ola super conference 2014: session 413 r 03/02/2014...

OLA�Super�Conference�2014:�Session�413�ͲDeveloping�a�Research�Data�Management�Service�ͲͲ A�Case�Study

03/02/2014

Queen's�University�Library�Ͳ Jeff�Moon�ͲData�&�Government�Information�Centre 1

Jeff�MoonData�&�Government�Information�LibrarianAcademic�Director,�Queen’s�Research�Data�Centre

Developing�a�Research�Data�Management�Service�– a�Case�Study

OLA�Super�Conference�Thursday,�Jan�30,�2014�ͲͲ Session�413

3.�Just�do�it

2.�Educate

1.�Plan,�build,�test,�refine

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Developing�aResearch�Data�Management�Service

What�is�‘Research�Data”?

That�which�is�collected,�observed,�or�created�in�a�digital�form,�for�purposes�of�analysing�to�produce�original�research�results.

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NumericVideoImagesSoundText

&�more…

What�is�‘Research�Data�Management�(RDM)”?

It�is�what�we�do�with�research�data�to�ensure�it�is:CompleteAccurate

WellͲdocumentedCompliant�with�ethics�standards

OpenIn�preservationͲfriendly�format(s)

RDM�&�the�Data�Life�Cycle

Source:�http://www.ucl.ac.uk/ich/researchͲich/mrcͲcech/images/data/lifeͲcycle/researchͲdataͲlifeͲcycle.jpg?hires

TriͲCouncil�Funding�Agencies

Journal�Publishers

Evolving�Role�of�Libraries

National�and�International�Initiatives

What�is�driving�RDM?


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ARL/DLF�EͲScience�Institute�

QUL�Research�Data�Working�

Group

CARL�Research�Data�Management�Workshop

Growing�awareness�among�researchers

Local�drivers… Research�Data�Management1.�Plan,�build,�test,�refine

Research�Data�Management�3Ͳyear�plan

Storage/Archiving�options

Documentation�(metadata)�&��licensing


IntroductionI.�Current�Data�ServicesII.�Drivers�and�TrendsIII.�Data�Services�in�2016IV.�Partnerships�and�CollaborationsV.�Implementation�PrinciplesVI.�Advocacy,�Outreach,�Promotion�and�EducationVII.�StaffingVIII.�Evaluation�and�Assessment

External�influences…

Recent�TriͲCouncil�consultation�paperOctober�2013

Toward�a�Policy�Framework�for�Advancing�Digital�Scholarship�in�Canada

http://www.sshrcͲcrsh.gc.ca/news_roomͲsalle_de_presse/latest_newsͲnouvelles_recentes/big_data_consultationͲdonnees_massives_consultationͲeng.aspx

Capitalizing�on�Big�Data…

Toward�a�Policy�Framework�for�Advancing�Digital�Scholarship�in�Canada…�3�main�goals:

Establishing�a�culture�of�stewardship

Coordination�of�stakeholder�engagement

Developing�capacity�and�future�funding�parameters

Establishing�a�Culture�of�Stewardship�

a.�Require�that�all�grant�applications�include�specific�data�management�plans including�costs�of�data�collection,�analysis,�and�preservation

b.�Definition�of�specific�elements�of�data�plans�that�will�be�considered�by�reviewers�in�the�assessment�of�funding�applications;�

c.�Guidelines�indicating�which�data�must�be�preserved�and�in�what�formats;�and�for�selecting�suitable�data�repositories;�

d.�Consolidated�open�access�policies�and�guidelines


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Coordination�of�stakeholder�engagement

Ensure�involvement�of�all�stakeholders�in�the�development�of�a�national�digital�infrastructure�

ecosystem for�research

Developing�capacity�and�future�funding�parameters

Evaluate�funding�parametersBalance�the�roles�of�national,�provincial�and�

institutional�stakeholders�Collaborate�in�the�development�a�number�of�

worldͲclass�centres specializing�in�data�management

Enhance�networks�and�infrastructureSupport�skills�development�and�graduate�and�

researcher�training

Research�Data�Management1.�Plan,�build,�test,�refine




Investigate�archiving�options

DisciplineͲspecific�archives


Weigh�features�&�benefits�of�each…

Level�of�access�control• Specific�researcher/researchers• Your�university?• Ontario�Universities• WorldAbility�to�provide�IPͲbased�accessAbility�to�support�data�


DisciplineͲspecific�archives


Features

Weigh�the�features�&�benefits�of�each…

Local�Control

Flexible�access�control

DataͲspecific�Metadata

Online�Statistical�Analysis�Tools

Powerful�Search�Features

UserͲmediated�Deposit

Archive�tailored�to�discipline

yes no no no no no no

yes no yes yes no no no

yes yes yes some yes yes flexible

no no yes yes yes nosomewhat�flexible

? ? yes ? ? ? yes


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Web�site�topics�mapped�onto�our�evolving�Data�Management�workflow1.�Plan,�build,�test,�refine…

Map�out�your�workflow�and�staffing�needs…

Research�Data�Management1.�Plan,�build,�test,�refine




Metadata

“Data�about�data”

• Documentation�in�standardized and�structured form• Ideally�computerͲready• Explains your�data:�origin,�purpose,�time�reference,�geographic�location,�creator,�access�conditions�and�terms�of�use�of�a�data�collection.

Researchers�are�in�the�best�position�to�answer�questions�about�their�data!

http://www.dataenthusiast.com/wpͲcontent/uploads/2011/10/meta_data_standard_transmission1.jpg

Metadata�Standards


Metadata�Concept�Map�by�Amanda�Tarbet is�licensed�under�a�Creative�Commons�AttributionͲNonCommercialͲShareAlike 3.0�Unported License.


http://androvm.org/blog/exͲbuildroid/files/2012/01/Build.jpg

2.�EducateÆ RDM�web�site1.�Plan,�build,�test,�refine


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• Overview• Writing�a�Data�Management�Plan• Funding�Agency�Guidelines• Metadata• Data�Repositories�&�Archives• Citing�Data• Best�Practices�in�Data�Management• Library�as�Data�Partner• QUL�Research�Data�Archive


http://guides.library.queensu.ca/rdm

All�of�this�depends�upon�sufficient�staffing�and�expertise.

Strategic�hiring/retraining,�&�

Ongoing�training/professional�development



http://www.fastcoexist.com/multisite_files/coexist/imagecache/960/1280Ͳflesky.jpg

Writing�a�Data�Management�PlanFunding�Agency�Guidelines

MetadataBest�Practices�in�Data�ManagementCiting�Data

Web�site�topics�mapped�onto�our�evolving�Data�Management�workflow

QUL�Research�Data�ArchiveData�Repositories�&�Archives

1.�Plan,�build,�test,�refine…

Start�thinking�about�COLLECTION�

DEVELOPMENTdecisions


http://www.thesmokingtire.com/wpͲcontent/uploads/2010/05/DecisionsͲdecisionsͲJunͲ07.jpg



Collection�Development


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Manageability• Size�of�file(s)

• How�clean�is�the�data?

• How�complete�and�clear�is�the�documentation

• How�familiar�is�the�RDM�service�with�the�data�‘discipline’?�Can�sense�be�made�of�the�data?

• What�format�is�the�data�in?�Outdated?�Open/archival?��Proprietary?�Tied�to�specific�hardware/software?

• Restricted/Private?�Need�for�anonymization/deͲidentification


Value• Provenance�–

Undergraduate?�Graduate�PhD?�Masters?�PostͲDoc?�Established�researcher/research�team?�External�agency?�

• Reputation�of�researcher(s)• Potential�for�future�use?• Potential�impact�of�future�use?• Reproducibility?�• Cost�of�producing�original�data• Scope�of�data�– local,�small�sample;�provincial/national,�large�

sample;�international?



Collection�Development

Seminars,�courses,�conferences…��

Outreach:�Office�of�University�Research,�others

Targeted�marketing,�wordͲofͲmouth

2.�EducateÆ Communicate

http://scriptcat.files.wordpress.com/2012/03/research1.jpghttp://authorjenniferchase.files.wordpress.com/2012/01/bullseye2.jpg

Liaison�Librarians

2.�EducateÆ Enlist�help…

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• Campus�IT• Office�of�University�

Research• Other�Libraries

3.�Just�do�it

http://timemanagementninja.com/wpͲcontent/uploads/2011/12/JustͲDoͲItͲNow.jpg


03/02/2014


‘If�you�build�it,�they�will�come’

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But�be�prepared�to�knock�on�doors,�make�calls,�and�send�

emails…

You�may�face�rejection…�

http://pestcemetery.com/wpͲcontent/uploads/2010/08/PictureͲ3.png

…�but�don’t�let�that�stop�you!

Projects�at�Queen’s…

Potential Canada's �Waste�Flow�ProjectPotential Digitized�Dinosaur�BonesPotential Canadian�Institute�for�Military�and�Veteran�Health�Research�(CIMVHR)Potential Effects�of�Music�on�MilitaryPotential Sleep�LabPotential Ending�Bullying,�Health�Canada �data?Potential Mental �Health�Ͳ�Reducing�StigmaPotential Mining�dataPotential Ethnicity�and�Democratic�GovernancePotential SPEG�Ͳ�Social�Program�Evaluation�GroupPotential KinesiologyPotential Business �Ͳ�Marketing�dataPotential Frontenac�Arch�Biosphere�ReservePotential Canadian�Suburbs �Ͳ�SURPPotential Queen's �Biology�Station�Ͳ�Data �ArchivePotential South�African�Migration�ProjectPotential Social�Environments,�networks,�and�health

The�Globalization�of�Personal�Data�Project

Studied�implications�of�surveillance�and�personal�data�in�nine�countries:�Canada,�U.S.A.,�France,�Spain,�Hungary,�Mexico,�

Brazil,�China,�and�Japan.�

Key�element�of�success�was�a�‘data�manager’�on�the�project.

QSpace Dataverse ODESI

Cranial�Nonmetric�Trait�Database

27�separate�databases�in�Borland�Paradox�

format�on�an�older�vintage�

PC

Used�“R”�to�stack�the�files.

Reviewed,�refined,�&�

enriched�coding�scheme

Reviewed,��refined,�&�enriched�

documentation

QSpace Dataverse

Whenever�possible…

http://www.actbucks.org.uk/images/research.jpg

Catch�them�early…


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The�Data�Life�Cycle

Source:�http://www.ucl.ac.uk/ich/researchͲich/mrcͲcech/images/data/lifeͲcycle/researchͲdataͲlifeͲcycle.jpg?hires

Catch�them�early…

http://larshagberg.files.wordpress.com/2012/05/lth102.jpghttp://micheleͲnorris.com/wpͲcontent/uploads/2012/02/writingͲpencil.jpg

GOAL:�Archived�Data�&�

Documentation

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GOAL:�Archived�Data�&�

Documentation

Catch�them�early…Some�final�insights…

http://www.therenegadewriter.com/wpͲcontent/uploads/2011/09/lightbulbs.jpg

Manage�expectations

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Manage�expectations

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Don’t�oversell�your�service…

http://rookery.s3.amazonaws.com/1031000/1031212_78c6_1024x2000.jpg

Strive�for�elegant�solutions

Work�with�what�you’ve�got…

Be�prepared�for�casualties…

http://laughingsquid.com/wpͲcontent/uploads/starͲtrekͲexpendability.jpg

Put�yourself�out�there…

http://25.media.tumblr.com/tumblr_m6ag0g0sXF1rqazxao1_400.jpg

Contact�informationJeff�Moon�–Data�and�Government�Information�LibrarianAcademic�Director,�Queen’s�Research�Data�[email protected]

ola super conference 2014: session 413 r 03/02/2014...

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