Rochester’s emphasis on user-centered repository design = good fit for context-driven approach to data curation
Step 1: identify stakeholders and the designated community
photo credit for the image of the data producer, repository staff, and data reuser: <a href="http://www.flickr.com/photos/32066106@N06/3000043099">Passing time</a> via <a href="http://photopin.com">photopin</a> <a href="https://creativecommons.org/licenses/by-nc-nd/2.0/">(license)</a>
photo credit for The DSpace Digital Repository Model image: http://www.ariadne.ac.uk/issue55/vandeventer-pienaar#27, Figure 4
Trying to find the edges of our designated community. We aren’t and can’t be a disciplinary repository, so whose needs are in scope and whose are out?
What we got was huge diversity, and a range of systems that could do some, but not all of the needs – you can do anything, but you can’t do everything.
Attempting to focus in on user context for data blew us back out to look at the library context for the repository.
In order to interpret and implement the things users tell us they need, we need to have clear justification for who we determine are users. When building a repository that necessarily serves a very broad and very diverse set of users, there will always be competing priorities. Previous repository has a gazillion metadata fields, 99% of them optional, and they’re a major barrier.
Context in context: applying a context-driven approach in an academic library
Context in context: applying a
content-driven approach in an
• Relatively low usage, with a few exceptions
• Electronic theses and dissertations; musical scores
• Needed to think about changing software
• Usability research never focused on metadata
• Metadata form was challenging for users
• Data can be deposited to UR Research, but it wasn’t a focus
of the repository design process
.csv, codebooks, research design,
survey, images, images, notes,
shape files, specimens,
description, creator, title, publisher,
date, donor, rights, collector, taxon,
documents, subject, coverage,
But based on whose needs?
A Context-driven Approach to Data Curation for Reuse
data collection, data producer, and
repository information, prior reuse, missing data,
research objectives, provenance, advise on reuse, etc.
The DSpace Digital Repository Model
Scoping down to data…
For your purposes, what does a data repository need to do?
Producers / Reusers
Data curation profiles
Walk-throughs of existing systems
IR situation analysis
What systems match those needs?
What contextual information is needed to
effectively support reuse?
What resources can we devote to developing and
maintaining our repository?
…looking back out to library context
What use cases / user needs are in and out of
Should data be treated the same as other scholarly
resources in the IR?
What is the library’s strategic vision for data?
IT service model
Library strategic plan
• Kyle Parry, CLIR fellow for data curation for
visual and cultural studies
• Nora Dimmock, Asst Dean for IT
• Marcy Strong, Head of Cataloging
• Kathleen Fear, Data librarian
Conflict and consensus
• What contextual information supports reuse
of these images serving multiple goals
(artistic, political, documentary, etc.)?
• Where are instances of conflict in what
repository staff and different users prioritize?
How can these conflicts be resolved?