E-science involves large-scale collaborative research enabled by new technologies like high-speed networks and cheap data storage. It produces massive amounts of complex data from areas like climate modeling, particle physics experiments, biomedical research grids, and citizen science projects. This represents a major change for research that requires new infrastructure, expertise, and approaches. Universities like UVA are responding by establishing research computing support services in their libraries to help scientists with the computational and data aspects of e-science throughout the research lifecycle.

1. UNDERSTANDING THE
BIG PICTURE OF E-SCIENCE
Andrew Sallans
Head of Strategic Data Initiatives
University of Virginia Library
E-Science Bootcamp
Claude Moore Health Sciences Library, University of Virginia
4 March 2011

2. OUTLINE
 What it‟s all about
 Examples
 Implications
 UVA Libraries Response (Round 1)
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3. WHAT IT‟S ALL ABOUT (AROUND 1999)
"e-Science is about global collaboration in key areas
of science, and the next generation of
infrastructure that will enable it."
"e-Science will change the dynamic of the way
science is undertaken."
Dr Sir John Taylor
Director General of Research Councils,
Office of Science and Technology
United Kingdom
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Source: http://webscience.org/person/8.html

4. WHAT MADE THIS POSSIBLE?
 Internet/World Wide Web
 Faster networking (fiber, special research
networks, advances in grids)
 Better storage (higher capacity, faster access,
better reliability)
 Cheap storage (costs keep decreasing)
 Major funding initiatives
 Broader interest in collaboration
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5. SOME COMMON TERMS
 Computational science
 Scientific computing
 Research computing
 High-performance computing
 Cyberscience
 Cyberinfrastructure
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6. CLIMATOLOGY RESEARCH
Sources:
1) Climate Simulation on Cray XT5 “Jaguar” supercomputer, ORNL 6
(http://www.ornl.gov/info/ornlreview/v42_3_09/article02.shtml)
2) Cray XT5 “Jaguar” supercomputer, ORNL
(http://www.ornl.gov/info/ornlreview/v42_1_09/images/a05_p04_xt5_full.jpg)

7. LARGE HADRON COLLIDER AT CERN
 Circumference: 26,659 meters
 Magnets: 9,300
 Speed: protons move at
99.9999991% speed of light)
 Collisions/second: 600 million
 Data produced: equivalent to
100,000 dual layer DVDs per
year
 LHC Grid: tens of thousands
of computers around the world
used collectively to analyze
data (will take 15 years)
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Source: CERN website (http://cdsweb.cern.ch/record/975468/files/its-2006-003.gif?subformat=icon)

8. BIOMEDICAL INFORMATICS GRID (CABIG)
 Launched as test in 2004
 Adopted by over 50 NCI-designated cancer centers
 Focused on:
 Connecting scientists and practitioners through a
shareable and interoperable infrastructure
 Development of standard rules and a common
language to more easily share information
 Building or adapting tools for collecting, analyzing,
integrating, and disseminating information associated
with cancer research and care
Source: caBIG website, National Cancer Institute (https://cabig.nci.nih.gov/) 8

9. CITIZEN SCIENCE…THE SOCIAL SIDE
34,617,406 clicks done by 82,931 users!
Source: Zooniverse, Real Science Online (http://www.zooniverse.org/home) 9

10. IMPLICATIONS FOR RESEARCH
 Greater emphasis on technology
 Increase in interdisciplinary research and
collaboration
 Often bigger data, with far more complex
associated issues (storage, access, expertise,
funding, preservation, etc.)
 Need for innovative approaches and integration
into education/curriculum
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11. DATA TSUNAMI
IDC estimate of about 1.7 zetabytes (1 trillion terabytes) around 2011
….twice the available space
Source: 11
1) The Great Wave off Kanagawa, Katsushika Hokusai. Found on Wikipedia.
2) The Diverse and Exploding Digital Universe, IDC, May 2010 (http://www.emc.com/collateral/analyst-
reports/diverse-exploding-digital-universe.pdf)

12. BUT, NOT ALL DATA IS EQUAL….
Source: Long Tail, Wikipedia (http://en.wikipedia.org/wiki/The_Long_Tail) 12

13. CASE STUDY: UVA LIBRARIES RESPONSE
(ROUND 1)
 Collaboration established around 2005 through
discussions between ITC and Library, and
impetus of Frye Institute capstones.
 Research Computing Support services in need of
greater visibility, Library seeking ways to
support changes in scientific research, collocation
provides mutual benefits.
 In 2006, staff moved to Library locations
(Research Computing Lab & Scholars‟ Lab),
setup new service points and services.
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14. RESEARCH IN THE E-SCIENCE WORLD
 Heavy use of electronic information resources
 Work is predominantly done from a lab/office, not
in the Library
 Collaboration is fundamental, but don‟t always
know people in other domains
 Grad students are usually bringing new
technology/methods into the team (learning more
about grad students in a research study now)
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15. IDENTIFIED E-SCIENCE TRENDS
 Various components
 Computationally intensive science
 IT/software/infrastructure
 Collaboration
 Data
 Often intertwined with Open Access initiatives
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16. E-SCIENCE IN OTHER LIBRARIES
 Purdue University
 Focus on data curation
 IATUL Conference, June 2010
 University of Illinois – Urbana Champaign
 Focus on data curation
 Summer Institute on Data Curation
 Cornell University
 Metadata consulting services
 University of New Mexico
 Major DataONE grant
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17. RESEARCH COMPUTING LAB RESPONSE
 Aiming to provide support across the entire
scientific research data lifecycle
 Staff with expertise in:
 Data
 Quantitative data, statistics
 Modeling, visualization
 Scientific publishing
 Emphasis on consulting, not drop-off services
 Partnership with traditional librarians to help
ease transition to new support models
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18. RCL OUTREACH
University Community
 Speaker series 2006, 2007, 2008
 Research 2.0 Symposium
 Partnerships with courses, other units (ie.
MLBS)
 Short course series each semester
Library Community
 Panel at the ACCS Conference in 2007
 Poster at ARL/CNI Forum in 2008
 Poster at STS Section of ALA in 2009
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 Journal article in JLA in 2009

19. SAMPLE RCL CONSULTATIONS
 STS Undergrad Environmental Justice (2008)
 Development of technology solutions for empowering the
citizen scientist
 Web 2.0 tools, data collection/management
 Data analysis
 Economics Graduate Student (2008/2009)
 Airline flight price modeling
 Screen scraping, data collection/management
 Data analysis
 Mountain Lake Beetle Project (2009)
 Mobile data acquisition/collection solution
 Database development/management, programming
 Data analysis
 Archiving of dissertation data (2009)
 EVSC student, ModelMaker 4.0 data
 Biology student, IDL, Matlab, R code 19

20. SPECIFICS FOR MEDICAL CENTER
 At least 600 RCL support requests from Medical
Center from October „07 through December „09
 Medical Center patrons are heavy users of
computational software like Matlab, SAS,
LabView
 Increasing emphasis on collaboration
(translational research)
 Greater attention to open access (NIH policy)
 Growing interest in areas like image integrity
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21. TAKE-AWAYS
 This is the future
 Heavily growing space, lots of opportunity
 Requires big investment and commitment, the
biggest being training and priority alignment
 Libraries and institutions need to make decisions
on what to do and what not to do
 It‟s a culture change for both libraries,
institutions, and researchers
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22. COMING LATER….(ROUND 2)
 “Practical Applications of e-Science” in UVA
Libraries today
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23. QUESTIONS?
 Please feel free to contact me with questions:
 als9q@virginia.edu
 434-243-2180
 Twitter: asallans
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24. ADDITIONAL INFORMATION
 E-Science Talking Points for ARL Deans and
Directors, Elisabeth Jones, University of
Washington, October 2008
(http://www.arl.org/rtl/escience/)
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