Layne Johnson "e-Science at the Univeristy of Minnesota"

e-Science at the
University of Minnesota
e-Science Symposium
National Network of Libraries of Medicine
South Central Region
Texas Medical Center Library
Layne M. Johnson, Ph.D.
February 13, 2012

©2012 Regents of the University of Minnesota. All rights reserved.

History, Evolution & Current State
• Present my experiences as a researcher and show
how changes in research call for the expertise of
librarians and information specialists
• Review the experiences at Minnesota and the
roles that the University Library colleagues have
played
• Take a fresh look at the developing requirements
and advances in e-science to best prepare
ourselves and researchers to meet future needs


First, Let’s Agree…
• E-Science (or eScience) is computationally
intensive scientific research, typically
performed over distributed networks and
involving large amounts of data. Also, e-
science often involves collaboration.
• E-Science can be referred to as E-Research to
accommodate disciplines outside of the
sciences, e.g. the Digital Humanities.


E-Science
• …has often been called cyberinfrastructure in
the U.S.
• Nowadays, cyberinfrastructure is used less (at
least by me), and e-Science is used more
frequently.


Technology is the Major Impact on Science

• High-throughput
screening, sequencing
• Equipment generating
data 24/7
• Information increasing
logarithmically
• Communications,
networking
• Regulations, standards


Layne, the Bacteriologist


Screening for New Medicines

384 colonies

24 96 384

What next?

Single colony


Technology Increased Speed & Data Generation


Manual Colony Selection Became Automated

mRNA Microarrays [increased
Aseptic Robotic Automation #samples, increased sensitivity]


More Data Requires (More) Computers
# of data points generated
460800
500000

450000

400000

350000

300000

250000

200000
115200
150000

100000 28800
50000
240
0

1 Colony 24 Colonies 96 Colonies 384 Colonies

Bacterial Colonies Tested

Researchers & Computers Network to
Manage Data – Share, Store, Analyze


Reviewing our definition…

…computationally intensive scientific research,
typically performed over distributed networks
and involving large amounts of data, with a dash
of collaboration thrown in


Which Leads to the Next Part of the Story


Layne, the University of Minnesota
Person / Gopher / Frozen Guy
aka informationist, informaticist,
librarian, etc.


Research at University of Minnesota
• Twin Cities Campus
– >52,000 students, 3400 faculty, 150 graduate and
professional degrees
– Medicine to business, law to liberal arts, science
and engineering to architecture and agriculture
– ~$770M in sponsored research in 2011, $305M
from NIH
– 2010 NSF research ranking – 8th among top 20
public research universities in the US


Academic Health Center (AHC)


E-Science Landscape – 2006 - Present
• Libraries developed a model for assessing
research support – 2005-2006 – humanities,
social sciences
• 2006-2007 – analysis of science faculty and
graduate students re: discovery, use, &
management of data and information
• 2007 – ARL report – “Agenda for Developing E-
Science in Research Libraries” – W. Lougee
coauthor


E-Science Landscape – 2006 - Present
• 2007+ - virtual community development –
EthicShare, AgEcon
• 2007-2009 – Cyberinfrastructure Alliance –
University Libraries, IT, Office VP Research
• 2010 – data audits, Research Networking
implemented
• 2010+ - Data Management workshops - >300
faculty


Things We’ve Learned from Researchers

• Participants want
– help with Data Management Plans (80%)
– to share data with collaborators (84%)
– to use metadata services (70%)
– auto-backup data services (77%)
– long term access to data (76%)
– repositories for data on campus (e.g. GIS, 70%)


Researchers also indicate they’d like:
• to gain a better understanding of new data
management and collaboration tools (76%)
• to be able to not only identify experts, but
also core resources, like biorepository
samples, core centers of technology and
methods & equipment support
• to compete more aggressively for funding
opportunities


We used Charles Humphrey’s model of the
Research Life Cycle to help inform or
strategic direction for supporting e-science
at the University of Minnesota
(in the spirit of full disclosure, we modified
the model – and so should you)


The Research Life Cycle


We Identified Library Roles
• Content/Collection Development & Managing
Datasets
• Teaching and Learning
• Outreach
• Liaison Services
• Translational Science and Informatics Support
• Research Networking
• Leadership


Research Networking
• We first implemented open source Harvard
Profiles (UMN Profiles) - ~4,000 researchers
• Collaboration with Office VP Research,
Libraries, Colleges – we are implementing
SciVal Experts, Funding, Spotlight
– Got us at the table
– Is pushing us into ontology work, linked open
data, and the semantic web


Data Management Plans (DMP)
• Brief description of how primary
investigator will comply with funder’s data
sharing policy
• Largest funding agencies include NIH and NSF


Several DMP Tools Are Available
• A variety of data management resources have
been developed at by the University Libraries
the University of Minnesota. Check into them
at http://www.lib.umn.edu/datamanagement
• For DMPs, see specifically:
http://www.lib.umn.edu/datamanagement/D
MP (take a look at the DMP checklist)


DataOne (NSF) DMPs
• Several examples are provided at:
http://www.dataone.org/plans
• DMPTool provides guidance and resources for
your data management plan
– Create plans
– Meet funder requirements
– Step by step instructions
– Get data management advice


Where Our Work is Leading Us
• We realize that there are gaps in
understanding privacy and security issues
around data – especially in the health sciences
• We also have identified gaps in our
understanding of bench scientist (discovery,
T1, pre-human) needs
• We recognize an opportunity to transform
several roles to support research and data
needs


Research Support and Services Collaboratives

• Data Access Working Group
• Research Communities and Networks
• Digital Humanities

• We see the boundaries between the Academic
Health Center dissolving and a focus on
enterprise solutions expanding
• One example is the AHC Information Exchange

AHC Information Exchange
• Governance structure consisting of the SVP
AHC, SVP of Research, CTSA PI
• Working groups include informatics, research
studies, architecture
– Many groups request support from the libraries
– Identity (like ORCID, etc.)
– Metadata
– Classification


Future of e-science
• A positive development for libraries – it is
important for us to take the lead
– This is shown to be true from e-Science Institute
activities with ARL
• Need for open dialogue at all levels – local,
regional, national, global
• Opportunities abound – and can be leveraged
with existing resources and sound strategies


Acknowledgements
• University of Minnesota Health Sciences
Colleagues, particularly Andre Nault, Jonathan
Koffel, Linda Watson
• University Library colleagues, especially Lisa
Johnston, John Butler, Meghan Lafferty,
Wendy Lougee
• Health Informatics Colleagues and Clinical and
Translational Science Institute Colleagues
• Cooper Mark Edward Johnson


Layne Johnson "e-Science at the Univeristy of Minnesota"

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