File size: 1,835 Bytes
0a850fa 3f6ffba 0a850fa 3cbac30 0a850fa 3cbac30 3f6ffba 3cbac30 ad0c4f8 c0b2c11 3f6ffba 3cbac30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- text-classification
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-*
---
# SPOT-MetaData
> Metadata & Annotations for **Scientific Paper ErrOr DeTection** (SPOT)
> *SPOT contains 83 papers and 91 human-validated errors to test academic verification capabilities.*
This dataset is introduced in the paper [When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research](https://huggingface.co/papers/2505.11855).
## 📖 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](https://huggingface.co/datasets/amphora/SPOT).
For codes see: [link](https://github.com/guijinSON/SPOT).
Project page:
> **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. |