Semi-Permeable Boundaries
Among Institutions:
Facilitating the Flow of
Between Service Settings
Libbie Stephenson, ISSR,
University of California, Los
Angeles
libbie@ucla.edu
3/21/2016
Jon Stiles, UC DATA,
University of California,
Berkeley
jons@berkeley.edu
Stephenson/Stiles 08/06/2008
1
Semi-Permeable
WHAT?
Starting Point:
Data support occurs in a variety of institutional settings.
Those settings may – and probably do – differ in terms
of mission, clientele, resources and focus.
These differences can be a strength, in that services
can be tailored to local context and needs, but can also be
isolating and unnecessarily limit services to users.
Question:
How do some services wind up in particular settings,
how does that affect end use, and how can institutions work to
bridge barriers that limit end use?
3/21/2016
2
What we plan to cover
▪ Local History: Development of secondary data support
at UCLA and Berkeley
▪ 1960’s, 1970’s, 1980’s, and beyond
▪ Changing roles
▪ technology, expertise, mission, resources, turf, AND data
producers
▪ internal, inter-organizational, external factors
▪ Models of collaboration
▪ Cross-unit collaboration and challenges
3/21/2016
Stephenson/Stiles 08/06/2008
3
Data Services is about relations
between producers and intermediaries
… intermediaries and data
… intermediaries and other intermediaries
… intermediaries and users
… and users and data
Intermediaries
Producers
3/21/2016
Users
4
Evolution of Data Services
Landscape: 1960’s
General Environment
Increasing use of surveys
Technology supportive of machine-readable data; expensive, barriers to
entry
Producers
Key institutional players (Census Bureau, large survey/research
organizations, NSF/Funders).
Users
More interest and use (demand)
Fairly specialized community, content focused
Local Environment very important
Lateral Institutions
Activities bundled; not easily broken up
Data
Largely survey based. Dynamic and developing environment.
3/21/2016
5
UCDATA and ISSR
1960’s
Content focused collections
Strong links with researchers with content/methodological knowledge
In-house consumption, small clientele
Training an important component
Technology
Berkeley
▪ International Data Library & Reference Service (IDLRS -1962)
▪ NSF Funds active outreach /acquisition ( 1964)
▪ CSSDA
UCLA
▪ Political Behavior Archive (PBA-1961)
▪ Library receives NSF funding for CIS
▪ Survey Research Center – Archival Data Library (1964)
3/21/2016
6
Evolution of Data Services
Landscape: 1970’s- 80’s
General Environment
▪ “Thin Edge of the Wedge” – 1970 STF’s in Depository Libraries
▪ Continued development of computing/storage technology
▪ Bibliographic control through MARC; descriptive cataloging
▪ IASSIST formed
• ICPSR and national archives gain prominence
Archives:
▪ Unbundling of support components
▪ Complementary activities at Libraries, archives, computing centers
• Influence on data producers to provide better documentation
3/21/2016
7
Two different avenues of
development – UCDATA and ISSR
1970’s – 1980’s
UCDATA
▪ Census Service Facility – broad dissemination and services
▪ Increased focus on State Data, Field Poll Collection
▪ Records in library catalog begin in mid-1970’s
▪ Census State Data Center network 1979
▪ Strong Census-related development through 1980’s
ISSR
▪ Library acquires 1970 Census – limited do-it-yourself service
▪ ISSR established; data archivist hired; census transferred
▪ ISSR Data Archive is de facto central campus unit
▪ Extensive campaign to preserve faculty-generated data
3/21/2016
8
Evolution of Data Services
Landscape: 1990’s to …
▪ Increase in collaboration and joint projects
▪ Over-lap of clientele, data formats and services
▪ Variety in organizational operating models for libraries
and archives
▪ New cohort of professionals have increased
technological skills
▪ Potential of opportunities using Internet seems endless
3/21/2016
9
Two different avenues of
development – UCDATA and ISSR
Berkeley
▪ Mission expanded and name change in 1990’s
▪ Collaborative projects with Library & others
▪ Library and archive develop services in parallel
UCLA
▪ Data services provided by ISSR
▪ Involvement in IASSIST
▪ ISMF developed; join IFDO
3/21/2016
10
What does history tell us?
(One reading)
Secondary Data Mission involves (at least) 4 sets of
relations
[Producer relations]
[User relations]
[Institutional (Local -Lateral) Relations]
[Data Relations]
Change at institutional levels emerges from:
Internal factors (expertise, funding, interest, etc)
Other institutions (archives, producers, private sector)
Big environment (technology, user demands)
3/21/2016
Stephenson/Stiles 08/06/2008
11
Part II
Changing:
Roles of Practitioners
Operational models
3/21/2016
Stephenson/Stiles 08/06/2008
12
Changing Roles –Who provides the
services?
producers
infrastructure
users
▪ Data discovery
▪ Statistical advice
▪ Technical assistance
▪ Data visualization support
3/21/2016
▪ Access to files, documentation and tools
▪ Cataloging and metadata
▪ Data curation and preservation
▪ Physical storage space
▪ Virtual storage space
▪ Staff, training, programming, licensing, funding
13
Players
Single → Local multi-unit → Federated → Consortial
Independent → membership/consortial → national
mandate (heirarchical)
Modes
Structures
Levels
Changing operational
models
Collaboration → Separation → Hierarchy
Amazon, Google and the individual data creator
Part IV
Barriers & Tools
3/21/2016
Stephenson/Stiles 08/06/2008
15
Pros and cons to
models
• Separation
• Collaboration
• Hierarchical
3/21/2016
Stephenson/Stiles 08/06/2008
16
Collaboration—barriers and tools
Barriers:
▪ Institutional culture
▪ Turf
▪ Political power plays
▪ Financial constraints
▪ Technological capacity
▪ Workforce limitations
3/21/2016
Constructive tools:
▪ SWOT
▪ Competing
Values
Framework
17
Multiple-points-of-access-model
Goal: provide best services and resources possible
▪ Develop shared expertise across units
▪ Collaborative collection building
▪ Develop access and data use tools
▪ Provide support for data visualization
▪ Use metadata standards to enhance data
discovery
3/21/2016
18
Summary and conclusions
•Models for services and support are increasingly
complex
•Politics, turf, finances require skill and temerity
to navigate – stakes are higher
•Players do not possess common skill set, or
common vocabulary nor common
goals/objectives
•Payoffs are high – extended scope, projects
3/21/2016
19