Faez Ahmed, Cyril Picard, Wei Chen, Christopher McComb, Pingfeng Wang, Ikjin Lee, Tino Stankovic, Douglas Allaire, Stefan Menzel, "Special Issue: Design by Data: Cultivating Datasets for Engineering Design", Journal of Mechanical Design, 2025.

The transformative impact of data-driven methods, which have revolutionized fields like image and text analysis, relies on the availability of adequately large and diverse datasets. These datasets have fueled breakthroughs in deep learning, enabling the development of useful AI tools such as ChatGPT, Gemini, Llama, and Stable Diffusion. Similarly, in engineering design, data-driven methodologies are reshaping traditional paradigms—enhancing design theory, decision-making processes, optimization strategies, and educational curricula. By facilitating faster design exploration and automation, these methods are opening new frontiers in the field. Despite these advancements, the adoption of machine learning and data-driven approaches in engineering design faces significant hurdles, primarily due to dataset-related challenges. Key among these are the scarcity of publicly available datasets, insufficient sample sizes and feature diversity in existing datasets, and the limited integration of critical dimensions such as functional performance. Moreover, the demand for high-quality data presents a persistent hurdle, as engineering applications require datasets that are robust, comprehensive, and tailored to the complexities of design tasks. This editorial highlights a special collection of papers that share design datasets and examine the intersection of data-driven methodologies and engineering design. The selected works not only investigate novel approaches but also provide detailed discussions on the datasets employed, many of which are publicly released to encourage broader use and collaboration. These contributions span several critical areas of engineering, with topics ranging from engineering catalogs and vehicle systems to advanced materials and manufacturing processes. The datasets cover various domains including power systems, human-centered design, synthetic data generation, mechanical artifacts, and infrastructure monitoring. Each dataset aims to fill a unique gap in current research and application capabilities, providing a valuable resource for future studies and developments in these fields. Ultimately, this special issue seeks to spark dialogue around best practices for managing, curating, publishing, and using datasets within the engineering design community. It also aims to inspire further research and development by making high-quality datasets accessible and promoting transparency in data use. The discussions and findings presented below build upon our collective experiences from this special issue and broader efforts in the field, leading us to explore in-depth the challenges and offer recommendations for future design datasets.