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Astronomy education and public outreach (EPO) is one of the important part of the future development of astronomy. During the past few years, as the rapid evolution of Internet and the continuous change of policy, the breeding environment of science EPO keep improving and the number of related projects show a booming trend. EPO is no longer just a matter of to teachers and science educators but also attracted the attention of professional astronomers. Among all activates of astronomy EPO, the data driven astronomy education and public outreach (abbreviated as DAEPO) is special and important. It benefits from the development of Big Data and Internet technology and is full of flexibility and diversity. We will present the history, definition, best practices and prospective development of DAEPO for better understanding this active field.

Astronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

We propose and preliminarily implement a data-mining based platform to assist ex- perts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR. With the telescopes established and the surveys ongoing, such as the Guoshoujing telescope (LAMOST, Zhao 1999), more and more spectra are collected and released. Unfortunately, except the qualified data, there still exist many spectra unclassified by the automated pipeline. Usually most of these spectra are too low SNR to be classified because of the limited magnitude of the instruments or other reasons. Some important new discoveries are probably hidden in these unknown spectra. Therefore, we should not give up these seemingly useless data, even though they are quite defective. How to handle these unknown spectra is one of the biggest challenges to the modern statistics and data mining techniques as well as eyeball check. In order to ensure the accuracy of the results, we have to motivate experts to check these spectra by visual inspection. Owing to huge amount of such spectra generated continuously by the large sky surveys, it will spend much time and efforts to check spectra one by one. Consequently, a platform to efficiently manage and coarsely classify these unqualified data is in great requirement for the large surveys.

The Chinese Astronomical Data Center (CAsDC) is a member of World Data System, hosted at National Astronomical Observatories, Chinese Academy of Sciences(NAOC). The CAsDC keeps close collaboration with IVOA, WDS and CODATA. The whole set of LAMOST data, including raw data and data products, are hosted at the CAsDC. Data resources and services of the CAsDC are introduced.