metadata

dataset_info:
  features:
    - name: doi/arxiv_id
      dtype: string
    - name: title
      dtype: string
    - name: paper_category
      dtype: string
    - name: error_category
      dtype: string
    - name: error_location
      dtype: string
    - name: error_severity
      dtype: string
    - name: error_annotation
      dtype: string
    - name: paper_content
      list:
        - name: image_url
          struct:
            - name: url
              dtype: string
        - name: text
          dtype: string
        - name: type
          dtype: string
    - name: error_local_content
      list:
        - name: image_url
          struct:
            - name: url
              dtype: string
        - name: text
          dtype: string
        - name: type
          dtype: string
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: train
      num_bytes: 58231756
      num_examples: 68
  download_size: 55816319
  dataset_size: 58231756
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
language:
  - en
size_categories:
  - n<1K

SPOT

Preprocessed Contents of Scientific Paper ErrOr DeTection (SPOT)
SPOT contains 83 papers and 91 human-validated errors to test academic verification capabilities.
This repo contains preprocessed contents of 62 manuscripts with share-permissive licenses.

📖 Overview

This repository holds the full paper files (parsed Markdown, and base64 encodings of extracted figures) for the subset of SPOT manuscripts that are openly licensed. Combined with the annotations in SPOT-MetaData, you can run end-to-end evaluations of LLMs on multi-modal academic error detection.

Benchmark at a glance

83 published manuscripts

91 confirmed errors (errata or retractions)

10 scientific domains (Math, Physics, Biology, …)

6 error types (Equation/Proof, Fig-duplication, Data inconsistency, …)

Average paper length: ~12 000 tokens & 18 figures

Included

62 open-access papers (CC-BY or equivalent)

High-fidelity Markdown conversions of each PDF

base64 encoding of every figure, table, and equation

All in openai api format.

Excluded

Paywalled or proprietary manuscripts (cannot be redistributed)

📋 Column Descriptions

Each row in annotations/errors.csv contains the following fields:

doi/arxiv_id: The paper’s DOI (journal) or arXiv identifier.
title: Full title of the manuscript.
paper_category: Scientific domain of the paper, one of: Mathematics, Physics, Biology, Chemistry, Materials Science, Medicine, Environmental Science, Engineering, Computer Science, Multidisciplinary.
error_category: Type of error, one of:
- Equation/Proof
- Figure duplication
- Data inconsistency
- Experiment setup
- Reagent identity
- Statistical reporting
error_location: Where the error appears (e.g., Figure 2, Equation (5), Section 3.1, Table 4).
error_severity: Indicates whether the issue led to an Erratum correction or a Retraction.
error_annotation: Written summary describing the error.
paper_content: Processed content of the full paper (text in markdown, images in base64 encodings).
error_local_content: Extracted snippet around the error—paragraph, caption, or equation block used in experiments in Appendix B.1.

📜 License & Copyright

SPOT code: CC-BY-4.0
Individual papers & Processed Contents: distributed under their original licenses.