{"url":"/dataset/https-github-com-computational-cognitive","name":"https://github.com/Computational-Cognitive-Musicology-Lab/CoCoPops","full_name":"CoCoPops: The Coordinated Corpus of Popular Musics","description_markdown":"CoCoPops is a meta-corpus of melodic and harmonic transcriptions of popular music. CoCoPops has been developed primarily by Nat Condit-Schultz and Claire Arthur in the Computational and Cognitive Musicology Lab, within the Georgia Tech Center for Music Technology.\r\nThe goal of CoCoPops is to make a large ammount of comparable melodic/harmonic data available in a consistent, standardized format. All CoCoPops files are stored in humdrum format. CoCoPops currently includes of two main sub-corpora, the Billboard subset and the Rolling Stone subset. We plan on continuing to add more as additional datasets and corpora of popular music with melodic transcriptions become available.","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["https://github.com/Computational-Cognitive-Musicology-Lab/CoCoPops"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}