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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2026
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| Accès en ligne: | https://arxiv.org/abs/2603.11933 |
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| _version_ | 1866912963341844480 |
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| author | Dvořáková, Anna Eipert, Tim Lacoste, Debra Hajič jr, Jan |
| author_facet | Dvořáková, Anna Eipert, Tim Lacoste, Debra Hajič jr, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11933 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Making Chant Computing Easy: CantusCorpus v1.0 and the PyCantus Library Dvořáková, Anna Eipert, Tim Lacoste, Debra Hajič jr, Jan Digital Libraries 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. |
| title | Making Chant Computing Easy: CantusCorpus v1.0 and the PyCantus Library |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2603.11933 |