Datasets:

gineven
/

GeneralScience-MLLM-22K

language:
  - en
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa
pretty_name: GeneralScience-MLLM-22K
size_categories:
  - 10K<n<100K
tags:
  - science
  - multiple-choice-qa
  - multimodal
  - image-text
  - education
  - jsonl
license: other
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: test
        path: test.jsonl

GeneralScience-MLLM-22K

Dataset Summary

GeneralScience-MLLM-22K is a unified general-science multiple-choice QA collection built from local snapshots of SciQ, AI2 ARC, and ScienceQA. It follows the same release style as a subject-specific MLLM dataset: every sample is stored as one JSONL record, text-only and image-text examples share one schema, and ScienceQA images are exported as standalone files referenced by relative paths.

The release contains 22,661 examples:

train.jsonl: 17,880 examples
test.jsonl: 4,781 held-out examples
images/scienceqa/: 351 exported ScienceQA images

The upstream test splits are kept as held-out test data. Upstream train and validation splits are merged into train.jsonl.

Data Sources

Source	Subset	Count	Modality	License note
`allenai/sciq`	`null`	13,679	text	CC BY-NC 3.0, from local HF dataset card
`allenai/ai2_arc`	`ARC-Challenge`	2,581	text	CC BY-SA 4.0, from local HF dataset card
`allenai/ai2_arc`	`ARC-Easy`	5,180	text	CC BY-SA 4.0, from local HF dataset card
`derek-thomas/ScienceQA`	`null`	1,221	text / image-text	License metadata was not included in the local snapshot; verify upstream before public redistribution

OpenBookQA exists in the local workspace but is not included in this main release because its license was not confirmed in the local snapshot.

File Structure

general_science_release/
  README.md
  general_science_card.md
  stats.json
  train.jsonl
  test.jsonl
  images/
    scienceqa/
      *.png
  scripts/
    build_general_science_release.py
    validate_general_science_release.py

Data Format

Each line in train.jsonl and test.jsonl is one JSON object:

{
  "id": "scienceqa-train-09049",
  "dataset": "derek-thomas/ScienceQA",
  "subset": null,
  "split": "train",
  "task_type": "multiple_choice_science_qa",
  "modality": "image_text",
  "question": "What is the probability that a rainbow trout produced by this cross will be homozygous dominant for the body color gene?",
  "image": {
    "path": "images/scienceqa/scienceqa-train-09049.png",
    "mime_type": "image/png"
  },
  "choices": [
    {"label": "A", "text": "1/4"},
    {"label": "B", "text": "2/4"},
    {"label": "C", "text": "3/4"},
    {"label": "D", "text": "0/4"},
    {"label": "E", "text": "4/4"}
  ],
  "answer_label": "D",
  "answer_text": "0/4",
  "support": "...",
  "source_meta": {
    "source_file": "scienceqa_hf/data/train-00000-of-00001-1028f23e353fbe3e.parquet",
    "source_split": "train",
    "source_index": 9049
  }
}

For text-only examples, image is null and modality is text.

Field Meaning

id: unique example ID in this release.
dataset: upstream dataset name.
subset: upstream subset/config name, or null.
split: release split, either train or test.
task_type: fixed as multiple_choice_science_qa.
modality: text or image_text.
question: question text.
image: relative image path and MIME type for image-text examples; otherwise null.
choices: regenerated multiple-choice options, labeled from A.
answer_label: correct answer label after option shuffling.
answer_text: correct answer text.
support: explanation or supporting context when available.
source_meta: source file, original split/index, original answer metadata, and license notes.

Statistics

Token statistics use regex_approx_v1 because tiktoken was not installed in the local memory environment during construction.

Split	Examples	Image examples	Text examples	Avg input tokens	Avg support tokens	Avg full record tokens
train	17,880	281	17,599	33.83	73.63	394.80
test	4,781	70	4,711	44.56	33.46	386.07
overall	22,661	351	22,310	36.09	65.15	392.96

Modality distribution:

Modality	Count
text	22,310
image_text	351

Construction Method

Read local parquet files only; no dataset is re-downloaded.
Convert every valid example into the unified JSONL schema.
Merge upstream train and validation into release train.
Keep upstream test as release test.
Export ScienceQA image bytes to images/scienceqa/ and store relative paths in JSONL.
Deterministically shuffle choices with seed=42.
Remove normalized duplicates within split.
Remove train examples whose normalized question+choices+answer key overlaps with held-out test.

Deduplication summary:

{
  "train_duplicates_removed": 9,
  "test_duplicates_removed": 0,
  "train_removed_for_test_overlap": 7,
  "train_test_overlap_after_filter": 0
}

How to Load

import json
from pathlib import Path

root = Path("general_science_release")

with (root / "train.jsonl").open(encoding="utf-8") as f:
    first = json.loads(next(f))

print(first["question"])
print(first["choices"])

if first["image"] is not None:
    image_path = root / first["image"]["path"]
    print(image_path)

For model evaluation, use question, image, and choices as input. Do not feed support unless the task explicitly allows explanation or retrieval context, because support may reveal the answer.

Validation

The release was checked with:

conda run -n memory python scripts/validate_general_science_release.py

Validation result:

train=17880
test=4781
total=22661
image_examples=351
validation=ok

License

This release combines multiple upstream datasets and should be redistributed only under terms compatible with all included sources.

SciQ: CC BY-NC 3.0 according to the local Hugging Face dataset card.
AI2 ARC: CC BY-SA 4.0 according to the local Hugging Face dataset card.
ScienceQA: license metadata was not available in the local snapshot used here; verify the upstream dataset license before public HF/ModelScope upload.

Because the combined release includes non-commercial and share-alike sources, downstream usage should be treated conservatively. Public upload should include the source attribution and license notes above.