建立清数湾 的初衷/ The movitation of “Clear Data Bay“

数据驱动作为物理试验，解析推导和数值模拟之外的第四类研究范式正在重塑着科学研究的基本思路和模式。尽管从数据和算法的角度，都有各种各样尝试使得以更少的数据发挥更大的效能，但毕竟巧妇难为无米之炊！海量高保真数据的获取，仍是一个制约数据驱动方法发挥更大效力的关键所在！

 As the fourth research paradigm alongside physical experiments, analytical formulation, and numerical simulation, data-driven approaches are reshaping the fundamental logic and methodology of scientific discovery. Although efforts have been made from both data and algorithmic perspectives to achieve greater efficacy with fewer data, one can't make bricks without straw! The acquisition of massive, high-fidelity datasets remains a critical bottleneck restricting the full potential of data-driven methods.

在大模型等AI技术颠覆各行各业的时代，数据同算力一样，正成为一种科学研究的重要生产要素。然而，数据的管理，目前面临着高质量数据稀缺、数据分布不均衡、数据孤岛化、且缺乏标准等挑战。建立跨机构、跨领域的数据共享平台，打破数据孤岛，已成为推动数据驱动科学研究继续向前发展的关键瓶颈之一。

In the era where AI technologies like large foundation models are revolutionizing various industries, data, much like computational power, has become a vital factor of production in scientific research. However, data management currently faces multiple challenges, including the scarcity of high-quality data, imbalanced data distribution, data silos, and a lack of standardization. Establishing cross-institutional and interdisciplinary data-sharing platforms to break down data silos has emerged as a key bottleneck in advancing data-driven scientific research.

基于此，“Clear Data Bay” 尝试以元数据为主要方式集成领域内的开放数据集，旨在构建一个综合、全面但是轻量化的数据字典，建立一个为工程科学研究提供数据的管理、共享、合作和交易平台，从而促进工程科学数据的高效流通与价值释放。

To address this emerging challenge, "Clear Data Bay" aims to integrate open datasets within specific domains primarily through metadata, with the goal of constructing a comprehensive yet lightweight data dictionary. It seeks to establish a platform for the management, sharing, collaboration, and transaction of engineering science data, thereby facilitating efficient circulation and value unlocking of scientific and engineering data.