific (i.e., Afrikaans) sociopragmatic and/or sociolinguistic investigations and applications, as well as for comparative linguistic research and general statistical modelling.

New Cinema History broadened film studies by emphasising the complexity of cinema as a multifaceted phenomenon that includes the socio-economic context in which films were made, circulated, shown and received. As part of the digital turn, the discipline adopted computational methods and created quantitative research data to research this socio-economic context at scale. However, not all datasets created in this context adhere to FAIR principles, decreasing their reusability. By reconciling 14 cinema-related datasets, Cinema Belgica facilitates research into the history of Belgian cinema. This research paper documents and critically reflects on the choices made when selecting, modelling and reconciling information for the Cinema Belgica database.

The Places dataset is still in-process and being added to on a regular basis. It is comprised of complete data management documentation and ethics documentation in English and French, concept notes in English and French, and participant data files that include .csv metadata sheets, .wav audio recordings of interviews, .jpeg photographs of the place of the interview and open ELAN .eaf transcriptions. The participant interview data relies on a narrative inquiry methodology and is collected in one-on-one interviews with researchers on the theme of anglophone expatriation and adaptation to French socio-physical space. These interviews are informal and interactive, aiming for a sharing of experience, since the researchers are, themselves, anglophone expatriates. The aim of this accompanying article is to provide information on the dataset through a concomitant reflection on the research context in France and the affordances of the increasingly powerful TGIR Huma-Num digital humanities infrastructure, as well as the tensions that are prevalent in the Franco-European space between the demands of Open Science and the restrictions of the European General Data Protection Regulations. These affordances and this tension have framed the process that has resulted in the Places data architecture.

Using the reparation movement for Japanese colonial and wartime atrocities as a case study, this paper explores the applicability of graph database management systems to network analysis research and teaching in the field of digital humanities. Since the late 1980s, Asian victims of Japanese colonial and wartime atrocities (such as the infamous “comfort women” system) began to sue the Japanese government and corporations with the help of local and Japanese lawyers and activists. This paper uses the plethora of publicly available court materials produced by the movement to study social networks in this transnational historical justice movement and illustrate how graph database management systems can upgrade traditional network analysis methodologies in digital humanities. By inputting the data about lawsuits and lawyers in the movement into graph database applications like Neo4j and GraphXR, the paper demonstrates the advantages of managing network data in graph database structure in terms of scalability, modifiability, intuitive visibility, query efficiency, and analysis potential over relational database structure, which is the mainstream in network analysis research.

The <em>Corpus of the Epigraphy of the Italian Peninsula in the 1st Millennium BCE</em> (CEIPoM) is a linguistic database which covers the Oscan, Umbrian, Old Sabellic, Messapic and Venetic languages, as well as epigraphic Latin up to 100 BCE. The database is hosted on GitHub and Zenodo, and provides manually annotated linguistic information on all levels of language structure, ranging from phonology to syntax. In providing a high-resolution digital dataset for language varieties that have until now been largely restricted to printed reference works, this corpus opens up new avenues for research into this unique ancient linguistic area.

These two datasets are the first born-digital, normalized, peer-reviewed datasets of Harnack’s classic reconstruction of Marcion’s 'Gospel'. The first consists of human-readable postclassical Greek, the second of lemmatized and morphologically tagged text following the openly licensed BibleWorks Greek Morphology schema. The recent deluge of critical editions of Marcion’s 'Gospel' makes Harnack’s public domain work even more relevant as scholars turn from theology- to text-based approaches to restore Marcion’s 'Gospel' and account for its place in the editorial history of early canonical and non-canonical Gospels. These datasets resource Marcion’s 'Gospel' becoming a major topic of interest in Computational Linguistics research.

In this paper, we discuss how different types of automatic annotation of digitised newspaper articles can be integrated into the iterative questioning of the source material and the creation of research corpora out of a collection of unstructured texts (kept in a structured collection). We annotate a sizeable collection of Swiss press articles (183,270), extracted via the impresso interface^{<a id="xrn1" href="#n1">1</a>} using topic modelling (MALLET)^{<a id="xrn2" href="#n2">2</a>} as well as a naïve Bayes classifier (script by Milan van Lange). The methodological discussion we propose is to explore how text mining can help identify historical discourses that are difficult to query with keywords because of their inherent ambiguity and how to grasp them in a large corpus. We argue that the automated annotations can provide a body of corroborating evidence of the searched discourse, to be used as an intermediary and heuristic analysis step.

The Mapping Manuscript Migrations (MMM) project transformed three separate datasets relating to the history and provenance of medieval and Renaissance manuscripts into a unified knowledge graph. The source databases are: Schoenberg Database of Manuscripts, from the Schoenberg Institute for Manuscript Studies, University of Pennsylvania; Bibale, from the Institut de recherche et d’histoire des textes (IRHT-CNRS, Paris); and Medieval Manuscripts in Oxford Libraries, from the Bodleian Libraries, University of Oxford. The data consist of more than 20 million RDF triples which have been mapped to the MMM Data Model. The model combines classes and properties from CIDOC-CRM and FRBR, together with some specific MMM elements. The Knowledge Graph was created using the MMM data transformation pipeline. The MMM dataset is available from the Zenodo repository, and can be directly deployed on a SPARQL endpoint using a docker recipe. To test and demonstrate its usefulness, the MMM Knowledge Graph is in use in the MMM Semantic Portal: https://mappingmanuscriptmigrations.org.

We publish a dataset containing more than 40’000 manually annotated references from a broad corpus of books and journal articles on the history of Venice. References were considered from both reference lists and footnotes, include primary and secondary sources, in full or abbreviated form. The dataset comprises references from publications from the 19th to the 21st century. References were collected from a newly digitized corpus and manually annotated in all their constituent parts. The dataset is stored on a GitHub repository, persisted in Zenodo, and it is accompanied with code to train parsers in order to extract references from other publications. Two trained Conditional Random Fields models are provided along with their evaluation, in order to act as a baseline for a parsing shared task. No comparable public dataset exists to support the task of reference parsing in the humanities. The dataset is of interest to all working on the domain of reference parsing and citation extraction in the humanities. Funding Statement: The project is supported by the Swiss National Fund, with grants 205121_159961 and P1ELP2_168489.

This dataset, part of the Scissors and Paste Project (https://osf.io/nm2rq), describes instances of reprinting and text reuse (scissors-and-paste journalism) in British newspapers between 1800–1837. It was derived from the 19th-Century British Library Newspapers, Part 1 digitised newspaper collection by using plagiarism detection software to identify instances of substantially similar text. It contains a series of manifests that describe a) instances of shared content b) the likely directionality of copying and c) which instances are evolutionary dead-ends and have no known reprints. It is comprised of 1,824 TSV files, divided into four directories, each representing one month between January 1800 and December 1837.