Through or Around? scientific Research Data and the Institutional Repository

1. Through or Around? scientific Research Dataand the Institutional Repository Panel Presentation for the International Conference on University Libraries Universidad NacionalAutónoma de México November 6, 2013 Christopher Stewart, Ed.D. Assistant Professor Graduate School of Library and Information Science Dominican University

2. Enabling Access to Research Data Not a new issue for universities and academic libraries, but rapidly developing one… Source: SHERPA/JULIET

3. Expanding the Mandate U.S. Office of Science and Technology Policy directive, 2/22/2013* *Requires each Federal agency with over $100 million in annual conduct of research and development expenditures to develop a framework for awardees.

4. Research Data can be: • Heterogeneous • Unless accompanying publication, often “raw” • Highly idiosyncratic • Characterized by implied description rather than explicit description • Small and big

5. Big Data can be: • Unstructured • Unsuited for traditional (e.g., hierarchical, relational) database models • Complete, not sampled • Linked

6. Goals for Describing Scientific Research Data • Access • Re-use • Context • Content, not container (Yarmey, 2013)

7. Research Lifecycle Source: University of Virginia Library, Data Consulting Group

8. Describing Scientific Research Data: Semantic Modeling • Shared vocabularies provide metadata across a range of subjects • Ontologies allow for contextual relationships • Linked data enable multiple types of data, documents, etc. to be viewed as one database

9. Data Description Schemes (Greenberg, 2013) • Simple: interoperable, easy to generate, low barrier, multidisciplinary, agnostic, flat, general, 15-25 properties • Simple/moderate: interoperability with specific needs, requires expertise and greater domain focus, extensible, granular • Complex: hierarchical and granular, domain-centered, extensive, 100+ properties

10. Are Research Data Collections? • Selecting: partially, though volume and scope of data challenge current digital collection development frameworks • Acquiring: partially, though data not “owned” • Describing: yes, although some content may reside elsewhere • Organizing: yes, but with not with “traditional” IR taxonomies

11. How Academic Libraries are Working with Research Data Now • Institutional repositories are about all types of data, but are clearly set-up for research publications (Salo, 2010) • Most institutional repositories rely on Dublin Core, which is required as minimum operability by OAI-PMH, but most research and exchange standards use XML/RDF as base (Salo, 2010) • Geared for output, not context

12. Primary Metadata Use in Institutional Repositories Source: Simons & Richardson, 2012

13. Challenges for Current Data Curation Models in Academic Libraries • Beyond metadata at project level, dataset level provides some context for data, but can be limited (Yarmey, 2013) • Discoverability in institutional repositories is generally limited to library websites, catalogs, and Google Scholar (Burns, Lana, & Budd, 2013)

14. Content in Institutional Repositories Source: Burns, S. L., Lana, A., & Budd, J. M. (2013). Institutional Repositories: Exploration of Costs and Value. D-Lib Magazine, 19(1/2). Retrieved from http://www.dlib.org/dlib/january13/burns/01burns.html

15. Domain Repositories • Existing and developing metadata standards (e.g., Dryrad/DCAM, ICPSR/DDI) • Centralized or distributed (e.g., DataONE) • Evidence suggests that scholars who deposit materials in subject repositories prefer them over institutional repositories, and are not likely to use both (Xia, 2008) • Built around communities of interest • Cost sharing for cloud services

16. Data Management: Education and Programming Opportunities for Academic Libraries • Training and support for data management plans • Data librarianship • Data literacy

17. An Evolving Model

18. References • Burns, S. L., Lana, A., & Budd, J. M. (2013). Institutional Repositories: Exploration of Costs and Value. D-Lib Magazine, 19(1/2). Retrieved from http://www.dlib.org/dlib/january13/burns/01burns.html • Greenberg, J. (2012, August 22). Metadata for Managing Scientific Research Data. Presented at the NISO/DCMI Webinar. Retrieved from http://dublincore.org/resources/training/ • Salo, D. (2010). Retooling Libraries for the Data Challenge | Ariadne: Web Magazine for Information Professionals. Ariadne, (64). Retrieved from http://www.ariadne.ac.uk/issue64/salo • Simons, N., & Richardson, J. (2012). “New Roles, New Responsibilities: Examining Training Needs of Repository” by Natasha Simons and Joanna Richardson. Journal of Librarianship and Scholarly Communication, 1(2). Retrieved from http://jlsc-pub.org/jlsc/vol1/iss2/7/ • Xia, J. (2008). A Comparison of Subject and Institutional Repositories in Self-Archiving Practices. Journal of Academic Librarianship, 34(6), 489–495. • Yarmey, K. A., & Yarmey, L. R. (2013). All in the Family: A Dinner Table Conversation about Libraries, Archives, Data, and Science - Archive Journal Issue 3. Archive Journal, Summer 2013(3). Retrieved from http://www.archivejournal.net/issue/3/archives-remixed/all-in-the-family-a-dinner-table-conversation-about-libraries-archives-data-and-science/

19. Image Credits • Slide 7: http://www.newsrewired.com/2010/11/16/links-what-is-linked-data-and-why-does-it-matter-to-journalists-and-publishers/ • Slide 10: http://dmconsult.library.virginia.edu/