Trailblazing in the Wilderness of Data Management

Trailblazing in the Wilderness of Data Management

Where are we going and how do we get there from here.

Stephanie WrightData Services CoordinatorUniversity of Washington Libraries

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AGENDA

• Definitions• Why venture out• Paths already taken

–Assessments of needs–Existing programs–Tools & resources

• Blazing your own trail

Montana State University – 21 June 2013

Definitions

• Data• Data Management• Big Data• Long Tail of Data• Acronyms

www.lib.washington.edu

Definitions


DATA

By data, we do not mean a synonym for information. We mean research data, that which is collected, observed, or created, for purposes of analyzing to produce original research results.

Research data may be created in tabular, textual, statistical, numeric, geospatial, image, multimedia or other formats.

(Adapted from DISC-UK DataShare Project, p. 16)

Definitions


DATA

Data can be produced from a variety of processes (e.g., observation, experimentation, simulation, derivation, compilation), represented in numerous forms and stored in many digital formats (e.g., ASCII, PDF, SPSS, Excel, TIFF, Java, FITS, CIF, ZVI) The scope of this definition includes data from disciplines in the sciences, social sciences, and

humanities.

(Adapted from MIT Libraries, “What is Data?”, 2009)

Definitions


DATA MANAGEMENT

Pertains to the collection, cleaning, storage, sharing, access, disposal, preservation and/or archiving of research data.

(Adapted from University of North Carolina, Research Data Stewardship Report, 2012)

Definitions


BIG DATA

• Volume• Velocity• Variety

25 Definitions of Big Data: http://www.opentracker.net/article/25-definitions-big-data

– Now over 30 definitions

http://www.opentracker.net/article/25-definitions-big-data

http://www.opentracker.net/article/25-definitions-big-data

Definitions


LONG TAIL OF DATA

Image credit: disruptormonkey.typepad.com

Acronyms


• RDM – Research Data Management

• IR – Institutional Repository

• DR – Data Repository

• DMP – Data Management Plan

Why Venture Out

• Funding agencies• Universities• Researchers• Libraries


Image credit: National Park Service, Yellowstone photo collection, (http://www.nps.gov/features/yell/slidefile/mammals/wolf/Images/15314.jpg)


Funding Agencies


• 1998: NSF• 2003: NIH• 2011: NSF• 2013: NSF, OSTP, OMB, NIH

Universities


• Competitiveness• Reduce duplication of effort• Preserve the research record of the

institution• Encourage innovation & discovery

Researchers


• Verifiability & reproducibility• Increased citation rates for

publications– (Piwowar et al, 2007)

• Preservation of individual scholarly record

• Save time by planning early

Libraries


•Digital Preservation Network (DPN)

“The Digital Preservation Network is being created by research-intensive universities to ensure long-term preservation of the complete digital scholarly record.”

http://d-p-n.org/

http://d-p-n.org/

Libraries


NSF Proposal & Award Policies & Procedures Guide (Oct 2012)

“Instructions for preparation of the Biographical Sketch have been revised to rename the "Publications" section to "Products" .... (P)roducts may include, but are not limited to, publications, data sets, software, patents, and copyrights.”

Paths Already Taken

• Assessments• Existing programs• Tools & Resources


Image credit: John W. Ridge (http://commons.wikimedia.org/wiki/File:Yellowstone_Trail_Map.jpg)

Assessments


• UNC (2012) “Research Data Stewardship Report”

• University of Colorado Boulder (2012) “Research Data Management @ UCB”

• Purdue “Data Curation Profiles Directory” (http://docs.lib.purdue.edu/dcp/)

• More: Georgia Tech, Cornell, Houston, Oregon….

http://docs.lib.purdue.edu/dcp/

http://docs.lib.purdue.edu/dcp/

Findings


• Researchers use a wide variety of data types – across disciplines

• Most researchers rely on themselves for data management

• Researchers want to maintain control of their data

• Many are unaware of existing services

• They want tools that work in existing workflows

What’s Needed


• Creating & maintaining DMPs• Best practices guidance all along

lifecycle• Storage

– Short-term access– Long-term access– Backup– Versioning– Security

• Metadata creation

Existing Programs


• Cornell– Research Data Management Service

Group• Sr VP for Research and University

Librarian• Faculty Advisory Board

– 9 faculty across disciplines– OSP & Office of Research Integrity &

Assurance

• Management Council– 2 librarians, 2 faculty, 2 IT, 1 research institute

Existing Programs


• Purdue– D2C2: Distributed Data Curation

Center• Executive Committee

– Dean of Libraries, VP of Research & VP of IT

• Library: consulting & metadata support• IT: storage & research computing support

Existing Programs


• University of Washington– Data Services Program (1.5 FTE)

• Data Services Coordinator• Data Services Communications &

Curriculum Libn

– Data Services Team (10 members)– Partnerships

• Research Centers (eSci, CSDE, IHME)• Office of Research (OSP)• Campus IT• iSchool

Tools & Resources


• Data Mgmt Planning: DMPTool• Metadata & Sharing: DataUP• Sharing & Storage: DataBib• Citation: EZID• Best Practices: DMVitals

Blazing Your Own Trail


Image credit: Michigan State University Department of History, HST 321: History of the American West (http://history.msu.edu/hst321/files/2010/07/colter.jpg)


• Identify needs• Consider potential partners• Scope

– Disciplines– Specific areas of the data lifecycle

• Determine priorities– New services? Enhance existing?

Market existing?

Where do you want to go?


• Objective L1– Assess and improve where needed,

student learning of critical knowledge & skills

• Objective D1– Elevate the research excellence and

recognition of MSU faculty• D1.2

• Objective D2– Enhance infrastructure in support of

research, discovery and creative activities

MSU Strategic Plan


• Support for active data storage

• Data security guidance• Backup services• Development of tools that

can be inserted into existing workflows

Campus IT


• Guidance on legal / ethical considerations

• Incorporate DM planning into grant submission process

• New faculty data management orientations

Office of Research


• Market and provide access to existing RDM resources

• Provide learning opportunities on RDM best practices

• DMP consultation• Storage (final)• Metadata consultation

Libraries


• University policy on data management

• Integrate RDM activities into T&P process

• Consider campus policy on open data

University

Questions

Thank you!Stephanie Wright

Data Services [email protected]

@shefw

http://guides.lib.washington.edu/swright

Data Management Guidehttp://guides.lib.washington.edu/dmg

ResearchWorks Data Serviceshttp://researchworks.lib.washington.edu/rw-data.html

Trailblazing in the Wilderness of Data Management

Education

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