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--- |
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dataset_info: |
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features: |
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- name: doi/arxiv_id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: paper_category |
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dtype: string |
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- name: error_category |
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dtype: string |
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- name: error_location |
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dtype: string |
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- name: error_severity |
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dtype: string |
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- name: error_annotation |
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dtype: string |
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- name: paper_content |
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list: |
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- name: image_url |
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struct: |
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- name: url |
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dtype: string |
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- name: text |
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dtype: string |
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- name: type |
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dtype: string |
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- name: error_local_content |
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list: |
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- name: image_url |
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struct: |
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- name: url |
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dtype: string |
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|
- name: text |
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dtype: string |
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|
- name: type |
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dtype: string |
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|
- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 58231756 |
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num_examples: 68 |
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download_size: 55816319 |
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dataset_size: 58231756 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: cc-by-4.0 |
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language: |
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- en |
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size_categories: |
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- n<1K |
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--- |
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--- |
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# SPOT |
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> Preprocessed Contents of **Scientific Paper ErrOr DeTection** (SPOT) |
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> *SPOT contains 83 papers and 91 human-validated errors to test academic verification capabilities.* |
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> *This repo contains preprocessed contents of 62 manuscripts with share-permissive licenses.* |
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## 📖 Overview |
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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](https://github.com/<org>/SPOT-MetaData), you can run end-to-end evaluations of LLMs on multi-modal academic error detection. |
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> **Benchmark at a glance** |
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> |
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> * **83** published manuscripts |
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> * **91** confirmed errors (errata or retractions) |
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> * **10** scientific domains (Math, Physics, Biology, …) |
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> * **6** error types (Equation/Proof, Fig-duplication, Data inconsistency, …) |
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> * Average paper length: \~12 000 tokens & 18 figures |
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> **Included** |
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> - **62** open-access papers (CC-BY or equivalent) |
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> - High-fidelity **Markdown** conversions of each PDF |
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> - **base64 encoding** of every figure, table, and equation |
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> - All in **openai api** format. |
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> **Excluded** |
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> - Paywalled or proprietary manuscripts (cannot be redistributed) |
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## 📋 Column Descriptions |
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Each row in `annotations/errors.csv` contains the following fields: |
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* **`doi/arxiv_id`**: |
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The paper’s DOI (journal) or arXiv identifier. |
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* **`title`**: |
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Full title of the manuscript. |
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* **`paper_category`**: |
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Scientific domain of the paper, one of: |
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Mathematics, Physics, Biology, Chemistry, Materials Science, Medicine, Environmental Science, Engineering, Computer Science, Multidisciplinary. |
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* **`error_category`**: |
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Type of error, one of: |
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* `Equation/Proof` |
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* `Figure duplication` |
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* `Data inconsistency` |
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* `Experiment setup` |
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* `Reagent identity` |
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* `Statistical reporting` |
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* **`error_location`**: |
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Where the error appears (e.g., Figure 2, Equation (5), Section 3.1, Table 4). |
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* **`error_severity`**: |
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Indicates whether the issue led to an `Erratum` correction or a `Retraction`. |
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* **`error_annotation`**: |
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Written summary describing the error. |
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* **`paper_content`**: |
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Processed content of the full paper (text in markdown, images in base64 encodings). |
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* **`error_local_content`**: |
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Extracted snippet around the error—paragraph, caption, or equation block used in experiments in Appendix B.1. |
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## 📜 License & Copyright |
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> SPOT code: CC-BY-4.0 |
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> Individual papers & Processed Contents: distributed under their original licenses. |
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