{"url":"/dataset/mulms-az","name":"MuLMS","full_name":"Multi-Layer Materials Science","description_markdown":"The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas: \"Electrolysis\", \"Graphene\", \"Polymer Electrolyte Fuel Cell (PEMFC)\", \"Solid Oxide Fuel Cell (SOFC)\", \"Polymers\", \"Semiconductors\" and \"Steel\". It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks: measurement frame detection, NER, relation extraction, and argumentative zones classifications.","description_withheld":null,"homepage":"https://huggingface.co/datasets/timo-pierre-schrader/MuLMS","introduced_date":"2023-10-24","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MuLMS"],"data_loaders":[{"repo":"https://github.com/boschresearch/mulms-wiesp2023","url":"https://github.com/boschresearch/mulms-wiesp2023","frameworks":[]}],"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."}