{"url":"/dataset/nottingham","name":"Nottingham","full_name":"Nottingham","description_markdown":"The **Nottingham** Dataset is a collection of 1200 American and British folk songs.\n\nSource: [Rethinking Recurrent Latent Variable Model for Music Composition](https://arxiv.org/abs/1810.03226)\nImage Source: [https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/](https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/)","description_withheld":null,"homepage":"https://ifdo.ca/~seymour/nottingham/nottingham.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Music Modeling","url":"/task/music-modeling","datasets_with_task":"/datasets/task/music-modeling"}],"languages":[],"variants":["Nottingham"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/music-modeling-on-nottingham","task":"Music Modeling","dataset_variant":"Nottingham","rows":8,"metrics":["NLL","Parameters"],"first_row_in_archive_order":{"model":"R-Transformer","paper":"/paper/r-transformer-recurrent-neural-network","metrics":{"NLL":"2.37"},"code_links":[{"title":"DSE-MSU/R-transformer","url":"https://github.com/DSE-MSU/R-transformer"},{"title":"sfox14/butterfly-r-transformer","url":"https://github.com/sfox14/butterfly-r-transformer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/seq-u-net-a-one-dimensional-causal-u-net-for","title":"Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling","date":"2019-11-14","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/r-transformer-recurrent-neural-network","title":"R-Transformer: Recurrent Neural Network Enhanced Transformer","date":"2019-07-12","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-empirical-evaluation-of-generic","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","date":"2018-03-04","rows_on_this_dataset":4,"code_links":35,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":2,"samples_unverified":8,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":18,"samples_ran":4,"samples_unverified":14,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}