LAYERS Corpus Register: Bibliographic Dataset of Digitized Jesuit Institutional Sources

LAYERS Corpus Register is a curated bibliographic dataset prepared for the ERC Advanced Grant application Hidden Layers: Converso Networks and the Neural Architecture of the Early Jesuits (LAYERS) by Robert Aleksander Maryks (https://orcid.org/0000-0002-8401-4236). The dataset identifies digitized source volumes relevant to the study of early Jesuit institutional communication, governance, archival-secretarial routines, personnel documentation, and converso-related networks. The deposited file is a corpus register rather than a full-text corpus. It records bibliographic and access-level information for digitized printed or manuscript-derived source volumes that may serve as the evidentiary basis for subsequent OCR/HTR processing, TEI alignment, entity extraction, provenance tracking, and historical network analysis. The register is designed to support the construction of a provenance-aware research corpus for modelling the Society of Jesus as a temporally evolving, multi-relational institutional system. The dataset contains the following fields: author — attributed author, editor, or responsible person where applicable; title — full bibliographic title of the source volume; title code — short local identifier used for project-internal reference; number of pages — page count used for estimating corpus scale and processing requirements; URL — link to a publicly accessible digital surrogate or catalogue record. The current version contains 139 records and 115,828 pages. URLs point to external repositories and digital libraries; the dataset does not reproduce the digitized source images themselves. The register is intended as a transparent, citable, and reusable basis for corpus construction, source auditing, and computational historical analysis in Jesuit studies, converso studies, early modern institutional history, and digital humanities. This version should be cited as the stable v1.0.0 corpus register. Future corrections, additions, or transformations into TEI, RDF, graph, or full-text formats should be released as new dataset versions.