Andrew Treloar, ARCHER Project Director Cathrine Harboe-Ree, University Librarian Alan McMeekin, Executive Director ITS Dancing with data down under CNI Winter 2007 Project Briefing

2 O is for Overview Drivers for what we are presenting Research case study overview Challenges and solutions Australian national developments

3 D is for Drivers

4 D: Monash – a distinctive and internationalised university Established 1960 Research intensive, doctoral granting 55,000 students from more than 100 countries 6.1% of student load is graduate 3,500 academic staff (6,800 total EFT staff) 10 faculties Campuses in Australia (six), Malaysia, South Africa, centre in Prato Partnerships – India, Hong Kong, Singapore, China Total research income $186 mill. (2006)

5 D: Information Management Strategy 2 year initiative to develop an overarching strategy for the whole university Took holistic view of information Informed by views of range of information management professionals and stakeholders Report available at: Based on set of ten principles that have been extended into the research data domain

6 D: Monash data management environment High level support –DVC (Research), Prof Edwina Cornish –Establishment of E-Research Centre Need to manage growing deluge –Leading E-researchers in some disciplines –Synchrotron (1 TB per day) –Shoah Archives (12 TB) –And others Need to respond to Australian Code for the Responsible Conduct of Research –

7 Source: Adapted from Liz Lyon, eBank UK Presentation Grid E-Researchers Entire E-Research LifeCycle Encompassing experimentation, analysis, publication, research, learning 5 Institutional Archive Local Web Publisher Holdings Digital Library E-Researchers Graduate Students Undergraduate Students Virtual Learning Environment E-Experimentation E- Technical Reports Reprints Peer- Reviewed Journal & Conference Papers Preprints & Metadata Certified Experimental Results & Analyses Data, Metadata & Ontologies DART ARROW ARCHER D: Three inter-related national projects

8 R is for Research case study

9 R: Structure determines function Unfolded protein is chain of amino acids Highly mobile Inactive Sequence Folded protein Precise shape Stable Highly ordered Active Structure Function depends on protein shape Specific associations Precise reactions Function

10 R: Flow of biological Information

11 R: How to solve a structure Fourier synthesis Electron density Phases + Experimental methods = back to lab Use known structures (molecular replacement) 3D structure Diffraction intensities

12 R: Resulting publication in Science

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14 R: Access Statistics: 23/8/2007 to 1/12/2007 Views: 918 total –257 from library staff –152 from other Monash addresses –509 from non-Monash addresses Downloads: 498 total –87 from library staff –62 from other Monash addresses –349 from non-Monash addresses

15 R: Why he cares about data Raw data are sacred Data validation for reviewers and by peers His data are now safe and secure Store of examples for those doing methods development Some data cannot be processed by him; why not let others have a go?

16 C is for Challenges and Solutions Laboratory data management practice Institutional data management planning Sustainable storage provision Data curation across data stores Data in institutional repositories

17 C: Laboratory data management practice Challenge –Infrequent and deficient backup –No commitment to long-term preservation –Poor recording of metadata (descriptive/provenance) Solution –Embed IM professionals with research teams –Provide sustainable storage for backup –Improve laboratory data capture systems

18 C: Institutional data management planning Challenge –No systematic organisation-wide approach –No way of engaging with researchers

19 S: Institutional forum to discuss issues Membership –Library –ITS –Records and Archives –Research Office –e-Research Centre Outputs –Policy and Plan (print trial, web production) –Outreach activities

20 S: Data Management Plan – objectives Assists both researcher and institution Is completed at beginning of research project, updated as necessary –May become mandatory in future Captures some technical, access and descriptive metadata at the beginning of research project Is not onerous Delivers visible benefits Assists in providing complete research data solutions

21 S: Data Management Plan – components Originators and owners of the data Description of project Metadata used (schema, standards) Types of data to be collected Volume of data (initial estimate) Retention requirements (guidelines provided) Format/s of and software used in creation and use of the data Access policies and provisions IP constraints Confidentiality requirements Storage, preservation and archiving of data

22 C: Sustainable storage provision Challenge –Need sustainable way to provide large (terabyte) amounts of storage for researchers –Make this more financially attractive than JBOD under desk Solution –Large Research Data Storage (LaRDS)

23 C: LaRDS requirements Addresses institutional and researcher needs Formulates a set of principles to guide cost modelling and sustainable funding options Assumes commitment to storage in perpetuity –or “as long as required”, whichever comes first ;-) Adopts a central storage model … –Centrally funded basic allowance, plus –Directly charged excess allowance … in parallel with decentralised storage 700 TB and growing

24 C: Different stores for different domains

25 C: Data in institutional repositories Challenge –Most IRs are designed for document objects –Many data objects are large >2QP2 produced 36GB of image data –HTTP download metaphor doesn’t scale Solution –Trialling both managed content and externally referenced content at present –Investigating custom disseminators on server

26 A is for Australian national developments

27 A: Australian e-Research Infrastructure Term ≈ Cyberinfrastructure National Collaborative Research Infrastructure Strategy (A$555M, 5 yrs) –15 research capabilities –and Platforms for Collaboration Platforms for Collaboration (A$75M, 4.5 yrs) –National Computation Infrastructure –Interoperation and Collaboration Infrastructure –Australian National Data Services

28 A: Australian National Data Service Monash University is leading a project to establish ANDS ANU and CSIRO to be other members of collaborative partnership Tasks to be distributed more widely Four platforms: –Frameworks (policy) –Utilities –Repositories –Researcher Practice

29 Q is for Questions! * Thanks to Dr Ashley Buckle and colleagues at Monash for the use of the protein crystallography slides and movies

30 Federating Data The Australian Repository for Diffraction ImageS – National activity to support communities of protein crystallographers Ideal place to hook into the eCrystals Federation –