Semi-Permeable Boundaries Among Institutions: Facilitating the Flow of Between Service Settings

1. Semi-Permeable Boundaries Among Institutions: Facilitating the Flow of Between Service Settings Libbie Stephenson, ISSR, University of California, Los Angeles libbie@ucla.edu Jon Stiles, UC DATA, University of California, Berkeley jons@berkeley.edu Stephenson/Stiles 08/06/2008

2. 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? 4/2/2014 2

3. 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 4/2/2014 Stephenson/Stiles 08/06/2008 3

4. Data Services is about relations between producers and intermediaries … intermediaries and data … intermediaries and other intermediaries … intermediaries and users … and users and data 01010101010 01 000 11 Intermediaries 01010101010 01 000 11 Producers Users 01010101010 01 000 11 Environment 4/2/2014 4

5. 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. Evolution of Data Services Landscape: 1960’s 4/2/2014 5

6. 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) ▪ CSSDAUCLA ▪ Political Behavior Archive (PBA-1961) ▪ Library receives NSF funding for CIS ▪ Survey Research Center – Archival Data Library (1964)

7. 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 4/2/2014 7

8. Two different avenues of development – UCDATA and ISSR1970’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 4/2/2014 8

9. 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

10. 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

11. 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) 4/2/2014 Stephenson/Stiles 08/06/2008 11

12. Part II Changing: Roles of Practitioners Operational models 4/2/2014 Stephenson/Stiles 08/06/2008 12

13. ▪Data discovery ▪ Statistical advice ▪ Technical assistance ▪ Data visualization support ▪ Access to files, documentation and tools ▪ Cataloging and metadata ▪ Data curation and preservation ▪ Physical storage space ▪ Virtual storage space ▪ Staff, training, programming, licensing, funding Changing Roles –Who provides the services? users producers infrastructure 4/2/2014 13

14. Changing operational models Levels Single → Local multi-unit → Federated → Consortial Independent → membership/consortial → national mandate (heirarchical) Structures Modes Collaboration → Separation → Hierarchy Players Amazon, Google and the individual data creator

15. Part IV Barriers & Tools 4/2/2014 Stephenson/Stiles 08/06/2008 15

16. Pros and cons to models Separation Collaboration Hierarchical 4/2/2014 Stephenson/Stiles 08/06/2008 16

17. Collaboration—barriers and tools • Barriers: • ▪ Institutional culture • ▪ Turf • ▪ Political power plays • ▪ Financial constraints • ▪Technological capacity • ▪Workforce limitations Constructive tools: ▪ SWOT▪ Competing Values Framework 4/2/2014 17

18. 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 4/2/2014 18

19. 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 4/2/2014 19