A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing.

Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing. This dataset encompasses over 1.1 million peer-reviewed scholarly articles, enhanced with metadata and visuals that are aligned with the text. We assess the representativeness of MMAD by comparing its country/region distribution against benchmarks from SCImago. Furthermore, we propose an innovative quality validation method for MMAD, leveraging Language Model-based techniques. Utilizing carefully crafted prompts, this approach enhances multimodal processing capabilities to evaluate the accuracy of text-to-visual alignments. We also outline prospective applications for MMAD, providing the way for novel research endeavors, including automated caption generation and analysis of trends in figures. Thus, this work signals new research prospects and provides a fertile ground for advances in academic data processing.