{"url":"/dataset/robo6","name":"RoBo6","full_name":null,"description_markdown":"Dataset contains light curves of 6 rocket body types from Mini Mega Tortora database (MMT)[^1]. The dataset was created to be used as a benchmark for rocket body light curve classification.  \r\nFor more informations follow the original paper: _RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification_[^2]\r\n\r\nClass labels:\r\n- ARIANE 5 R/B\r\n- ATLAS 5 CENTAUR R/B\r\n- CZ-3B R/B\r\n- DELTA 4 R/B\r\n- FALCON 9 R/B\r\n- H-2A R/B\r\n\r\n## Dataset description\r\n\r\n## Usage\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"kyselica/RoBo6\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"})\r\n>>> dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],\r\n        num_rows: 5676\r\n    })\r\n    test: Dataset({\r\n        features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],\r\n        num_rows: 1404\r\n    })\r\n})\r\n```\r\n\r\n- `label` - class name\r\n- `id` - unique identifier of the light curve from MMT\r\n- `part` - part number of the light curve \r\n- `period` - rotational period of the object\r\n- `mag` - relative path to the magnitude values file\r\n- `phase` - relative path to the phase values file\r\n- `time` - relative path to the time values file\r\n\r\nMean and standard deviation of magnitudes are stored in `mean_std.csv` file.\r\n\r\n\r\n### File structure\r\n\r\n- `data` directory contains 5 subdirectories, one for each class. Light curves are stored in file triplets in the following format:\r\n  - `<track_id>_<#part>_mag.csv` - magnitude values\r\n  - `<track_id>_<#part>_time.csv` - time values\r\n  - `<track_id>_<#part>_phase.csv` - phase angle values\r\n\r\n  where `<track_id>` is the unique identifier of the light curve from MMT, `<\\#part>` is the part number of the light curve (some light curves are split into multiple parts).\r\n- `train.csv` and `test.csv` - contains information about the train and test splits (label, id, part, period, mag, phase, time)\r\n- `mean_std.csv` - contains mean and standard deviation for magnitudes, computed over the training set.\r\n\r\n```\r\nMMT Rocket Bodies\r\n├── README.md\r\n├── train.csv\r\n├── test.csv\r\n├── mean_std.csv\r\n├── data\r\n│   ├──  ARIANE 5 R_B\r\n│   │   ├── <track_id>_<\\#part>_mag.csv\r\n│   │   ├── <track_id>_<\\#part>_time.csv\r\n│   │   ├── <track_id>_<\\#part>_phase.csv\r\n│   ├── ATLAS 5 CENTAUR R_B\r\n│   │   ├── ...\r\n│   ├── CZ-3B R_B\r\n│   │   ├── ...\r\n│   ├── DELTA 4 R_B\r\n│   │   ├── ...\r\n│   ├── FALCON 9 R_B\r\n│   │   ├── ...\r\n│   ├── H-2A R_B\r\n│   │   ├── ...\r\n```\r\n\r\n\r\n## Data preprocessing\r\n\r\nTo create data sutable for both CNN and RNN based models, the light curves were preprocessed in the following way:\r\n\r\n1. Split the light curves if the gap between two consecutive measurements is larger than object's rotational period.\r\n2. Split the light curves to have maximum span 1_000 seconds.\r\n3. Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty.\r\n5. Resample the light curves to 10_000 points with step 0.1 seconds.\r\n4. Filter out light curves with less than 100 measurements.\r\n\r\n\r\n\r\n## Citation\r\n[^2]: RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification\r\n\r\n```\r\n@article{kyselica2024robo6,\r\n  title={RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification},\r\n  author={Kyselica, Daniel and {\\v{S}}uppa, Marek and {\\v{S}}ilha, Ji{\\v{r}}{\\'\\i} and {\\v{D}}urikovi{\\v{c}}, Roman},\r\n  journal={arXiv preprint arXiv:2412.00544},\r\n  year={2024}\r\n}\r\n```\r\n\r\n## References\r\n\r\n[^1]: Karpov, S., et al. \"Mini-Mega-TORTORA wide-field monitoring system with sub-second temporal resolution: first year of operation.\" Revista Mexicana de Astronomía y Astrofísica 48 (2016): 91-96.","description_withheld":null,"homepage":"https://huggingface.co/datasets/kyselica/RoBo6","introduced_date":"2024-11-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/robo6-standardized-mmt-light-curve-dataset","title":"RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification","first_author":"Daniel Kyselica","url":null},"license":{"name":"mit","url":"https://mit-license.org/"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["RoBo6"],"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."}