Digital Gregorian chant scholarship has for decades enjoyed the privilege of a large digital resource cataloguing chant sources: the Cantus ecosystem, with nearly 900,000 chants catalogued across more than 2000 sources. The Cantus Database data model and the Cantus ID mechanism has been adopted by 18 more chant databases, jointly accessible through the Cantus Index interface. However, this data has only been available piecemeal via the individual online user interfaces; computational methods have so far had only a limited opportunity to process these immense resources. To overcome this hurdle, we compiled CantusCorpus v1.0, a dataset that combines everything that was available across the Cantus Index-centered network of databases as of mid-2025, and we have also provided the code for updating the dataset as the databases grow. We then created the lightweight PyCantus library for working with this data. PyCantus decouples the data model from the Cantus codebase and thus allows integration of further chant data sources, which we illustrate with harmonising pilot data from the Corpus Monodicum project. Computational chant research is attractive - and CantusCorpus v1.0 and PyCantus are infrastructures that should make work in this field more transparent, replicable, and accessible to digital humanities practitioners beyond chant scholars themselves.

翻译：数字格里高利圣咏研究数十年来一直得益于一个庞大的数字资源目录，用于编录圣咏来源：Cantus生态系统，已将近90万首圣咏编入2000多个来源中。Cantus数据库数据模型与Cantus ID机制已被另外18个圣咏数据库采用，可通过Cantus Index界面联合访问。然而，这些数据此前仅通过各个在线用户界面零散提供；计算方法迄今为止只有有限的机会处理这些海量资源。为克服这一障碍，我们编译了CantusCorpus v1.0，这是一个整合了截至2025年中旬以Cantus Index为中心的网络数据库所有现有数据的集合，并提供了随着数据库增长而更新数据集的代码。然后，我们创建了轻量级的PyCantus库用于处理这些数据。PyCantus将数据模型从Cantus代码库中解耦，从而允许整合更多圣咏数据源，我们通过来自Corpus Monodicum项目的协调试点数据进行了说明。计算圣咏研究颇具吸引力——而CantusCorpus v1.0和PyCantus是能够使该领域工作对圣咏学者以外的数字人文实践者而言更透明、可复制且可访问的基础设施。