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
  - name: mean_score
    dtype: float32
  - name: mean_confidence
    dtype: float32
  - name: mean_novelty
    dtype: float32
  - name: mean_correctness
    dtype: float32
  - name: mean_clarity
    dtype: float32
  - name: mean_impact
    dtype: float32
  - name: mean_reproducibility
    dtype: float32
  - name: openreview_submission_id
    dtype: string
  splits:
  - name: acl_ocl_train
    num_bytes: 2553475339
    num_examples: 42060
  - name: acl_ocl_validation
    num_bytes: 805703352
    num_examples: 9013
  - name: acl_ocl_test
    num_bytes: 806719943
    num_examples: 9014
  download_size: 2084737209
  dataset_size: 4165898634
configs:
- config_name: acl_ocl
  data_files:
  - split: train
    path: data/acl_ocl_train-*
  - split: validation
    path: data/acl_ocl_validation-*
  - split: test
    path: data/acl_ocl_test-*
- config_name: openreview-full
  data_files:
  - split: train
    path: data/openreview_full_training-*
  - split: validation
    path: data/openreview_full_validation-*
  - split: test
    path: data/openreview_full_test-*
- config_name: openreview-iclr
  data_files:
  - split: train
    path: data/iclr_training-*
  - split: validation
    path: data/iclr_validation-*
  - split: test
    path: data/iclr_test
- config_name: default
  data_files:
  - split: train
    path: data/iclr_training-*
  - split: validation
    path: data/iclr_validation-*
  - split: test
    path: data/iclr_test-*
- config_name: openreview-iclr
  data_files:
  - split: train
    path: data/iclr_training-*
  - split: validation
    path: data/iclr_validation-*
  - split: test
    path: data/iclr_test-*
- config_name: openreview-neurips
  data_files:
  - split: train
    path: data/neurips_training-*
  - split: validation
    path: data/neurips_validation-*
  - split: test
    path: data/neurips_test-*
- config_name: openreview-public
  data_files:
  - split: train
    path: data/openreview_public_train-*
  - split: validation
    path: data/openreview_public_validation-*
  - split: test
    path: data/openreview_public_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).
 

## 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 (Last Update: 1.1.2025)
A dataset for review score and citation count prediction, obtained by parsing OpenReview.
Due to licensing the dataset comes in different formats:

### Datasets without parsed pdfs of submissions (i.e. the fields introduction, background, methodology, experiments_results, conclusion, full_text are available)
1. **openreview-public**: Contains full information on all OpenReview submissions that are accompanied with a CC BY 4.0 license.

### Datasets without parsed pdfs of submissions (i.e. the fields introduction, background, methodology, experiments_results, conclusion, full_text are None)
1. **openreview-full**: Contains all OpenReview submissions, splits generated based on publications dates.  
2. **openreview-iclr**: All ICLR submissions from the years 2018-2023 (training) and 2024 (validation and training).
3. **openreview-neurips**: All NeurIPS submissions from the years 2021-2023 (training) and 2024 (validation and training).

All datasets without parsed pdfs of submissions can be completed by running code available [here](https://github.com/NikeHop/automatic_scientific_quality_metrics)