{"url":"/dataset/neurit-dataset","name":"NeurIT Dataset","full_name":null,"description_markdown":"NeurIT Dataset is open-sourced for public research usage. It is collected using the customized robotic platform across three buildings. We collect the training, validation, and test-seen sets in Building A, and build the test-seen and test-unseen set in Building B and C. During data collection, the robot moves at varying speeds up to the maximum value (1.5m/s). The dataset contains 110 sequences, totaling around 15 hours of tracking data that corresponds to a travel distance of about 33.7 km. Each sequence of data lasts 6~10 minutes, containing both IMU data (acceleration, gyroscope, magnetometer) and the ground truth trajectory. The ratio of the training set, validation set, test-seen set, and test-unseen set is 15:3:3:4.","description_withheld":null,"homepage":"https://datahub.hku.hk/collections/NeurIT/7086466","introduced_date":"2024-02-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/neurit-pushing-the-limit-of-neural-inertial","title":"NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT","first_author":"Xinzhe Zheng","url":null},"license":null,"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NeurIT Dataset"],"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."}