THROUGH OR AROUND? SCIENTIFIC RESEARCH DATA AND THE INSTITUTIONAL REPOSITORYPanel Presentation for the International Conference on University LibrariesUniversidad Nacional Autónoma de MéxicoNovember 6, 2013

Christopher Stewart, Ed.D.Assistant Professor Graduate School of Library and Information ScienceDominican University

Enabling Access to Research DataNot a new issue for universities and academic libraries, but rapidly developing one…

Data Archiving RequirementsAgency Nation/RegionNSF United States

NIH United States

INSPIRE European Union

UK Research Council United Kingdom

ARC Australia

EUR-OCEANS France

CIHR Canada

FODAZIONE CARIPLO Italy

Source: SHERPA/JULIET

Expanding the MandateU.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.

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

description• Small and big

Big Data can be:• Unstructured• Unsuited for traditional (e.g., hierarchical, relational)

database models• Complete, not sampled• Linked

Goals for Describing Scientific Research Data

• Access• Re-use• Context• Content, not container (Yarmey, 2013)

Research Lifecycle

Source: University of Virginia Library, Data Consulting Group

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

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

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

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

Primary Metadata Use in Institutional Repositories

Standard Percent of Use Dublin Core 68%

OAI-PMH 46%

MARC 40%

Source: Simons & Richardson, 2012

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)

Content in Institutional Repositories Content Type Number of

Repositories Holding

Response Rate

Courseware 14 31%

Data sets 23 51%

Other 25 56%

Books 29 64%

Book chapters 35 78%

Tech reports, working papers 39 87%

Conference articles 40 89%

Presentations 41 91%

Theses and dissertations 43 43%

Journal articles 44 44%

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

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

Data Management: Education and Programming Opportunities for Academic Libraries • Training and support for

data management plans• Data librarianship• Data literacy

An Evolving ModelSubject/Domain Data Repository Institutional Repository“Raw” data Published data

Linked Hierarchical

Open Data Open Access

Complex description Basic description

Multi-type data Multi-type documents

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/

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/