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With the Open Science approach becoming important for research, the evolutiontowards open scientific-paper reviews is making an impact on the scientificcommunity. However, there is a lack of publicly available resources forconducting research activities related to this subject, as only a limitednumber of journals and conferences currently allow access to their reviewprocess for interested parties. In this paper, we introduce the newcomprehensive Open Review-Based dataset (ORB); it includes a curated list ofmore than 36,000 scientific papers with their more than 89,000 reviews andfinal decisions. We gather this information from two sources: theOpenReview.net and SciPost.org websites. However, given the volatile nature ofthis domain, the software infrastructure that we introduce to supplement theORB dataset is designed to accommodate additional resources in the future. TheORB deliverables include (1) Python code (interfaces and implementations) totranslate document data and metadata into a structured and high-levelrepresentation, (2) an ETL process (Extract, Transform, Load) to facilitate theautomatic updates from defined sources and (3) data files representing thestructured data. The paper presents our data architecture and an overview ofthe collected data along with relevant statistics. For illustration purposes,we also discuss preliminary Natural-Language-Processing-based experiments thataim to predict (1) papers' acceptance based on their textual embeddings, and(2) grading statistics inferred from embeddings as well. We believe ORBprovides a valuable resource for researchers interested in open science andreview, with our implementation easing the use of this data for furtheranalysis and experimentation. We plan to update ORB as the field matures aswell as introduce new resources even more fitted to dedicated scientificdomains such as High-Energy Physics.

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