{"url":"/dataset/musiccrowd","name":"MusicCrowd","full_name":null,"description_markdown":"A Crowdsourced Multi-Domain Music Dataset of Europeana Collections\r\n\r\nThe dataset is part of the paper entitled Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education presented at the 4th International Conference on Artificial Intelligence in Education Technology.\r\n\r\nThis audio dataset was derived from Europeana collections and includes 854 music tracks annotated across three basic musical domains: Genre, Emotion, and Instrument. It is a valuable resource for Music Information Retrieval (MIR) tasks, such as music audio tagging.","description_withheld":null,"homepage":"","introduced_date":"2023-06-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/employing-crowdsourcing-for-enriching-a-music","title":"Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education","first_author":"Vassilis Lyberatos","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["MusicCrowd"],"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."}