Dissemination Information Packages (DIPS) for Information Reuse


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Program on Information Science Brown Bag talk by Nancy McGovern, Head of Curation and Preservation Services

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  • Transparency: “Communicating audit results to the public—transparency—will engender more trust, and additional objective audits, potentially leading towards certification, will promote further trust in the repository and the system that supports it” (ISO TRAC, 2012, p. 19).
  • A core element of transparency for digital repositories – a showstopper if it’s missingEstablishes scope: a repository should be what it purports to be How might users/consumers view a mission statement?Would they be aware it exists? Should they be?Might it inform or encourage their use of content?What significance might it have for them?Does a repository’s track record or longevity have an impact on users/consumers?Would users be aware a repository’s track record? How?Might it encourage their use of a repository's content?
  • Dissemination Information Packages (DIPS) for Information Reuse

    1. 1. MIT Libraries Brown Bag Dissemination Information Packages (DIPS) for Information Reuse (DIPIR) DIPIR Principal Investigators: Ixchel M. Faniel, Ph.D. Elizabeth Yakel, Ph.D. Overview of DIPIR : Nancy Y McGovern, Ph.D.
    2. 2. Research-based Practice instruction research practice
    3. 3. • IMLS-funded project led by Drs. Ixchel Faniel (PI) & Elizabeth Yakel (co-PI) • 3-year project October 2010 – September 2013 • Studying the intersection between data reuse and digital preservation in three academic disciplines to identify how contextual information about the data that supports reuse can best be created and preserved. • Focuses on research data produced and used by quantitative social scientists, archaeologists, and zoologists. • The intended audiences of this project are researchers who use secondary data and the digital curators, digital repository managers, data center staff, and others who collect, manage, and store digital information.
    4. 4. Motivation for the DIPIR Project Two Major Goals 1. Bridge gap between data reuse and digital curation research 2. Determine whether reuse and curation practices can be generalized across disciplines Our interest is in this overlap. Data reuse research Disciplines curating and reusing data Digital curation research
    5. 5. The Research Team Resources at dipir.org: • Project Details • People • Sites • Publications • Bibliography • Project Reports • News Nancy McGovern ICPSR/MIT Elizabeth Yakel University of Michigan (CoPI) William Fink UM Museum of Zoology Ixchel Faniel OCLC Research DIPIR Project (PI) Eric Kansa Open Context For more information, please visit http://www.dipir.org
    6. 6. Next Steps Interviews • Social scientists • Archaeologists • Zoologists Survey • ICPSR Data Reusers Map significant properties of data as representation information Observations • UMMZ Data Reusers Faniel & Yakel 2011 Web analytics • OpenContext.org transaction log analysis
    7. 7. Methods Overview ICSPR Open Context UMMZ Phase 1: Project Start up Interviews Staff 10  Winter 2011 4  Winter 2011 10  Spring 2011 Phase 2: Collecting and analyzing user data Interviews data consumers 43  Winter 2012 Survey data consumers 2000  Summer 2012 Web analytics data consumers Observations data consumers 22  Winter 2012 27  Fall 2012 Server logs Ongoing 10 Ongoing Phase 3: Mapping significant properties as representation information
    8. 8. Measuring Data Repository Success A Survey of ICPSR Data Reusers
    9. 9. Survey of ICPSR Data Reusers - Part 1 Measuring Repository Success What data quality indicators contribute to quantitative social scientists’ data reuse satisfaction?
    10. 10. ICPSR Survey of Data Reusers – Part 1 Data Quality Indicators • • • • • Completeness – sufficiency, breadth, depth, and scope Relevancy – applicability and helpfulness of data for the task Accessibility – ease and speed data were retrieved Ease of Operation – ease data were managed and manipulated Credibility – correctness, reliability, impartiality of data (Wang and Strong, 1996; Lee et al., 2002) Additional Indicators: • Data Producer Reputation – regard for a data producer’s work • Documentation Quality – sufficiency and ability to facilitate use
    11. 11. Survey Methodology Data Collection 1,632 first authors of published journal articles 2008-2012 surveyed The Survey Part 1:inquire about data reuse experience Part 2: inquire about experience using ICPSR repository and intention to continue use Preliminary Findings • Tested measures of repository success • Extended ideas about data quality beyond credibility and relevance of data – Data reuse satisfaction requires data that are complete, accessible, and easy to operate • Data producer reputation was not significant • Documentation quality played a role if data reuse satisfaction
    12. 12. The Study Research Question How do novice social science researchers make sense of social science data? Data Collection 22 Interviews Data Analysis Code set developed and expanded from interview protocol http://www.english.sxu.edu
    13. 13. Making sense of matching and merging capabilities across multiple datasets • Combining longitudinal data • “If they're not asking the same question over years,… [it’s] particularly difficult because if they’ve changed the question wording, are then people answering differently and so there were several discussions that I had with my dissertation advisor…” (CBU18). • Merging data from different sources • “…authors will create a variable, they’ll average across a four or five year period, and I’m trying to match that with a variable that was coded for a single year period. So making an argument…that these two things should be put together …, is something I always have to be wary of …So when dealing with that,…I’ll see if it’s been done by others” (CBU04).
    14. 14. Preliminary Findings Research Question How do novice social science researchers make sense of social science data? Data Collection 22 Interviews Data Analysis Code set developed and expanded from interview protocol Preliminary Findings Novices engaged in careful articulation of the data producer’s research process. Novices relied on human scaffolding in the form of faculty advisors and instructors. Human scaffolding also came from the community as represented in the literature.
    15. 15. Social Science Resource Faniel, I.M., Kriesberg, A. & Yakel, E. (2012). Data Reuse and Sensemaking among Novice Social Scientists. Proceedings of the American Society for Information Science and Technology, 49. (Slides) Full list: http://dipir.org/publications/
    16. 16. The Challenges of Digging Data: A Study of Context in Archaeological Data Reuse Motivation • Social and economic forces pushing toward digital archaeological data publication • No robust set of standards exist for field archaeology • Data reuse studies can inform standards development, but there are few outside of science and engineering disciplines http://opencontext.org/
    17. 17. Archaeology resource Faniel, I.M., Kansa, E., Kansa, S.W., Barrera-Gomez, J. & Yakel, E. (2013). The Challenges of Digging Data: A Study of Context in Archaeological Data Reuse. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. (Preprint, Abstract, view slides via SlideShare) Full list: http://dipir.org/publications/
    18. 18. Archaeology Study Research Question 1. How does contextual information serve to preserve the meaning of and trust in archaeological field research over time? 2. How can existing cultural heritage standards be extended to incorporate these contextual elements? Data Collection 22 interviews with archaeologists Data Analysis Code set developed and expanded from interview protocol http://www.english.sxu.edu
    19. 19. Preliminary Findings • The lack of context was a persistent problem. • Data collection procedures were highly sought during data reuse. • Additional context also played a role during data reuse. • Researchers have an interest in the entire data lifecycle (data collection preparation through repository) • Need more studies involving data integration and reuse to help guide standards development (CIDOC-CRM not sufficient)
    20. 20. A Snapshot of the 27 Data Reusers 96% 93% reuse data from other repositories and websites reuse data from museums and archives 63% 37% study ecological trends 26% 26% are systematists reuse data from journal articles reuse data from colleagues
    21. 21. Data Selection Criteria Condition of specimen Data coverage Geographic precision Results of pre-analysis Identification or location errors Matches another dataset Availability of voucher specimen Relevant taxonomically Sequence has been published Time period specimen collected
    22. 22. Trust in Repositories Resource Yakel, E., Faniel, I., Kriesberg, A., & Yoon, A. (2013). Trust in Digital Repositories. International Journal of Digital Curation, 8(1), 143–156. doi:10.2218/ijdc.v8i1.251. (Awarded Best Conference Paper at the 8th International Digital Curation Conference (IDCC). Amsterdam, Netherlands). (Article) Full list: http://dipir.org/publications/
    23. 23. Stakeholder Trust DIPIR is examining trust factors for re-use: • Benevolence – The organization demonstrates goodwill toward the customer • Integrity – The organization is honest and treats stakeholders with respect • Identification – Understanding and internalization of stakeholder interests by the organization – ISO TRAC understanding the designated community (pp. 25-26) • Transparency – Sharing trust-relevant information with stakeholders – ISO TRAC sharing audit results (p. 19) (Pirson & Malhotra, 2011)
    24. 24. Theoretical Framework DeLone and McLean Information Systems (IS) Success Model Information Quality System Quality Intention Use to use Net Benefits User Satisfaction Service Quality (DeLone & McLean, 2003)
    25. 25. DIPIR and TRAC • DIPIR used TRAC requirements as a starting point for informing a survey of social scientists • That process raised questions about what users of digital repositories might notice and/or rely upon • Worthwhile to take a step back and consider how users might perceive our TRAC-related efforts
    26. 26. Perceptions of TRAC Examples from TRAC requirements: 3.1.1. Mission Statement reflects “commitment to the preservation of, long term retention of, management of, and access to digital information” 3.2. “sustained operation of the repository” 3.3.4. “commit to transparency and accountability in all actions” How might users of repositories become aware of and respond to our efforts to be compliant? Should we strive to encourage them to be aware? How? How can/would we know if their interest in our practices increases or changes? Who is our audience for demonstrating good practice?
    27. 27. Repository Trust Concepts Integrity Benevolence Transparency Identificationbased trust Social Factors Structural Assurances Performance Expectancy Trust Continuance Intention
    28. 28. How often interviewees mentioned Trust Factors Quantitative Social Scientists (44) (66) 0 1 1 5 1 1 1 5 1 2 2 10 1 7 8 9 1 10 4 0 23 1 27 1 Archaeologists Concepts (22) Stakeholder Trust in the Organization Benevolence Identification Integrity Transparency Social Factors Colleagues Structural Assurance Guarantees: Preservation/Sustainability Institutional reputation Third Party Endorsement All
    29. 29. Coming UP … DIPIR Research Assistant Adam Kriesberg will present a paper on Nov. 4 at the 2013 Meeting of the Association for Information Science and Technology (ASIS&T). The paper is entitled “The Role of Data Reuse in the Apprenticeship Process” and features Rebecca Frank, Ixchel Faniel, and Elizabeth Yakel as co-authors. http://dipir.org/news/
    30. 30. Acknowledgements • Institute of Museum and Library Services, – LG-06-10-0140-10 • Our co-authors: Sarah Whitcher Kansa, Ph.D., Julianna Barrera-Gomez, M.S.I., Elizabeth Yakel, Ph.D. • Partners: Nancy McGovern, Ph.D. (MIT), Eric Kansa, Ph.D. (Open Context), William Fink, Ph.D. (University of Michigan Museum of Zoology) • Students: Morgan Daniels, Rebecca Frank, Adam Kriesberg, Jessica Schaengold, Gavin Strassel, Michele DeLia, Kathleen Fear, Mallory Hood, Molly Haig, Annelise Doll, Monique Lowe