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.

通过OpenReview.net和SciPost.org网站，我们引入了全面的Open Review-Based数据集（ORB）；它包括一个精选列表，其中包含超过36,000篇科学论文的89,000多条评论和最终决策。我们提供了ORB的数据架构和相关统计数据的概述，还讨论了基于自然语言处理的初步实验，旨在预测论文的接受情况以及从嵌入中推断出的评分统计数据。ORB为对开放科学和审查感兴趣的研究人员提供了宝贵资源，我们的实现使进一步分析和实验使用这些数据更加便捷。我们计划随着领域的发展更新ORB，并引入更适用于专门科学领域（如高能物理学）的新资源。

基于开放评审的数据集 (ORB)：高能物理科学论文和实验提案的自动评估