MicroVQA++: A Large-Scale, High-Quality Microscopy Visual Question Answering Dataset

MicroVQA++ is a three-stage, large-scale and high-quality microscopy visual question answering corpus derived from biomedical imaging sources. The dataset, created by author ieellee and last updated on 2025-12-14, is designed to address the scarcity of training data for scientific reasoning in microscopy with multimodal large language models.

Use Cases

• Training multimodal LLMs for scientific reasoning based on microscopy images and text questions.
• Benchmarking model performance on high-quality biomedical visual question answering tasks.
• Exploring weakly supervised learning methods for graph generation from multimodal data.
• Advancing biomedical AI applications that require joint understanding of images and natural language.

Strengths

• Described as a 'large-scale' and 'high-quality' microscopy VQA corpus.
• Uses a three-stage construction process, suggesting structured data curation.
• Last updated on 2025-12-14, indicating recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and specific size are unknown, which may limit suitability assessment.
• The license is unspecified, requiring verification before use.

Provenance

Source

huggingface

Collection Method

Derived from biomedical imaging sources; details are in the linked arXiv paper.

Time Range

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Freshness

Last updated 2025-12-14 14:24:20

Geography

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