Does trust matter? Ixchel M. Faniel, Ph.D. OCLC Elizabeth Yakel, - - PowerPoint PPT Presentation

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Does trust matter? Ixchel M. Faniel, Ph.D. OCLC Elizabeth Yakel, - - PowerPoint PPT Presentation

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Research Forum at the 75th Annual Meeting of the Society of American Archivists August 22, 2011 - August 27, 2011 Chicago, IL Does trust matter? Ixchel M. Faniel, Ph.D. OCLC Elizabeth Yakel, Ph.D. Nancy McGovern, Ph.D. Kathleen Fear Morgan

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Does trust matter?

Ixchel M. Faniel, Ph.D.

OCLC

Elizabeth Yakel, Ph.D. Nancy McGovern, Ph.D. Kathleen Fear Morgan Daniels Adam Kriesberg

Universit y of Michigan

Research Forum at the 75th Annual Meeting of the Society of American Archivists August 22, 2011 - August 27, 2011 Chicago, IL

This project is possible with funding from the Institute of Museum and Library Services.

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Does Trust Matter? 2

Agenda

Dissemination Information Packages for Information Reuse (DIPIR) Proj ect Inter-university Consortium of Political and S

  • cial Research

(ICPS R) S urvey Audit and Certification

  • f Trustworthy Digital

Repositories (IS O/ TRAC)

  • Mot ivat ion
  • Research Quest ions
  • Research Met hods
  • Mot ivat ion
  • Research Quest ions
  • Research Met hods
  • Overview of At t ribut es
  • Example Mapping
  • Example Hypot heses
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THE DIPIR PROJECT

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DIPIR Proj ect

Nancy McGovern ICPS R Ixchel Faniel OCLC Eric Kansa Open Context William Fink UM Museum

  • f Zoology

Elizabeth Y akel UM S chool

  • f

Information

Research Team

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Does Trust Matter? 5

Research Motivation

Two Maj or Goals 1. Bridge gap between data reuse and digital curation research 2. Determine whether reuse and curation practices can be generalized across disciplines

Data reuse research Digital curation research Disciplines curating and reusing data

Our interest is in this overlap.

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Does Trust Matter? 6

Research Questions

1. What are the significant properties of data that facilitate reuse by the designated communities at the three sites? 2. How can these significant properties be expressed as representation information to ensure the preservation of meaning and enable data reuse?

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Research Methodology

Phase 1: Proj ect S tart up Phase 2: Collecting & Analyzing User Data across the Three S ites Phase 3: Mapping S ignificant Properties as Representation Information

Oct 2010 – Jun 2011 May 2011 – Apr 2013 Sep 2012 – Sep 2013

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ICPSR SURVEY

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Findings from digital curation literature

  • S

uggest significant properties that provide “ reliable, long-term access to managed digital resources”

(Trusted Digital Repositories: Attributes and Responsibilities 2002 )

  • Funct ionalit y, relat ionships, appearance (Coyne et al. 2007)
  • Look and feel (Hedstrom et al., 2006; Matthews et al. 2009)
  • Comput ing environment and usage (Morrissey 2010)
  • Purpose and use (Ashley et al. 2008)
  • Interest in determining the range of significant

properties a trusted repository might have to accommodate

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Findings from data reuse literature

  • S

uggest significant properties that help users understand the data and whom and what to trust

  • Dat a cleaning & manipulat ion (Carlson & Anderson 2007)
  • Dat a collect ion met hods (Faniel & Jacobsen 2010)
  • Ident it y of dat a collect or (Knorr Cet ina 1999; Van House 1998;

2002)

  • S

elect ion and calibrat ion of dat a collect ion inst rument s (Wallis et al. 2007)

  • Qualit y checks (Carlson & Anderson 2007; Zimmerman 2003)
  • Interest in determining trustworthiness of data

producers and an understanding their actions

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Does Trust Matter? 11

Research Questions

  • What are the significant properties of data that

facilitate reuse by the designated communities at ICPS R?

  • What differences do the attributes of a trusted digital

repository (TDR) make to researchers using data from that repository?

  • What at t ribut es of TDRs as out lined in TRAC do researchers care

about ?

  • How do t heir percept ions about reposit ories influence t heir

propensit y for dat a reuse?

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Research Methods

  • Outline concepts in reuse & curation literatures
  • Map TDR attributes in IS

O TRAC to concepts

  • Develop hypotheses –

In progress

  • Operationalize concepts –

In progress

  • Administer survey to ICPS

R dataset users – Fall 2011

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Comparison of Literatures

Curation Literature Reuse Literature Concepts Trusted data repository Trusted data producer Repository context perceptions of trustworthiness of source Primarily data-centered Primarily user-centered User context characteristics of user Identifying & maintaining data context that allows data to be rendered

  • ver the long term

Identifying & maintaining data context that allows data to be interpreted

  • ver the long term

Data context significant properties of data consistently accessible and supported accessed and supported at producer discretion Delivery context ways data are accessed and supported Both literatures agree that decisions to reuse are “ not by metadata alone… ”

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ISO TRAC MAPPING

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Overview of ISO TRAC Areas of Focus1

  • Organizational Infrastructure
  • “ charact erist ics of t he reposit ory organizat ion t hat affect

performance, account abilit y, and sust ainabilit y.” (p. 9)

  • Digital Obj ect Management
  • “ reposit ory funct ions, processes, and procedures needed t o

ingest , manage, and provide access t o digit al obj ect s for t he long t erm.” (p. 21)

  • Infrastructure & S

ecurity Risk Management

  • “ adequacy of t he reposit ory’ s t echnical infrast ruct ure and it s

abilit y t o meet obj ect management and securit y demands of t he reposit ory and it s digit al obj ect s.” (p. 43)

1Definitions from Trustworthy Repository Audit and Certification: Criteria & Checklist, 2007

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Summary of ISO TRAC Mapping

  • 63 attributes were mapped
  • 43 related to repository context
  • 20 related to data context
  • 11 related to delivery context
  • 20 related to user context
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DATA CONTEXT

S ignificant properties of data

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Example of Data Context ISO TRAC 4.2.5.2

ISO TRAC p. 4-11 “ The repository shall have tools or methods to determine what Representation Information is necessary to make each Data Obj ect understandable to the Designated Community.” Quote from CBU07

“ [… ], like t he quest ions on abort ion, t hey have changed

  • ver t ime. And t hey're doing

experiment s t o figure out bet t er wordings, so like half

  • f t he sample got one

quest ion, and t he ot her half

  • f t he sample got a different

quest ion. S

  • t he codebook is

like your guide t o all of t hat [… ]”

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DELIVERY CONTEXT

Ways data are accessed and supported

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Example of Delivery Context ISO TRAC 4.5.1

ISO TRAC p. 4-23 “ The repository shall specify minimum information requirements to enable the Designated Community to discover and identify material of interest.” Quote from CBU08

“ [… ] like wit h t he religious affiliat ion dat a, I've looked and looked [… ] but [… ] I don't even t hink I found a good t opical subj ect cat egory [… ] S

  • it 's definit ely helpful when

t hings are organized t hrough different cat egories t hat you can search t hrough for t opics."

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REPOSITORY CONTEXT

Perceptions about the trustworthiness of source

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Example of Repository Context ISO TRAC 4.2.9

ISO TRAC p. 4-15 “ The repository shall provide an independent mechanism for verifying the integrity

  • f the repository

collection/ content.” Quote from CBU03

“ S

  • t here’ s like a [reposit ory

t hat ] has a bad [… ] hist ory

  • basically. It was very biased
  • n how t hey developed t heir

met hodology and such. And t hen t hey said t hey correct ed what t hey did like recent ly or what ever, but now t hat hist ory creat es a hist ory of bad reput at ion. I don’ t know anyone t hat uses [t he reposit ory].”

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USER CONTEXT

Characteristics of user

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Example of User Context ISO TRAC 3.3.1

ISO TRAC p. 3-5 “ The repository shall have defined its Designated Community and associated knowledge base(s) and shall have these definitions appropriately accessible.” Quote from CBU09 “ Because I am so novice in these areas, I would heavily value the opinions

  • f like professors that

knew more than me [… ]”

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NEXT STEPS

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  • Trust in the data
  • Reposit ory cont ext –

perceived t rust wort hiness of t he source

  • Relevance of the data
  • User cont ext - charact erist ics of t he user environment (e.g.

t ask, experience level)

  • Quality of the data
  • Dat a cont ext - significant propert ies of t he dat a t hat make it

fit for use

  • Ease of use of the data
  • Delivery cont ext –

perceived effort needed t o access and get support for dat a

What contributes to decisions to reuse?

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  • Data producer –

reputation of the person who

  • riginally collected the data
  • Repository –

reputation of the institution providing access to the data

  • 3rd party –

reputation of an independent entity endorsing reuse of the data (e.g. faculty advisor, Data S eal of Approval Board)

Focusing on Trust – 3 Sources

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Example Hypotheses

  • For expert users, the reputation of the data producer

will be significantly more important in their decision to reuse data than the reputation of the data repository or recommendations from a trusted 3rd party.

  • For novice users, recommendations from a trusted 3rd

party will be significantly more important in their decision to reuse data than the reputations of the data producer or the data repository.

IS O TRAC at t ribut es t he hypot heses t ouch: 4.2.9, 3.3.1, 3.3.5, 3.3.6, 4.1.5, 4.2.8, 4.2.9, 4.1.4

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Survey Design and Administration

  • S

ample

  • ICPS

R’ s bibliography – i.e. academic lit erat ure result ing from reuse of ICPS R dat aset s

  • Recruitment
  • To coincide wit h ICPS

R’ s last maj or change t o it s delivery syst em

  • Contact
  • Email Endorsement from ICS

PR direct or

  • 1 email invit at ion t o first aut hors wit h survey link
  • 2 email reminders
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  • Institute of Museum and Library S

ervices

  • Annelise Doll, University of Michigan, S

chool of Information, MS I 2010

  • Mallory Hood, University of Michigan, S

chool of Information, MS I 2010

  • Molly Haig, Yale University, current undergraduate

Acknowledgements

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QUESTIONS & COMMENTS

Thank you.