EXSCLAIM! Validation Dataset - Selections from Amazon Mechanical Turk Benchmark

Due to recent improvements in image resolution and acquisition speed, materials microscopy is experiencing an explosion of published imaging data. The standard publication format, while sufficient for traditional data ingestion scenarios where a select number of images can be critically examined and curated manually, is not conducive to large-scale data aggregation or analysis. Most images in publications are presented as components of a larger figure with their explicit context buried in the main body or caption text, so even if aggregated, collections of images with weak or no digitized contextual labels have limited value. To solve the problem of curating labeled microscopy data from literature, the authors the EXSCLAIM! Python toolkit for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific literature. We highlight the methodology behind the construction of EXSCLAIM! and demonstrate its ability to extract and label open-source scientific images at high volume. This dataset is used to validate the classification and bounding box prediction accuracy of the FigureSeparator component of the EXSCLAIM! pipeline.

近年来，图像分辨率与采集速度的提升推动了材料显微学领域已发表成像数据的爆发式增长。标准出版格式虽能满足传统数据获取场景（人工严格检查与整理少量精选图像）的需求，但并不适用于大规模数据聚合与分析。出版物中的多数图像作为大型图表的组成部分呈现，其明确背景信息常隐藏于正文或图例文本中；因此，即便聚合，缺乏数字化上下文标签或标签较弱的图像集合价值有限。为解决从文献中整理带标签显微数据的难题，作者们开发了EXSCLAIM! Python工具包，用于从科学文献中自动提取、分离图像并基于图例进行自然语言标注（EXtraction, Separation, and Caption-based natural Language Annotation of IMages）。本文重点阐述EXSCLAIM!的构建方法论，并展示其对开源科学图像的大规模提取与标注能力。本数据集用于验证EXSCLAIM!流程中图分离组件（FigureSeparator）的分类及边界框预测精度。