# MOPRD: A multidisciplinary open peer review dataset

arXiv: https://arxiv.org/abs/2212.04972 · HTML: https://mlsanity.com/paper/2212.04972/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 21 of 21)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://mlsanity.com/paper/2112.07916/) (2021)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [MReD: A Meta-Review Dataset for Structure-Controllable Text Generation](https://mlsanity.com/paper/2110.07474/) (2021)
- [Argument Mining for Understanding Peer Reviews](https://mlsanity.com/paper/1903.10104/) (2019)
- [Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1610.09975/) (2016)
- [Does My Rebuttal Matter? Insights from a Major NLP Conference](https://mlsanity.com/paper/1903.11367/) (2019)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation](https://mlsanity.com/paper/2011.00802/) (2020)
- [Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers](https://mlsanity.com/paper/2209.14155/) (2022)
- [ReAct: A Review Comment Dataset for Actionability (and more)](https://mlsanity.com/paper/2210.00443/) (2022)
- [COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews](https://mlsanity.com/paper/2108.04366/) (2021)

## Built on by (most cited first, 30 of 32)

- [LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing](https://mlsanity.com/paper/2406.16253/) (2024)
- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis](https://mlsanity.com/paper/2407.12857/) (2024)
- [TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://mlsanity.com/paper/2506.07642/) (2025)
- [When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research](https://mlsanity.com/paper/2505.11855/) (2025)
- [ReviewEval: An Evaluation Framework for AI-Generated Reviews](https://mlsanity.com/paper/2502.11736/) (2025)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper](https://mlsanity.com/paper/2511.04583/) (2025)
- [From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review](https://mlsanity.com/paper/2506.11343/) (2025)
- [Insights from the ICLR Peer Review and Rebuttal Process](https://mlsanity.com/paper/2511.15462/) (2025)
- [Paper Copilot: Tracking the Evolution of Peer Review in AI Conferences](https://mlsanity.com/paper/2510.13201/) (2025)
- [PeerArg: Argumentative Peer Review with LLMs](https://mlsanity.com/paper/2409.16813/) (2024)
- [OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment](https://mlsanity.com/paper/2601.01576/) (2026)
- [Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models](https://mlsanity.com/paper/2512.22145/) (2025)
- [RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation](https://mlsanity.com/paper/2603.09723/) (2026)
- [Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://mlsanity.com/paper/2604.27924/) (2026)
- [OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models](https://mlsanity.com/paper/2505.21537/) (2025)
- [The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics](https://mlsanity.com/paper/2312.04576/) (2023)
- [Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review](https://mlsanity.com/paper/2602.11173/) (2026)
- [Defend: Automated Rebuttals for Peer Review with Minimal Author Guidance](https://mlsanity.com/paper/2603.27360/) (2026)
- [APRES: An Agentic Paper Revision and Evaluation System](https://mlsanity.com/paper/2603.03142/) (2026)
- [SEAGraph: Unveiling the Whole Story of Paper Review Comments](https://mlsanity.com/paper/2412.11939/) (2024)
- [FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research](https://mlsanity.com/paper/2602.14285/) (2026)
- [EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces](https://mlsanity.com/paper/2603.28515/) (2026)
- [LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges](https://mlsanity.com/paper/2606.25057/) (2026)
- [AutoSupervision: Closing the Feedback Loop in Scientific Workflows with Grounded Revision Verification](https://mlsanity.com/paper/2607.27845/) (2026)
- [ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models](https://mlsanity.com/paper/2410.05575/) (2024)
- [Is Peer Review Really in Decline? Analyzing Review Quality across Venues and Time](https://mlsanity.com/paper/2601.15172/) (2026)
- [From peer review nuances to best practices](https://mlsanity.com/paper/2607.22681/) (2026)

## Abstract (arXiv)

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as the data are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we propose a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance as indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications.