README.md

---
language:
- en
task_categories:
- question-answering
- table-question-answering
tags:
- scientific-reasoning
- tabular-data
- complex-reasoning
- algorithmic-reasoning
- math
pretty_name: SciTaRC
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: paper
    dtype: string
  - name: relevant_tables
    list:
      list: string
  - name: tables
    list:
      list: string
  - name: fulltext
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: plan
    dtype: string
  splits:
  - name: test
    num_bytes: 48991529
    num_examples: 371
  download_size: 13748575
  dataset_size: 48991529
---

# Dataset Card for SciTaRC

## Dataset Description

- **Paper:** SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation

### Dataset Summary

**SciTaRC** (Scientific Table Reasoning and Computation) is an expert-authored benchmark designed to evaluate Large Language Models (LLMs) on complex question-answering tasks over real-world scientific tables. 

Unlike existing benchmarks that focus on simple table-text integration or single-step operations, SciTaRC focuses on **composite reasoning**—requiring models to execute interdependent operations such as descriptive analysis, complex arithmetic, and ranking across detailed scientific tables. To facilitate granular diagnosis of model failures, every instance includes an expert-annotated **pseudo-code plan** that explicitly outlines the algorithmic reasoning steps required to reach the correct answer.

## Dataset Structure

The dataset is provided as a single `test` split containing 370 expert-annotated instances. 

### Data Instances
A typical instance contains the question, the ground truth answer, the expert-authored pseudo-code plan, the LaTeX representations of the relevant tables, and the full text of the source paper.

### Data Fields

Each JSON object in the dataset contains the following fields:

- `paper` *(string)*: The arXiv ID of the source scientific paper (e.g., `"2401.06769"`).
- `question` *(string)*: The complex, multi-step question asked about the tabular data.
- `answer` *(string)*: The ground-truth answer.
- `plan` *(string)*: The expert-authored pseudo-code blueprint. It explicitly structures the logical and mathematical operations required to solve the question (e.g., `SELECT`, `LOOP`, `COMPUTE`).
- `relevant_tables` *(list of lists of strings)*: The exact LaTeX source code for the specific table(s) required to answer the question.
- `tables` *(list of lists of strings)*: The LaTeX source code for all tables and figures extracted from the paper.
- `fulltext` *(string)*: The complete LaTeX source text of the original scientific paper, providing full context.

## Citation

If you use this dataset, please cite the original paper:

```bibtex
@misc{scitarc2026,
  title={SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation},
  author={Wang, Hexuan and Ren, Yaxuan and Bommireddypalli, Srikar and Chen, Shuxian and Prabhudesai, Adarsh and Baral, Elina and Zhou, Rongkun and Koehn, Philipp},
  year={2026},
  url={[Insert ArXiv URL here]}
}