README.md

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- SDQP
- scholarly
- citation_count_prediction
- review_score_prediction
dataset_info:
  features:
  - name: paperhash
    dtype: string
  - name: s2_corpus_id
    dtype: string
  - name: arxiv_id
    dtype: string
  - name: title
    dtype: string
  - name: abstract
    dtype: string
  - name: authors
    sequence:
    - name: name
      dtype: string
    - name: affiliation
      struct:
      - name: laboratory
        dtype: string
      - name: institution
        dtype: string
      - name: location
        dtype: string
  - name: summary
    dtype: string
  - name: field_of_study
    sequence: string
  - name: venue
    dtype: string
  - name: publication_date
    dtype: string
  - name: n_references
    dtype: int32
  - name: n_citations
    dtype: int32
  - name: n_influential_citations
    dtype: int32
  - name: introduction
    dtype: string
  - name: background
    dtype: string
  - name: methodology
    dtype: string
  - name: experiments_results
    dtype: string
  - name: conclusion
    dtype: string
  - name: full_text
    dtype: string
  - name: decision
    dtype: bool
  - name: decision_text
    dtype: string
  - name: reviews
    sequence:
    - name: review_id
      dtype: string
    - name: review
      struct:
      - name: title
        dtype: string
      - name: paper_summary
        dtype: string
      - name: main_review
        dtype: string
      - name: strength_weakness
        dtype: string
      - name: questions
        dtype: string
      - name: limitations
        dtype: string
      - name: review_summary
        dtype: string
    - name: score
      dtype: float32
    - name: confidence
      dtype: float32
    - name: novelty
      dtype: float32
    - name: correctness
      dtype: float32
    - name: clarity
      dtype: float32
    - name: impact
      dtype: float32
    - name: reproducibility
      dtype: float32
    - name: ethics
      dtype: string
  - name: comments
    sequence:
    - name: title
      dtype: string
    - name: comment
      dtype: string
  - name: references
    sequence:
    - name: paperhash
      dtype: string
    - name: title
      dtype: string
    - name: abstract
      dtype: string
    - name: authors
      sequence:
      - name: name
        dtype: string
      - name: affiliation
        struct:
        - name: laboratory
          dtype: string
        - name: institution
          dtype: string
        - name: location
          dtype: string
    - name: arxiv_id
      dtype: string
    - name: s2_corpus_id
      dtype: string
    - name: intents
      sequence: string
    - name: isInfluential
      dtype: bool
  - name: hypothesis
    dtype: string
  - name: month_since_publication
    dtype: int32
  - name: avg_citations_per_month
    dtype: float32
  splits:
  - name: train
    num_bytes: 160996851
    num_examples: 2060
  - name: validation
    num_bytes: 758528471
    num_examples: 9013
  - name: test
    num_bytes: 853299931
    num_examples: 9014
  download_size: 872309865
  dataset_size: 1772825253
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

Datasets related to the task of Scholarly Document Quality Prediction (SDQP). 
Each sample is an academic paper for which either the citation count or the review score can be predicted (depending on availability).
The information that is potentially available for each sample can be found below.  

## ACL-OCL Extended
A dataset for citation count prediction only, based on the [ACL-OCL dataset](https://huggingface.co/datasets/WINGNUS/ACL-OCL/tree/main).
Extended with updated citation counts, references and annotated research hypothesis

## OpenReview 
A dataset for review score and citation count prediction, obtained by parsing OpenReview.
Due to licensing the dataset comes in 3 formats:

1. openreview-public: Contains full information on all OpenReview submissions that are accompanied by a license. 
2. openreview-full-light: The full dataset excluding the parsed pdfs of the submitted papers.
3. openreview-full: A script to obtain the full dataset with submissions.



## Citation

If you use the dataset in your work, please cite:



The data model for the papers:

### Paper Data Model
```json
{
# ID's
"paperhash": str,
"arxiv_id": str | None,
"s2_corpus_id": str | None,

# Basic Info
"title":str,
"authors": list[Author],
"abstract": str | None,
"summary": str | None,
"publication_date": str | None,

# OpenReview Metadata
"field_of_study": list[str] | str | None,
"venue": str | None,

# s2 Metadata
"n_references": int | None,
"n_citations": int | None,
"n_influential_citations": int | None,
"open_access": bool | None,
"external_ids": dict | None,
"pdf_url": str | None,

# Content
"parsed_pdf": dict | None,
"parsed_latex": dict | None,
"structured_content": dict[str, Section],

# Review Data
"openreview": bool,
"decision": bool | None,
"decision_text": str | None,
"reviews": list[Review] | None,
"comments": list[Comment] | None,

# References
"references": list[Reference] | None,
"bibref2section": dict,
"bibref2paperhash": dict,

# Hypothesis
"hypothesis": dict | None
}
```

### Author Data Model
```json
{
"name":str,
"affiliation": {
  "laboratory": str | dict | None,
  "institution": str | dict | None, 
  "location": str | dict | None
  }
}
```

### Reference Data Model 
```json
{
"paperhash": str,
"title": str,
"abstract": str = "",
"authors": list[Author],

# IDs
"arxiv_id": str | None,
"s2_corpus_id": str | None,
"external_ids": dict| None,

# Reference specific info
"intents": list[str] | None = None,
"isInfluential": bool | None = None
}
```


### Comment Data Model 
```json
{
"title": str,
"comment": str
}
```


### Section Data Model 
```json
{
"name": str,
"sec_num": str,
"classification": str,
"text": str,
"subsections": list[Section]
}
```

### Review Data Model

```json
{
"review_id": str,
"review": {
  "title": str | None,
  "paper_summary": str | None,
  "main_review": str | None,
  "strength_weakness": str | None,
  "questions": str | None,
  "limitations": str | None,
  "review_summary": str | None
}
"score": float | None,
"confidence": float | None,
"novelty": float | None,
"correctness": float | None,
"clarity": float | None,
"impact": float | None,
"reproducibility": float | None,
"ethics": str | None
}
```