The Role of Libraries in the Context of e-Science

1. The Role of Libraries in the Context of e-Science Dr Anne E Trefethen Oxford e-Research Centre Anne.trefethen@ierc.ox.ac.uk

2. A Definition of e-Science ‘e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.’ John Taylor Director General of Research Councils Office of Science and Technology, 2001

3. UK e-Science Programme Director’s Awareness and Co-ordination Role Director’s Management Role Generic Challenges EPSRC (£15m) £16.2m, DTI (£15m) Pilot Application Programme PPARC (£26m) £31.6m BBSRC (£8m) £10.0m MRC (£8m) £13.1m NERC (£7m) £8.0m ESRC (£3m) £10.6m EPSRC (£17m) £18.0m CLRC (£5m) £5.0m Research Councils (£74m),£96.3m DTI (£5m) Collaborative projects Industrial Collaboration

4. e-Science Goals • to enable new forms of science that are • distributed • collaborative • multi-disciplinary • information-intensive • data-intensive • to use information technology to • leverage data as a form of science capital • to manage the “data deluge” • improve access to scientific information

5. Powering the Virtual Universehttp://www.astrogrid.ac.uk(Edinburgh, Belfast, Cambridge, Leicester, London, Manchester, RAL) AstroGrid Slides courtesy of Nick Walton, Cambridge Multi-wavelength showing the jet in M87: from top to bottom – Chandra X-ray, HST optical, Gemini mid-IR, VLA radio. AstroGrid will provide advanced, Grid based, federation and data mining tools to facilitate better and faster scientific output. Picture credits: “NASA / Chandra X-ray Observatory / Herman Marshall (MIT)”, “NASA/HST/Eric Perlman (UMBC), “Gemini Observatory/OSCIR”, “VLA/NSF/Eric Perlman (UMBC)/Fang Zhou, Biretta (STScI)/F Owen (NRA)” National Centre for Text Mining

6. SWIFT satellite observes gamma ray burst Gamma Ray Bursts Image from ESO D. Ducros, ESA Image + IRIS data Interaction with observatory pipe- lines Localise GRB alert in minutes – as fade rapidly. Collate data from multiple telescopes over months - meta data issues Large computational photometric redshift calcs on multi-λ > gives distance Cross reference multi-λ data – ID pre-cursor and or environment Compare against SN light curves – bump shows eveidence for a SN in the GRB (Price et al, 2002) Reprocessing of ionospheric STP data change coords from earth to celestial National Centre for Text Mining

7. Dark Matter + Large Scale Structure X-ray cluster: Chandra X-ray (Mullis) overlaid on a deep BRI image (Clowe & Luppino). Image from ESO Multi-TB λCDM models, e.g. Millennium Sim Automatic cluster finding techniques Multiple large image sources: registration & association Generate Shear Maps c.f. CDM models > DM distribution with redshift Remove stars correlate gals with z Source ID from multiplexed spectral data Colour-Colour relationships classification in multi-phase space National Centre for Text Mining

8. Some facts on Astronomy data • Virtual observatories • Many national virtual observatories containing data at different wavelengths. Estimated • US NVO project alone will store 500 Terabytes/year • Laser Interferometer Gravitational Observatory (LIGO) generates 250 Terabytes/year • VISTA, Visible and infrared survey telescope estimated to generate 250 Gigabytes of raw data/night – 10 terabytes of stored data/year. • Together with data analysis need to combine with previously published knowledge on that astronomical time/space events

9. myGrid:Directly Supporting the e-Scientist myGrid slides courtesy of Carole Goble Partners Manchester, EBI, Southampton,Nottingham, Newcastle, Sheffield AstraZenecaGlaxoSmithKline Merck KGaA Epistemics LtdGeneticXchangeNetwork Inference IBM SUN Microsystems http://mygrid.man.ac.uk

10. (courtesy of Carole Goble, Manchester) myGrid Project • Imminent ‘deluge’ of genomics data • Highly heterogeneous • Highly complex and inter-related • Convergence of data and literature archives

11. People Provenance record of workflow runs Literature Notes Data in and out Services used An in silico experiment = a web of interconnected information and components Provenance of the workflow template. Related workflows. Ontologies describing workflows (courtesy of Carole Goble, Manchester)

12. The eBank Project • Building links between e-research data, from the CombeChem project, with scholarly communication and other on-line sources • Investigating the role of aggregator services in linking data-sets from Grid enabled projects to open data archives contained in digital repositories through to peer-reviewed articles as resources in portals • JISC-funded project led by UKOLN in partnership with the Universities of Southampton and Manchester (eBank slides courtesy of Liz Lyon and Jeremy Frey)

13. Comb-e-Chem Project Video Simulation Properties Analysis StructuresDatabase Diffractometer X-Raye-Lab Propertiese-Lab Grid Middleware (eBank slides courtesy of Liz Lyon and Jeremy Frey)

14. Goals of e-Bank Project • Provide self archive of results plus the raw and analysed data • Links from traditionally published work provides the provenance to the work • Disseminate for “Public Review” – raw data provided so that users can check themselves • Avoid the “publication bottleneck” but still provide the quality check (eBank slides courtesy of Liz Lyon and Jeremy Frey)

15. Crystallographic e-Prints • Direct Access to Raw Data from scientific papers Raw data sets can be very large and these are stored at National Datastore using SRB server (eBank slides courtesy of Liz Lyon and Jeremy Frey)

16. e-Bank: Some Comments • Data as well as traditional bibliographic information is made available • Can construct high level search on data • aggregate data from many e-print systems • Build new data services • Will extend to provision of real spectra - rather than very reduced summaries - for chemistry publications (eBank slides courtesy of Liz Lyon and Jeremy Frey)

17. Grid E-Scientists collaboration storage & processing data & metadata Current E-Science Focus: Experimentation Virtual collaborations for large-scale experimentation & analysis E-Experimentation (eBank slides courtesy of Liz Lyon)

18. Grid E-Scientists 1 Experimentation & Analysis Cycle E-Experimentation (eBank slides courtesy of Liz Lyon)

19. Grid Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies 2 Publication & Preservation Cycle E-Scientists E-Experimentation (eBank slides courtesy of Liz Lyon)

20. Grid Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies Research Cycleaccess & impact 3 Digital Library E-Scientists E-Scientists E-Experimentation (eBank slides courtesy of Liz Lyon)

21. Virtual Learning Environment Grid Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies Undergraduate Students Digital Library Graduate Students E-Scientists 4 Learning Cycletraining and developing tomorrow’s e-scientists E-Scientists E-Experimentation (eBank slides courtesy of Liz Lyon)

22. Virtual Learning Environment Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies Undergraduate Students Digital Library Graduate Students E-Scientists E-Scientists E-Scientists Grid 5 E-Experimentation Entire E-Science CycleEncompassing experimentation, analysis, publication, research, learning (eBank slides courtesy of Liz Lyon)

23. Role of publications in science • Product of research • Cumulative, historical record of science • Input to research • Value chain: Network of documents linked via citations (courtesy of Christine Borgman)

24. Publication changes • Changes much broader than just the libraries • Nature of publishing • Cycle of authoring, publication, access Drivers • Technology • Economics • Social and Legal

25. Data Publishing Databases, notably in biology, are replacing (paper) publications as a medium of communication • Built and maintained with a great deal of human effort • Often do not contain source experimental data, sometimes just annotation/metadata • Borrow extensively from, and refer to, other databases • Researchers are now judged by databases as well as (paper) publications • Upwards of 1000 (public databases) in genetics • Integration of literature and data analysis of increasing importance - linking bio-database to literature, using publishers to check, complete or complement contents of such databases

26. Digital Curation? • ‘In next 5 years e-Science projects will produce more scientific data than has been collected in the whole of human history’- Tony Hey • In 20 years can guarantee that the operating and spreadsheet program and the hardware used to store data will not exist • Research curation technologies and best practice • Need to liaise closely with individual research communities, data archives and libraries

27. Generic Issues • Data Deluge from e-Science projects requires technologies to facilitate discovery, analysis, curation of data • Sheer volume of text published and new results appearing, is impossible for researchers to read and correlate – text mining • Effective automated processing required research, locate, gather and make use of knowledge encoded electronically in available literature

28. What data deserve to be permanently accessible? • What are the scientific criteria for preservation? • What is the equivalent of peer review for data? • Whose data do you trust? • What data will be re-used? • How much to invest? • Who will add the value?

29. Digital Curation Centre • Actions needed to maintain and utilise digital data and research results over entire life-cycle • For current and future generations of users • Digital Preservation • Long-run technological/legal accessibility and usability • Data curation in science • Maintenance of body of trusted data to represent current state of knowledge • Research in tools and technologies • Integration, annotation, provenance, metadata, security….. (www.dcc.ac.uk)

30. The hybrid library ‘The dominant user view of a library is of a physical space. But libraries are services which provide organised access, to the intellectual record, wherever it resides, whether in physical places or scattered digital information spaces. The ‘hybrid’ library of the future will be a managed combination of physical and virtual collections and information resources.’ Reg Carr, Oxford University

31. Conclusions • Publication of data and “paper” becoming integrated in the digital scholarly research cycle • Libraries will move further to the “hybrid” model – Institutional repositories • e-Science brings with it the data deluge – needs for data management and curation skills • e-Scientists also need library training in discovery and access • Have implicitly touched on Open Access but as policies begin to apply to data as well as publication research outputs, then the above will be even more so.

32. Acknowledgements With special thanks to Tony Hey, Carole Goble, Reg Carr, Jeremy Frey, Liz Lyon, Chris Borgman and Nick Walton