1. Scientific Data Infrastructure in CAS Dr. Jianhui Li(lijh@cnic.cn) Scientific Data Center Computer Network Information Center Chinese Academy of Sciences

2. Scientific Data infrastructure Middle ware (Scientific data grid middleware, internet-based storage service middleware…) Scientific databases Massive storage system Data-intensive computing facilities High speed network Application enabled environments and typical applications Software and Toolkits (scientific data collection, curation, and publishing, data analyzing and visualization…)

3. DRC: Data Resource Center A new organization responsible for data preservation, curation and access service in CAS Mass data backup Data online service Mass data analysis and process Long-term preservation of important data Data Resource Center Technology service Network storage space system environment Application service mass data Managemen t system collaborator staff

4. Infrastructure for DRC High Speed Network –2Gbps linked with CSTNET –2 Gbps linked with CSTNET-CNGI –GLORIAD Data Intensive Computing facilities –~1000 CPU Core Clusters + Scientific Computing Grid （ ~200Tflops ） Massive Storage System –1PB online disk + 5PB Tape –A storage network will start to build this year 1 center + 1 archive center + 10 storage nodes around China Over 20PB

5. Scientific Databases (SDB) A Long-term mission started in 1986 which funded by CAS –many institutes involved –long-term, large-scale collaboration –data from research, for research Collecting multi-discipline research data and promoting data sharing –More than 350 research databases and 400 datasets by 61 institutes –Over 60TB data available to open access and download http://www.csdb.cn

6. Scientific Databases (cont.) SDB Contents –Physics & Chemistry, Geosciences, Biosciences, Atmospheric & Ocean Science, Energy Science, Material Science, Astronomy & Space Science

7. Scientific Databases (cont.) Database integration –Resource database –Reference database –Application oriented database Research database Resource database Reference database Application oriented database

8. Scientific Databases (cont.) 8 Resource databases –Geo-Science –Biodiversity –Chemistry –Astronomy –Space Science –Micro biology and virus –Material science –Environment 2 Reference databases –China Species –compound 4 application-Oriented databases –High Energy (ITER) –Western Environment Research –Ecology research –Qinghai Lake Research

9. CAS Scientific Data Grid Based on Scientific Data Grid Middleware (SDG) –SDG is built upon the Scientific Database, supporting to find and access large scale, distributed and heterogeneous scientific data uniformly and conveniently in a SECURE and proper way Building scientific data application grid according to domain requirements –Integrate distributed data, analysis tools and storage and computing facilities, providing a uniform data service interface –4 pilot grids bioscience grid geoscience grid Chemistry grid Astronomy and space science grid

10. Function Framework of SDG A scalable and integrated data sharing environment –Providing services for grid users, grid managers and resource provides –Operating by the operation center, science gateways and data nodes User Grid Manager Resource Provider Operation CenterScience GatewayData Node

11. Access Scientific Data Grid Software Tool Research Database Resource Databases Reference Databases Research Database App- Oriented Databases External Data Source Science Gateway and access portal Grid Middleware

12. VisualDB - Powered your database A toolkit to manage, publish and share scientific database by visual configure interface without writing codes A database integration access broker A data quality assessment tool A database access and usage statistics tool

13. Function Framework of VisualDB VDB Security Center Data Forge myDBvReportWebAPI VDB- SDK Catalog Builder

15. Security Center

16. Data Forge

17. vReport

18. Application enabled environments and typical applications Domain specific data intensive application environment –Support one specific research area –Integrated scientific data, storage, computing analysis model and tools –An easily and friendly interactive interface –Scalable user defined data process workflow Typical pilot systems –Remote sensing data on-demand accessing and processing service environment –CFCI - China FLUX Cyber-Infrastructure –DarwinTree——Molecular data analysis and application environment –Atmospheric science data integration analysis platform

19. Atmospheric science data integration analysis platform Status quo

20. Atmospheric science data integration analysis platform Problems –The size of Atmospheric data has reached TB level and they are distributed. –The personal computer hard disk, memory limit of the research work –Many algorithm finished by scientific researcher can’t be shared easily.

21. Scientific Data Analysis Online Platform Distributed Distributed data Algorithm Model Web browser 1)custom 2)visualize Algorithm Chosen Data Finding Computing for Workflow Combined with data and model Define workflow Iterative Resercher Result Using Architecture

22. work flow Select Data Choose algorithm Config param plot Analyse result Iterative Five step

23. Select data

24. Choose algorithm

25. Config param

26. plot and result

27. Thank you!