Abstract
This article examines how benchmarking digital methods can advance historical research by recovering women’s lives from fragmented and underused archives. It focuses on the Funeral Entries held in the Genealogical Office of the National Library of Ireland, a rich but understudied manuscript collection compiled by the Ulster King of Arms between the late sixteenth and early eighteenth centuries. These records, which document death dates, kinship networks, and social affiliations, contain an unusually high proportion of women for the early modern period (about 38 percent), offering rare insight into gendered experiences of death, family, and memory.
The article emerges from the ERC-funded VOICES project, which develops AI-powered approaches to recover women’s voices and experiences from early modern sources (VOICES Project, 2023; https://voicesproject.ie). As part of this work, we present a benchmarking experiment using the Funeral Entries to assess the ability of Handwritten Text Recognition (HTR) and Named Entity Recognition (NER) models to cope with early modern orthography, multilingual naming practices, and manuscript variability. These experiments not only illuminate the constraints of existing tools but also demonstrate how benchmarking can generate reusable workflows, inform the creation of annotated gold standards, and support the production of FAIR-aligned humanities data.
Rather than offering a completed dataset, the article argues for the historiographical value of iterative benchmarking. We show how evaluating and refining computational methods reframes questions of archival visibility, evidentiary status, and the interpretive potential of genealogical records, positioning the Funeral Entries not merely as instruments of male lineage but as essential sources for recovering early modern women’s social worlds.