--- license: mit pretty_name: AstroM3Dataset size_categories: - 10K3: A self-supervised multimodal model for astronomy](https://arxiv.org/abs/2411.08842) - Code Repository: [GitHub: AstroM3](https://github.com/MeriDK/AstroM3/) --- ## Subsets and Seeds AstroM3Dataset is available in different subset sizes: - `full`: Entire dataset - `sub50`: 50% subset - `sub25`: 25% subset - `sub10`: 10% subset Each subset is sampled from the respective train, validation, and test splits of the full dataset. For reproducibility, each subset is provided with different random seeds: - `42`, `66`, `0`, `12`, `123` ## Data Organization The dataset is organized as follows: ``` AstroM3Dataset/ ├── photometry.zip # Contains all photometry light curves ├── utils/ │ ├── parallelzipfile.py # Zip file reader to open photometry.zip ├── spectra/ # Spectra files organized by class │ ├── EA/ │ │ ├── file1.dat │ │ ├── file2.dat │ │ ├── ... │ ├── EW/ │ ├── SR/ │ ├── ... ├── splits/ # Train/val/test splits for each subset and seed │ ├── full/ │ │ ├── 42/ │ │ │ ├── train.csv │ │ │ ├── val.csv │ │ │ ├── test.csv │ │ │ ├── info.json # Contains feature descriptions and preprocessing info │ │ ├── 66/ │ │ ├── 0/ │ │ ├── 12/ │ │ ├── 123/ │ ├── sub10/ │ ├── sub25/ │ ├── sub50/ │── AstroM3Dataset.py # Hugging Face dataset script ``` ## Usage To load the dataset using the Hugging Face `datasets` library: ```python from datasets import load_dataset # Load the default full dataset with seed 42 dataset = load_dataset("MeriDK/AstroM3Dataset", trust_remote_code=True) ``` The default configuration is **full_42** (entire dataset with seed 42). To load a specific subset and seed, use {subset}_{seed} as the name: ```python from datasets import load_dataset # Load the 25% subset sampled using seed 123 dataset = load_dataset("MeriDK/AstroM3Dataset", name="sub25_123", trust_remote_code=True) ``` --- ## Citation If you find this dataset usefull, please cite: ```bibtex @article{rizhko2024astrom, title={AstroM $\^{} 3$: A self-supervised multimodal model for astronomy}, author={Rizhko, Mariia and Bloom, Joshua S}, journal={arXiv preprint arXiv:2411.08842}, year={2024} } ```