SciTSR-CC-BY-NC-SA

A license-filtered subset of SciTSR, a large-scale table structure recognition dataset of scientific tables extracted from arXiv LaTeX source files.

This subset contains tables whose source papers are compatible with a CC-BY-NC-SA 4.0 open-weight model release — covering public domain, CC-BY, CC-BY-NC, and CC-BY-NC-SA licensed papers. The dataset itself is released under CC-BY-NC-SA 4.0.

Dataset Details

Split	Tables	Papers
train	697	—
test	192	—
total	889	470

Source paper licenses present:

License	Tables	Notes
CC-BY	444	Attribution required
CC-BY-NC-SA	337	NC + share-alike
CC0	85	Public domain
other:http://creativecommons.org/licenses/publicdomain/	23	Old CC public domain dedication

Why This Subset Exists

The full SciTSR dataset (15,000 tables) was crawled from arXiv without license filtering. ~90% of those papers use the arXiv non-exclusive license, which retains full author copyright. This subset was produced by querying the arXiv OAI-PMH API for the license of each source paper and retaining only those compatible with a non-commercial open-weight release.

License compatibility logic: CC-BY-NC-SA on model weights requires that training data allows NC use and that any SA clause matches the output license. CC-BY-SA was explicitly excluded because its SA clause conflicts with adding an NC restriction. This analysis assumes model weights are not derivative works of training data — a legally unsettled question. Users should conduct their own legal review for their specific use case.

For unrestricted commercial use with no attribution requirements, see SciTSR-PD.

Dataset Structure

Each row represents one table extracted from a scientific PDF. The paper_license column records the source paper's license so downstream users can apply their own filtering.

Column	Type	Description
table_id	string	Unique identifier, format {arxiv_id}v{version}.{table_index}
paper_id	string	arXiv paper ID
paper_title	string	Paper title from arXiv metadata
paper_authors	list[string]	Author names from arXiv metadata
paper_license	string	License of the source paper
split	string	train or test
is_comp	bool	Whether this table is in the SciTSR-COMP subset (tables with at least one spanning cell)
image	Image	PNG render of the table (150 DPI)
pdf	binary	Raw PDF of the isolated table
chunks	list[dict]	Pre-extracted text spans with bounding box coordinates {text, x1, x2, y1, y2} (PDF coordinate space, bottom-left origin)
cells	list[dict]	Structure annotation: {id, tex, content, start_row, end_row, start_col, end_col}
relations	list[dict]	Chunk adjacency labels {chunk_id_1, chunk_id_2, relation, num_blank} where relation=1 is horizontal and relation=2 is vertical. Empty for test rows.

Usage

from datasets import load_dataset

ds = load_dataset("rootsautomation/SciTSR-cc-by-nc-sa")

# Iterate train split
for row in ds["train"]:
    image   = row["image"]          # PIL Image
    cells   = row["cells"]          # list of cell dicts
    chunks  = row["chunks"]         # list of chunk dicts with positions
    print(row["table_id"], row["paper_title"], row["paper_license"])

# Filter to only public domain if needed
pd_only = ds.filter(lambda x: "CC0" in x["paper_license"] or "publicdomain" in x["paper_license"])

Important Caveats

• Annotation quality: Structure annotations were generated automatically from LaTeX source by the original SciTSR pipeline. Simple grid tables are generally reliable. Tables with spanning cells (is_comp=True) have a higher rate of annotation errors, particularly in spanning cell coordinates. Treat annotations as noisy weak supervision rather than ground truth.
• Attribution: CC-BY, CC-BY-NC, and CC-BY-NC-SA papers require attribution to original authors. paper_title, paper_authors, and paper_id (linking to arxiv.org/abs/{paper_id}) are provided for this purpose.
• Chunk coordinates: The chunks field was pre-processed by TabbyCDF and may contain noise.
• Relations: Available for train split only; empty list for test.

Source & Citation

This dataset is a license-filtered derivative of SciTSR. If you use this dataset, please cite the original work:

@article{chi2019complicated,
  title={Complicated Table Structure Recognition},
  author={Chi, Zewen and Huang, Heyan and Xu, Heng-Da and Yu, Houjin and Yin, Wanxuan and Mao, Xian-Ling},
  journal={arXiv preprint arXiv:1908.04729},
  year={2019}
}