# FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research arXiv: https://arxiv.org/abs/2602.14285 ยท HTML: https://mlsanity.com/paper/2602.14285/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 45 references that are in the map. - First posted on arXiv 15 Feb 2026. ## Builds on (most cited first, 30 of 30) - [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023) - [Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models](https://mlsanity.com/paper/2403.00231/) (2024) - [ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation](https://mlsanity.com/paper/2406.09961/) (2024) - [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025) - [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) - [MOPRD: A multidisciplinary open peer review dataset](https://mlsanity.com/paper/2212.04972/) (2022) - [TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://mlsanity.com/paper/2506.07642/) (2025) - [SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding](https://mlsanity.com/paper/2408.15545/) (2024) - [ReviewEval: An Evaluation Framework for AI-Generated Reviews](https://mlsanity.com/paper/2502.11736/) (2025) - [SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning](https://mlsanity.com/paper/2306.03491/) (2023) - [MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding](https://mlsanity.com/paper/2407.04903/) (2024) - [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021) - [LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers](https://mlsanity.com/paper/2403.15529/) (2024) - [Unveiling the Merits and Defects of LLMs in Automatic Review Generation for Scientific Papers](https://mlsanity.com/paper/2509.19326/) (2025) - [cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers](https://mlsanity.com/paper/2406.08398/) (2024) - [ACL-Fig: A Dataset for Scientific Figure Classification](https://mlsanity.com/paper/2301.12293/) (2023) - [MMReview: A Multidisciplinary and Multimodal Benchmark for LLM-Based Peer Review Automation](https://mlsanity.com/paper/2508.14146/) (2025) - [Uni-SMART: Universal Science Multimodal Analysis and Research Transformer](https://mlsanity.com/paper/2403.10301/) (2024) - [RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance](https://mlsanity.com/paper/2406.10294/) (2024) - [What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review](https://mlsanity.com/paper/2509.25701/) (2025) - [Identifying Aspects in Peer Reviews](https://mlsanity.com/paper/2504.06910/) (2025) - [AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning](https://mlsanity.com/paper/2505.03332/) (2025) - [Gen-Review: A Large-scale Dataset of AI-Generated (and Human-written) Peer Reviews](https://mlsanity.com/paper/2510.21192/) (2025) - [Multi-Agent LLMs for Generating Research Limitations](https://mlsanity.com/paper/2601.11578/) (2025) - [LLM Context Conditioning and PWP Prompting for Multimodal Validation of Chemical Formulas](https://mlsanity.com/paper/2505.12257/) (2025) - [Inconsistencies in TeX-Produced Documents](https://mlsanity.com/paper/2407.15511/) (2024) - [AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing](https://mlsanity.com/paper/2409.10016/) (2024) ## Built on by (most cited first, 1 of 1) - [From peer review nuances to best practices](https://mlsanity.com/paper/2607.22681/) (2026) ## Abstract (arXiv) Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on automated and AI-assisted peer review has proliferated. Despite this momentum, empirical progress remains constrained by several critical limitations in existing datasets. While reviewers routinely evaluate figures, tables, and complex layouts to assess scientific claims, most existing datasets remain overwhelmingly text-centric. This bias is reinforced by a narrow focus on data from computer science publications. Furthermore, existing datasets rarely preserve precise alignment between review comments and specific manuscript versions, obscuring the iterative relationship between peer review and manuscript evolution. In response, we introduce FMMD, a multimodal and multidisciplinary open peer review dataset curated from F1000Research. The dataset addresses the current limitations by integrating manuscript-level visual and structural data with version-specific reviewer reports and editorial decisions. By explicitly aligning review comments with the exact article version under review, FMMD enables granular analysis of the peer review lifecycle. Importantly, its coverage of F1000Research extends ASPR research beyond its traditional focus on computer science to a diverse range of scientific disciplines. FMMD supports a range of research tasks, including visual-semantic consistency classification, figure-related review comment generation, and editorial decision prediction based on multimodal manuscript inputs, thereby providing a comprehensive empirical resource for developing and evaluating multimodal ASPR systems and advancing peer review research.