metadata
dataset_info:
features:
- 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
splits:
- name: train
num_bytes: 35801
num_examples: 91
download_size: 22781
dataset_size: 35801
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
SPOT-MetaData
Metadata & Annotations for Scientific Paper ErrOr DeTection (SPOT)
SPOT contains 83 papers and 91 human-validated errors to test academic verification capabilities.
📖 Overview
SPOT-MetaData contains all of the annotations for the SPOT benchmark—no paper PDFs or parsed content are included here. This lightweight repo is intended for anyone who needs to work with the ground-truth error labels, categories, locations, and severity ratings.
Parse contents are available at: link.
For codes see: link.
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
📜 License
This repository (metadata & annotations) is released under the CC-BY-4.0 license.