The document discusses the importance of data curation in academic research, emphasizing its role in maintaining data integrity, efficiency, and compliance with policies. It highlights various initiatives and tools, such as DMPTool and DataUp, that support researchers in managing and sharing data effectively. Additionally, it addresses challenges in data curation and the library's role in facilitating access and collaboration within research communities.

1. Building Communities and Services in
Support of Data-Intensive Research
Stephen Abrams
University of California Curation Center
California Digital Library
August 20, 2013

2. Topics
 Data curation
 UC3 services
 DMPTool
 DataUp
 EZID
 Merritt
 WAS
 Collaborative initiatives
 DataShare
 Research Hub
 Conclusions

3. Why is data curation important?
 Integrity
 Enabling appropriate scrutiny, debate, reproduction, and
verification of results
 Efficiency
 Avoiding needless duplication of effort
 Policy
 Complying with institutional policies, publication requirements,
and funder mandates
“*Data] is a valuable national asset whose value is multiplied when it is made
easily accessible to the public”
– Office of Science and Technology Policy

4. Why is data curation important?
 Catalyzing
 Promoting progress through new collaborations and creative
(re)use of data
“If I have seen further it is by standing on the shoulders of giants”
– Isaac Newton, 1676

5. What is the library’s role?
 A continuation of its long-standing mission and practice to
connect patrons with content of interest in meaningful ways
across barriers of space and time
Cf. Tenopir et al. (2012), “Academic librarians and research data services: Preparation and attitudes,” 78th
IFLA General Conference and Assembly, Helsinki, http://conference.ifla.org/past/ifla78/116-tenopir-en.pdf
 Offering solutions that enhance the natural points of
alignment between the scholarly research and information
lifecycles
Publish
Reuse
ShareCreate
Discover
Collect
PreserveAccessResearchResearch CurationCuration
Scholarly lifecycle Information lifecycle

6. Why is data curation hard?
 Ever increasing number, size, and diversity of content
 Inevitability of disruptive change
 Resources not keeping pace with growth
 Stakeholders outside of traditional cultural heritage domains,
with lots of questions
 Who can give me advice on what I should do?
 How should I describe and package my data?
 How can I cite my data in order to receive
credit for it?
 How can I share my data?
 What can I do with web published data?
…

7. DMPTool – guidance and resources
Finalist, 2012
DPC Award for
Research and
Innovation
http://dmptool.org/
 Create, edit, and share data
management plans
 Meet funder requirements
 Provide institutional guidance
 Links to local resources

8. DMPTool – guidance and resources
Finalist, 2012
DPC Award for
Research and
Innovation
http://dmptool.org/
 Create, edit, and share data
management plans
 Meet funder requirements
 Provide institutional guidance
 Links to local resources

9. DMPTool – guidance and resources
Two recently
funded projects
 Functional
enhancements
and open source
community
development
Sloan Foundation
 Training and
outreach
IMLS
http://dmptool.org/
 New options for DMP
collaboration and formal
and ad hoc review
 Stronger administrative
control and customization

10. DataUp – description and packaging
http://dataup.cdlib.org/ http://www.dataup.org/
“It’s easier to augment systems than
it is to change behavior”
Curation for tabular datasets
 Excel add-in
 Azure cloud service

11. DataUp – description and packaging
http://dataup.cdlib.org/ http://www.dataup.org/
 Best practices check
 Data description
 Identifier and citation generation
 Repository submission to
ONEShare
Curation for tabular datasets
 Excel add-in
 Azure cloud service

12. DataUp – description and packaging
http://dataup.cdlib.org/ http://www.dataup.org/
What researchers don’t need to know
 Schema definition and XML syntax
 Identifier registration procedures
 Citation format
 Repository packaging and submission
 Harvesting for aggregation
2013 Innovation Award winner
Recently funded project
 Functional enhancements and open
source community development
NSF

13. EZID – identification and citation
http://n2t.net/ezid/
UC3 is a founding
member of the
DataCite consortium
 Mint DOI and
ARK
 Add descriptive
metadata
 Receive QR code
 Global resolution
 Aggregated
discovery
 Updatable
resolution URLs
 Establish and maintain persistent two-way
linkages between the literature and the data
that underlies its results

14. EZID – identification and citation
UC3 is a founding
member of the
DataCite consortium
 Mint DOI and
ARK
 Add descriptive
metadata
 Receive QR code
 Global resolution
 Updatable
resolution URLs
 Link to dataset in repository
http://n2t.net/ezid/

15. EZID – identification and citation
UC3 is a founding
member of the
DataCite consortium
 Mint DOI and
ARK
 Add descriptive
metadata
 Receive QR code
 Global resolution
 Updatable
resolution URLs
 Link from dataset landing page to article
citing the data

16. EZID – identification and citation
UC3 is a founding
member of the
DataCite consortium
 Mint DOI and
ARK
 Add descriptive
metadata
 Receive QR code
 Global resolution
 Updatable
resolution URLs
 Link from article back to dataset

17. EZID – identification and citation
UC3 is a founding
member of the
DataCite consortium
 Aggregated discovery via DataShare and Ex Libris Primo
 Later this year, aggregation via T-R Data Citation Index

18. EZID – identification and citation
UC3 is a founding
member of the
DataCite consortium
 SEI for public visibility in leading search engines

19. Merritt – preservation and access
 Content agnostic,
model free
 Micro-service
architecture
 UI and RESTful API
 26 curatorial units
 271 collections
 325,000 objects
 450,000 versions
 4,500,000 files
 13 TB
http://merritt.cdlib.org/
 Enforceable Data Use Agreements (DUAs) in
response to concerns over potential loss of
control over dissemination and reuse
Open to the UC
community and
external partners
 Dark archive for
long-term
assurance
 Bright archive for
sharing
 Integration with
preservation grids
 Integration with
public access
portals
 Integration with
CMS

20. Merritt – preservation and access
 Content agnostic,
model free
 Micro-service
architecture
 UI and RESTful API
 26 curatorial units
 271 collections
 325,000 objects
 450,000 versions
 4,500,000 files
 13 TB
 For curatorially-designated collections and
objects, a download request triggers …
Open to the UC
community and
external partners
 Dark archive for
long-term
assurance
 Bright archive for
sharing
 Integration with
preservation grids
 Integration with
public access
portals
 Integration with
CMS

21. Merritt – preservation and access
 Content agnostic,
model free
 Micro-service
architecture
 UI and RESTful API
 26 curatorial units
 271 collections
 325,000 objects
 450,000 versions
 4,500,000 files
 13 TB
Open to the UC
community and
external partners
 Dark archive for
assurance
 Bright archive for
sharing
 Integration with
preservation grids
 Integration with
public access
portals
 Integration with
CMS
 Click-through DUA; acceptance of terms of
use triggers …

22. Merritt – preservation and access
 Content agnostic,
model free
 Micro-service
architecture
 UI and RESTful API
 26 curatorial units
 271 collections
 325,000 objects
 450,000 versions
 4,500,000 files
 13 TB
Open to the UC
community and
external partners
 Dark archive for
assurance
 Bright archive for
sharing
 Integration with
preservation grids
 Integration with
public access
portals
 Integration with
CMS
From: no-reply-merritt@ucop.edu
Subject:Merritt DUA acceptance
Name: Stephen Abrams
Affiliation: California Digital Library
Collection: UCSF DataShare
Object: Frontotemporal Lobar Degeneration (FTLD)
Date: 2013-05-3109:50:34PDT
Terms of use: As part of this agreement, Consumer submits to the following
statements:
(1) I will receive access to de-identified data and will not attempt to establish the
identity of any of the study subjects.
(2) I will share these data only with my immediate co-workers, and I will not transfer
these data to other research groups. I understand that these data are available to
other research groups through the process by which I obtain them.
(3) I will require anyone in my group who utilizes these data, or anyone with whom I
share these data to comply with this data use agreement
...
 Email notification to consumer and curator
 Delivery of requested content

23. Web Archiving Service
http://was.cdlib.org/
 Collect, describe,
manage, preserve,
and provide access
to web sites
 Analysis tools
 Full-text search
 27 curatorial units
 185 collections
 10,772 web sites
 97,121 captures
 64 TB
“You can’t study life
in our time without
the Internet, so we
must preserve it”
– René Vourburg, KB
 Initially developed
as part of the
NDIIPP-funded Web
at Risk project
 The web has become the publication platform
of choice
 Source of important primary and secondary
research data

24. Web Archiving Service
http://was.cdlib.org/
 Collect, describe,
manage, preserve,
and provide access
to web sites
 Analysis tools
 Full-text search
 27 curatorial units
 185 collections
 10,772 web sites
 97,121 captures
 64 TB
“You can’t study life
in our time without
the Internet, so we
must preserve it”
– René Vourburg, KB
 Initially developed
as part of the
NDIIPP-funded Web
at Risk project
 For example, California water district web sites
supplement UC Davis source water assessment
and protection (SWAP) Merritt collections

25. Connecting to communities of practice
 Engage with new user communities where and how they
already work
 Shifting user roles, shifting expectations
 Institutional  individual researcher
 Behavioral expectations set by the commercial/mobile web

26. DataShare – catalyzing science
 UCSF Clinical and Translational Science Institute
http://ctsi.ucsf.edu/
 UCSF Library
http://www.library.ucsf.edu/
 UCSF Center for Imaging of Neurodegenerative Disease
http://www.radiology.ucsf.edu/cind/
http://datashare.ucsf.edu/
“Making data transparent
and available is going to
accelerate all of science;
it's a relatively
inexpensive way to get
more value out of all of
the work that we do”
– Michael Weiner, UCSF
 Pilot project in
biomedical imaging
“The goal is to
catalyze widespread
sharing of scientific
research data”
 Prepare
 Describe
 Upload
 Curate
 Discover
 Share

27. DataShare – catalyzing science
 UCSF-developed submission client, supporting intuitive
drag & drop operation and metadata entry
 EZID for DOIs; Merritt for preservation
 XTF-based faceted search/browse portal
http://xtf.cdlib.org/
http://datashare.ucsf.edu/
“Making data transparent
and available is going to
accelerate all of science;
it's a relatively
inexpensive way to get
more value out of all of
the work that we do”
– Michael Weiner, UCSF
 Pilot project in
biomedical imaging
“The goal is to
catalyze widespread
sharing of scientific
research data”
 Prepare
 Describe
 Upload
 Curate
 Discover
 Share

28. Research Hub – content mgmt and collaboration
 3,900 users
 770 projects
 Alfresco CMS
 Desktop sync
 Mobile apps
 Abode Creative
Suite
 Personal file
management
 Project
collaboration
 Departmental
resource pooling
 Research data
sharing
“Powerful tools for
management and
collaboration”
 Create
 Organize and enrich
 Keep safe
 Share
http://hub.berkeley.edu/
 UC Berkeley Information Services &Technologies
http://ist.berkeley.edu/

29. Research Hub – content mgmt and collaboration
 3,900 users
 770 projects
 Alfresco CMS
 Desktop sync
 Mobile apps
 Abode Creative
Suite
 Personal file
management
 Project
collaboration
 Departmental
resource pooling
 Research data
sharing
“Powerful tools for
management and
collaboration”
 Create
 Organize and enrich
 Keep safe
 Share
http://hub.berkeley.edu/
 Primary discovery and access via Research Hub
 EZID for DOIs; Merritt for preservation
 Merritt access called for in succession plans

30. Data curation
“Access to and sharing of data are essential for the conduct and
advancement of science”
— Arzberger et al. (2004), “Promoting access to public research data for scientific, economic,
and social development,” Data Science Journal 3: 135-52, doi:10.2481/dsj.3.135
 Pro-active curation of research outputs is necessary to ensure
their ongoing viability and use
 Good for research; good for researchers
 Quicker, more innovative science; higher impact factor
 Increasingly necessary for conformance to institutional
policies, publication requirements, and funder mandates

31. Data curation
 Widespread adoption is dependent on outreach, education,
and minimal intrusion into existing disciplinary workflows and
common community practices
 The most effective – and sustainable – curation services are
composed from best-of-breed components
 Libraries are a natural curation partner for the research
community

32. For more information
 UC Curation Center
http://www.cdlib.org/uc3/
uc3@ucop.edu
Stephen Abrams David Loy
Patricia Cruse Mark Reyes
Shirin Faenza Joan Starr
Scott Fisher Carly Strasser
Erik Hetzner Marisa Strong
Joshua Hubbard Bhavitavya Vedula
Greg Janée Kenneth Weiss
John Kunze Perry Willet
Rosalie Lack
 DataShare
http://datashare.ucsf.edu/
Geoffrey Boushey Megan Laurance
Anirvan Chatterjee Angela Rizk-Jackson
Maninder Kahlon Michael Weiner
Julia Kochi
 Research Hub
http://hub.berkeley.edu/
Ian Crew Patrick McGrath
Michael McCarthy Noah Wittman