Datasets:

Sand33p
/

AstroPRISM

license: other
language:
  - en
task_categories:
  - image-to-text
tags:
  - astronomy
  - multimodal
  - spectra
  - private-research-dataset
pretty_name: AstroPRISM image and spectrum caption pairs
configs:
  - config_name: image_caption
    data_files:
      - split: train
        path: data/image_caption/train.parquet
      - split: validation
        path: data/image_caption/validation.parquet
      - split: test
        path: data/image_caption/test.parquet
  - config_name: spectrum_caption
    data_files:
      - split: train
        path: data/spectrum_caption/train.parquet
      - split: validation
        path: data/spectrum_caption/validation.parquet
      - split: test
        path: data/spectrum_caption/test.parquet

AstroPRISM

Private research staging data for aligning PRISM with astronomical images and spectra. The package contains 646 object-level candidates with a fixed, source-group-safe split (516 train / 65 validation / 65 test).

Configs and training status

Config	Splits	Rows	Training status
`image_caption`	train, validation, test	646	Only train rows are allowed; captions are accepted weak supervision.
`spectrum_caption`	train, validation, test	646	All captions are accepted, reviewed, and dataset-ready; only train rows allow gradient training.

Every spectrum-caption row has caption_status=accepted, review_status=accepted, reviewed=true, and dataset_ready=true. Dataset readiness means the pair is approved for its assigned split; it does not grant gradient-training permission. Only train rows have training_allowed=true. Validation and test rows have role=held_out_evaluation and training_allowed=false so they remain clean evaluation supervision.

The embedded image and spectrum_plot columns are inspection previews. Model inputs are the calibrated arrays in assets/raw/images/ and assets/raw/spectra/. Released AION embeddings, download caches, and encoder weights are intentionally excluded.

Direct raw-array indexing

candidate_index and raw_array_index are identical. They index axis 0 of every monolithic NumPy array. This remains true in all Parquet splits; indices are deliberately not renumbered.

from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="Sand33p/AstroPRISM",
    repo_type="dataset",
    filename="assets/raw/images/image_array.npy",
)
images = np.load(path, mmap_mode="r")
image = images[row["candidate_index"]]  # float32 [4, 160, 160]

Load pair metadata with an authenticated Hugging Face session:

from datasets import load_dataset

images = load_dataset("Sand33p/AstroPRISM", "image_caption")
spectra = load_dataset("Sand33p/AstroPRISM", "spectrum_caption")

See metadata/candidates.jsonl for the authoritative cross-modal identity and split mapping, provenance/SOURCES.json for immutable upstream pins, LICENSES.md for source-specific terms, and SHA256SUMS for integrity verification.

Important limitations

Image descriptions are synthetic weak supervision and can contain unsupported interpretation.
Spectrum captions were generated from deterministic measured facts and accepted by user instruction.
Spectrum flux units were not retained explicitly in the extracted local arrays.
Object-level source groups must stay in their assigned split across every derived task.
Raw scientific data retain their original survey terms and required acknowledgements.