A talk by Kenji Takeda, given at the Eduserv Symposium 2011 - Virtualisation and the Cloud.

1. Institutional Data Management Blueprint Kenji Takeda (Engineering Sciences), Mark Brown (University Librarian), Simon Coles (Chemistry), Les Carr (ECS, EPrints), Jeremy Frey (Chemistry), Graeme Earl (Archaeology) Peter Hancock (iSolutions), Wendy White (Library)

2. Introduction Why data management? IDMB project Key findings Recommendations Business plan Conclusions www.southamptondata.org 2

3. Data Management @ Southampton What do we mean? Everything Why do we care? Foundation for all of our research How should it be managed? We want to find out from users How can the University help? What do researchers need? Key outcomes Impact & profile 3 copyright © 2010 Sean Dreilinger. Reproduced under Creative Commons license

4. IDMB Project Overview Produce framework for managing research data for an HEI Scope and evaluate a pilot implementation plan for an institution-wide data model 4

5. Review of Data Management

6. Where do you store your data? 6

7. How much electronic data do you currently retain? 7

8. How long do you keep your data for? 8

9. How frequently do you backup your data? 9

10. Where do you backup your data? 10

11. Key Findings Schools research practice is embedded and unified Schools data management capabilities vary widely Data management is carried out on an ad-hoc basis in many cases Researchers demand for storage is significant Researchers resort to their own best efforts in many cases, where central support does not meet their needs Users want more support for backup, particularly for large quantities of data 11

12. Key Findings Researchers want to keep their data for a long time There is a need from researchers to share data, both locally and globally Data curation and preservation support needs to be improved 12

13. Gap Analysis Policy and governance is robust, but is not communicated to researchers in the most accessible way Services and infrastructure are in place, but lack capacity and coherence There is a lack of training and guidance on data management Lack of coherence and sustainable business model 13

14. Recommendations

15. Recommendations Short-term (1 year) Develop an institutional data repository Develop a scalable business model One-stop shop for data management advice and guidance Medium-term (1-3 years) Comprehensive and affordable backup service for all Open research data mandate, and supporting infrastructure Research data lifecycle management Embedding data management training and support 15

16. Long-term recommendations Provide coherent data management support across all disciplines Embed exemplary data management practice across the institution Agile business plan for continual improvement 16

17. Pilot Projects

18. Metadata Framework 18

19. Archaeology Data Management Archaeology is all about data and metadata Spectrum of data is huge Laser scans Photography Geophysics CAD CGI Context is everything http://www.portusproject.org/

20. SharePoint 2010 Data Management 20

21. Data Browsing in Context 21

22. Training

23. Archaeology eThesis process with data Exemplar: Lithic tools over 600,000 years. Looking for evidence of use in social signalling and used in visual display. Examined 10,000 artefacts 18,300 photographs (72GB) Catalogued in SPSS database IPR/copyright? Humanities Graduate School workshop for Easter 2011 23

24. Chemistry Embedding in UG courses 1st year UG intro M.Chem more detailed Discussion group Graduate School roll-out for next intake With discipline librarians 24

25. Business planning

26. Business Plan Strategy Principles Policy Infrastructure and services Business model Partnership approach between all stakeholders Senior management, Researchers, IT, Library, Research & Innovation Services, Finance, Legal 26

27. Institutional Data Management Policy Help researchers Provide guidance on what is expected Provide guidance on how to manage their data Help the institution Define what is required Comply with funders Provide governance and decision-making process 27

28. Cost Modelling Data management is expensive Who is responsible? Who pays for it? How does it scale? What if somebody cannot afford it? Not just about hardware Sustainability 28

29. Cloud Possibilities

30. Cloud Solutions Data storage demand growing at frightening rate Users can accommodate this locally very cheaply Difficult to satisfy with current server-based storage Potential to provide: Zero capital cost Burst capability Scalability 30

31. Cloud Benefits 31 Diagram courtesy of Dr Steven Johnston

32. Cloud Issues Cost models Dropbox£8k per TB Data transfer charges Security Reliability Amazon! Legal implications Vendor lock-in 32

34. ktakeda@soton.ac.uk