This document discusses research data management and the role of university libraries. It describes the SEAD (Sustainable Environment Actionable Data) project, which provides data services like curation, preservation, and a social community network to support research data across its lifecycle. SEAD aims to support interdisciplinary research by allowing researchers to define and manage related collections of data and metadata called Research Objects in a scalable way. The document argues that research organizations are best positioned to provide comprehensive long-term data services that integrate across the entire research process.

1. Data Sets, Ensemble Cloud
Computing, and the University
Library:
Getting the Most Out of Research Support
Jim Myers1, Margaret Hedstrom1, Beth A Plale2, Praveen Kumar3, Robert
McDonald4, Rob Kooper5, Luigi Marini5, Inna Kouper4, Kavitha Chandrasekar4
myersjd@umich.edu
1 School on
Information, University of Michigan, Ann Arbor, MI, United States.
School of Informatics and Computing, Indiana University, Bloomington, IN, United States.
3 Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, IL, United States.
4 Data To Insight Center, Indiana University, Bloomington, IN, United States.
5 National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, IL, United States.
2

2. Overview
• Technological advances are making it ever easier to
move computation, data, and metadata around
• With decreasing costs and increasing recognition of the
value of data re-use, many organization are exploring
their role in data curation/preservation
• If we look at the nature of the problem
– How should data be curated to scalably support research?
• Lifecycle approaches to manage value-defined research objects
– Can we do it?
• SEAD as an end-to-end demonstration…
– What organization(s) are best positioned/the most capable
of leading/providing such services long-term?
• Primary research organizations have a combination of capability,
motivation, and long-term commitment.

3. Technology – the world is flat
• Today’s researchers can employ computing
and data resources from anywhere, using
scalable search technologies …
Enough said.

4. Data as a key resource, Big Data
• Data is increasingly recognized as valuable
beyond its initial use:
–
–
–
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Data reproducibility
Re-analysis
Reference Data
Data mining/machine learning/…
– NSF Data plan requirement
– Paper publication with data requirements
– Community and institutional collections growing

5. Data Publication today
• Data cited in papers (to limited depth)
• Project file archives (large, limited description,
gray/dark)
• Reference/analytical data (standardized content,
limited breadth)
• Historical collections (temporal breadth, limited
numbers)
- do any of these solve the problem?

6. Researchers think, and work, like this:
• Multi
– Disciplinary
– Format
– Model
– Semantics
– Location

7. and this
–
–
–
–
–
–
Raw and derived data
~5 levels of quality,
processing, maturity
Observations,
calibrations,
experiments, models,
statistical ensembles, …
Also organized by
location, time, variables,
technique, creator,
project, provenance, …
Large amount of
reference information
from external sources
(e.g. NASA)
Evidence for ‘nonorthogonal’ subcollections

8. What’s Really Needed?
Scalable Research Productivity Requires:
• A way to
– store what you want
– Reference what you want
– Organize how you want (search, filter, tag, collect)
• At the scale, and level of detail/richness, you want
• When you figure that out
• In a way that is self-describing/high-fidelity across
applications and owners
• In the vocabularies and formats you find efficient
• Beyond the lifetime of individual/project interest
• For active use and external credit
• With minimal training/IT support required.

9. How can we approach magic?
• Global identifiers – data, terms, metadata
• Content management abstractions (blob + type +
metadata)
• Service architectures and automated processing
(conversion, preview, extraction, derivation, cataloging,
…)
• Applications that share these abstractions – write what
you know, display/ignore what you don’t
• Research Object management (structured, interrelated collections)
Web 2.0, Web3.0, + explicit context management …

10. SEAD: Sustainable Environment Actionable Data
• An NSF DataNet project started in
October, 2011
• An international resource for
sustainability science
• A provider of light-weight Data Services
based on novel technical and business
approaches:
– Supporting the long-tail of research
– Enabling active and social curation
– Providing integrated lifecycle support for data
http://sead-data.net/
Margaret Hedstrom, PI
Praveen Kumar, co-PI
Jim Myers, co-PI
Beth Plale, co-PI

11. SEAD is:
• Data discovery
• Project workspaces
• A data-aware
community network
• Curation and
preservation services
that link to multiple archives and discovery
services

12. SEAD is:
• An active repository that creates data pages with
–
–
–
–
–
–
–
–
Previews
Extracted Metadata
Overlays
Tags
Comments
Provenance
Use information
Download/Embed

13. SEAD is:
• A tool for community exploration:
– Personal and
Project Profiles
– Publications and
Data Citations
– Co-author,
co-investigator
graphs
– Temporal analysis

14. SEAD is:
• Curation and Preservation Services:
– Research Object
management
– ID assignment
– Matchmaking to
long-term repositories
Citation Generation
– Catalog Registration SEAD’s Virtual Archive allows curators to
access, assess, enhance, package, and submit
data from SEAD project repositories for long– Discovery services
term storage in SEAD-managed storage or
external institutional repositories and cloud
data services.

15. –
–
–
–
Apps read what they need and write what they know
Curation snapshots meaningful Research Objects
Multiple ROs can be defined/managed re-using the same underlying ‘living’ content
The larger graph can be ~reassembled w/o the ongoing cost of managing at the item level
Flickr-style web management of data
Sensor data
Semantic Content Middleware
over Scalable File System and
Triple Store
Geospatial, social
network mash-ups,
workflows and services
Curation Services to harvest
and package specific data sets
Federation of OAI
repositories for
long-term
preservation

16. Key Points
• Research Objects have meaning/value but data comes in
smaller chunks
• Research Objects are not orthogonal, but individual data
sets/files are
• Lifecycle approaches for datasets are becoming possible
• Managing intermixed ROs is the problem that needs to be
tackled to meet the research community’s needs
• Research Data Alliance (RDA) can help drive
standardization/scaling

17. What will drive research data preservation?
• The most valuable data service(s) are
active/actionable research service(s)…
– The ability to define Research Objects is more
important than any given RO
• Led by research organizations as part of their
long-term mission?
– The only organizations with the focus, scope, and
scale to solve the whole problem (end-to-end
research productivity)

18. Acknowledgements
• SEAD Team @ UM, UI, IU
• NSF
• NCED, IRBO, WSC-Reach, IMLCZO, ICPSR, other
sustainability researchers
• and Thank You!
… stop by the SEAD booth and share your thoughts!
http://sead-data.net/