Multimodal datasets, integrating data from multiple sources such as text, images, audio, and physiological signals, have become increasingly valuable in scientific research. These datasets provide a comprehensive understanding of complex phenomena, facilitating advancements in fields like medicine, psychology, computer vision, and natural language processing. Publicly available multimodal datasets have democratized access to high-quality data, enabling researchers worldwide to contribute to and benefit from scientific advancements. This review article examines the significance of public multimodal datasets, highlighting their contributions to scientific research, challenges in their use, and future directions. We explore key datasets, their applications, and the methodological innovations they have spurred. By providing a detailed overview, this article aims to inform researchers about the potential and considerations in leveraging multimodal datasets for advancing scientific knowledge. The integration of diverse data types offers unprecedented opportunities for developing sophisticated machine learning models, uncovering novel insights, and fostering interdisciplinary collaborations. However, the use of these datasets also presents challenges, such as data integration, computational demands, and privacy concerns, which need to be addressed to fully realize their potential.