# The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics

arXiv: https://arxiv.org/abs/2312.04576 · HTML: https://mlsanity.com/paper/2312.04576/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [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)
- [MReD: A Meta-Review Dataset for Structure-Controllable Text Generation](https://mlsanity.com/paper/2110.07474/) (2021)
- [MOPRD: A multidisciplinary open peer review dataset](https://mlsanity.com/paper/2212.04972/) (2022)
- [Does My Rebuttal Matter? Insights from a Major NLP Conference](https://mlsanity.com/paper/1903.11367/) (2019)
- [What Have We Learned from OpenReview?](https://mlsanity.com/paper/2103.05885/) (2021)

## Built on by (most cited first, 4 of 4)

- [SocialMaze: A Benchmark for Evaluating and Enhancing Social Reasoning in Large Language Models in Complex Social Environments](https://mlsanity.com/paper/2505.23713/) (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)
- [Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models](https://mlsanity.com/paper/2512.22145/) (2025)

## Abstract (arXiv)

With the Open Science approach becoming important for research, the evolution towards open scientific-paper reviews is making an impact on the scientific community. However, there is a lack of publicly available resources for conducting research activities related to this subject, as only a limited number of journals and conferences currently allow access to their review process for interested parties. In this paper, we introduce the new comprehensive Open Review-Based dataset (ORB); it includes a curated list of more than 36,000 scientific papers with their more than 89,000 reviews and final decisions. We gather this information from two sources: the OpenReview.net and SciPost.org websites. However, given the volatile nature of this domain, the software infrastructure that we introduce to supplement the ORB dataset is designed to accommodate additional resources in the future. The ORB deliverables include (1) Python code (interfaces and implementations) to translate document data and metadata into a structured and high-level representation, (2) an ETL process (Extract, Transform, Load) to facilitate the automatic updates from defined sources and (3) data files representing the structured data. The paper presents our data architecture and an overview of the collected data along with relevant statistics. For illustration purposes, we also discuss preliminary Natural-Language-Processing-based experiments that aim to predict (1) papers' acceptance based on their textual embeddings, and (2) grading statistics inferred from embeddings as well. We believe ORB provides a valuable resource for researchers interested in open science and review, with our implementation easing the use of this data for further analysis and experimentation. We plan to update ORB as the field matures as well as introduce new resources even more fitted to dedicated scientific domains such as High-Energy Physics.