Leveraging AI to boost network application has attracted much attention in academia and the industry. Currently AI training is data-driven, and training AI models for networking demands massive amounts of real data with different environments. The quality and depth of the data determines the accuracyte level of AI models a researcher can achieve. Until now, real network data have been far from enough to train an ideal model for network application, since the Internet, composed of heterogeneous access devices and switching devices, and transmission media provides a nearly unlimited number of scenarios. For example, RTC application requires precise network estimation to improve its quality of experience (QoE); however, due to network data deficiency, researchers still cannot provide an AI model to adapt to the variance of the bandwidth in the real network. To solve this issue, we are calling for a new research platform – OpenNetLab.