---
license: other
language:
- en
- zh
task_categories:
- visual-question-answering
- question-answering
pretty_name: SciDocBench
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*.parquet
---

# SciDocBench

Official data release for [SciDocBench: A Workflow-Centered Benchmark and Data
Pipeline for Scientific Document Understanding](https://arxiv.org/abs/2609.05141).
Project and evaluation code: [InternLM/SciDocBench](https://github.com/InternLM/SciDocBench).

## Dataset Summary

SciDocBench contains 496 evaluation instances derived from
124 expert-authored scientific-document questions.
Each question is represented under four matched settings that cross English/Chinese
questions with all-images-first/interleaved document representations.

| Partition | Instances |
|---|---:|
| EN, all images first | 124 |
| EN, interleaved | 124 |
| ZH, all images first | 124 |
| ZH, interleaved | 124 |

The release references 7,052 images across instances and
contains 2,758 unique image files after SHA-256 deduplication.

## Files

- `data/test-00000-of-00001.parquet`: Hugging Face test split.
- `SciDocBench.tsv`: portable VLMEvalKit-compatible source table.
- `scidocbench.tsv`: compatibility alias for the original repository path.
- `images/SciDocBench/`: content-addressed document images.
- `release_manifest.json`: counts and checksums.
- `manifests/images.jsonl`: per-image hashes and reference counts.

In the Parquet file, `image_path` is a list of paths relative to the VLMEvalKit image
root, while `images` contains repository-relative paths. Interleaved `segments` retain
their ordering and refer to the rewritten `image_path` values.

## Usage

Load the table with Hugging Face Datasets:

```python
from datasets import load_dataset

dataset = load_dataset("HenryExcellent/SciDocBench", split="test")
```

For a local VLMEvalKit checkout, download the repository and place the TSV and image
tree under `LMUData`:

```bash
hf download HenryExcellent/SciDocBench   --repo-type dataset   --local-dir SciDocBench

cp SciDocBench/SciDocBench.tsv "$LMUData/SciDocBench.tsv"
mkdir -p "$LMUData/images/SciDocBench"
cp -a SciDocBench/images/SciDocBench/. "$LMUData/images/SciDocBench/"
```

## Evaluation

Use the SciDocBench integration in
[VLMEvalKit](https://github.com/open-compass/VLMEvalKit) for standardized inference
and evaluation.

## License and Use

Benchmark annotations and scripts are released for research use under the terms stated
by the SciDocBench project. Paper pages and figures originate from heterogeneous
scientific sources and may retain source-specific copyright or license terms. Users
are responsible for checking the applicable terms before redistribution or commercial
use. No blanket license is asserted over third-party document imagery.

## Citation

```bibtex
@article{wu2026scidocbench,
  title   = {SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding},
  author  = {Wu, Shenxi and Liu, Yuhong and Zhang, Haosong and Zou, Tongjin and Zhang, Yanxun and Chen, Gaochang and Liang, Dun and Wang, Jiaqi and Wang, Zhecan James and Zang, Yuhang and Lin, Dahua},
  journal = {arXiv preprint arXiv:2609.05141},
  year    = {2026}
}
```