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

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

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


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

Single colony

24 96 384

384 colonies

What next?


Technology Increased Speed & Data Generation


Manual Colony Selection Became Automated

Aseptic Robotic Automation

mRNA Microarrays [increased

#samples, increased sensitivity]


More Data Requires (More) Computers

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

1 Colony 24 Colonies 96 Colonies 384 Colonies

240 28800

115200

460800

Bacterial Colonies Tested

# o

f data

po

ints

ge

ne

rate

d


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/DMP (take a look at the DMP checklist)

http://www.lib.umn.edu/datamanagement

http://www.lib.umn.edu/datamanagement/DMP




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

http://www.dataone.org/plans

http://www.dataone.org/plans


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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