The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

Partaking in the editorial process of an academic journal is both a challenging and rewarding experience. It takes a village of dedicated individuals with a vested interest in the dissemination and sharing of high-quality research outputs. As members of the editorial team of an open access data journal, we reflect on the emergence of data-driven open research, a new journal genre (data paper), and a new journal type (data journal) in the Arts, Humanities, and Social Sciences (AHSS). Access to data—the currency of empirical research—is valuable to the research community, crucial to scientific integrity, and leads to cumulative advancements in knowledge. It therefore requires significant investment and appropriate venues for dissemination. We illustrate the necessity of raising awareness about data-driven open research and best practices in data-driven publishing. We discuss how it involves building a community of authors and readers, establishing a company of editors, reviewers, and support staff, and passing on the practice, which has been challenging the status quo in research and publishing. Potential future directions are considered, including data peer review and reward, recognition, and funding structures for data sharing.

Large Language Models (LLMs) can appear to generate expert advice on legal matters. However, at closer analysis, some of the advice provided has proven unsound or erroneous. We tested LLMs' performance in the procedural and technical area of insolvency law in which our team has relevant expertise. This paper demonstrates that statistically more accurate results to evaluation questions come from a design which adds a curated knowledge base to produce quality responses when querying LLMs. We evaluated our bot head-to-head on an unseen test set of twelve questions about insolvency law against the unmodified versions of gpt-3.5turbo and gpt-4 with a mark scheme similar to those used in examinations in law schools. On the "unseen test set", the Insolvency Bot based on gpt-3.5-turbo outperformed gpt-3.5-turbo (p = 1.8%), and our gpt-4 based bot outperformed unmodified gpt-4 (p = 0.05%). These promising results can be expanded to cross-jurisdictional queries and be further improved by matching on-point legal information to user queries. Overall, they demonstrate the importance of incorporating trusted knowledge sources into traditional LLMs in answering domain-specific queries.

Traditional philological methods in Roman legal scholarship such as close reading and strict juristic reasoning have analysed law in extraordinary detail. Such methods, however, have paid less attention to the empirical characteristics of legal texts and occasionally projected an abstract framework onto the sources. The paper presents a series of computer-assisted methods to open new frontiers of inquiry. Using a Python coding environment, we have built a relational database of the Latin text of the Digest, a historical sourcebook of Roman law compiled under the order of Emperor Justinian in 533 CE. Subsequently, we investigated the structure of Roman law by automatically clustering the sections of the Digest according to their linguistic profile. Finally, we explored the characteristics of Roman legal language according to the principles and methods of computational distributional semantics. Our research has discovered an empirical structure of Roman law which arises from the sources themselves and complements the dominant scholarly assumption that Roman law rests on abstract structures. By building and comparing Latin word embeddings models, we were also able to detect a semantic split in words with general and legal sense. These investigations point to a practical focus in Roman law which is consistent with the view that ancient law schools were more interested in training lawyers for practice rather than in philosophical neatness.