{"url":"/dataset/cosmoflow","name":"CosmoFlow","full_name":null,"description_markdown":"The latest CosmoFlow dataset includes around 10,000 cosmological N-body dark matter simulations. The simulations are run using MUSIC to generate the initial conditions, and are evolved with pyCOLA, a multithreaded Python/Cython N-body code. The output of these simulations is then binned into a 3D histogram of particle counts in a cube of size 512x512x512, which is sampled at 4 different redshifts.","description_withheld":null,"homepage":"https://ml4sci.lbl.gov/datasets-tools","introduced_date":"2018-08-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/cosmoflow-using-deep-learning-to-learn-the","title":"CosmoFlow: Using Deep Learning to Learn the Universe at Scale","first_author":"Amrita Mathuriya","url":null},"license":{"name":"Custom","url":"https://portal.nersc.gov/project/m3363/"},"modalities":[],"tasks":[],"languages":[],"variants":["CosmoFlow"],"data_loaders":[],"num_papers_in_archive":12,"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."}