Semi-permeable boundaries among institutions: the Canadian scene(s)

1. A presentation to IASSIST 2009 Laine G.M. Ruus University of Toronto. Data Library Service <laine.ruus@utoronto.ca> Semi-permeable boundaries among institutions: the Canadian scene(s)

2. Overview • History of data services development from a Canadian perspective • Models of organizing data services • Models of collaboration in Canada

3. Why data services began in the social sciences • Relative rates of change/periodicity • Geology (,000 or ,000,000s of years) • Social sciences (years, months, weeks significant) • Finance (days) • Environment (hours) • Therefore, need to study/predict change more immediate and visible

4. Replicability – if you loose it, can you ever get it back? • Access to historical data • Keeping the report is no substitute • Research funding • Well funded sciences collect more data, no need for secondary analysis • Academic stature measured by grants; collecting more data needs a bigger grant • social sciences always poor

5. Importance of comparative research: time and/or space and/or interdisciplinary • Note: data preservation/service procedures and skill are relatively discipline neutral • Data files in the sciences are a bit bigger, some different analysis software is used, and research questions are different

6. History of data services from a Canadian perspective • The future begins in the past • Germination of data archives/data services in the 1940s • Growth began in the 1960s, in Europe and the US

7. The first data archives • 1946 – The Roper Public Opinion Research Center, Williams College • 1950s- Social Systems Research Institute, University of Wisconsin, Madison • 1960 – Zentralarchiv für Empirische Sozialforschung, Cologne • 1962 – Inter-University Consortium for Political Research (ICPR) • 1963 – International Data Library and Reference Service, University of California, Berkeley • 1964 – DATUM, Bad Goedesberg • 1964 – Steinmetzarchief, University of Amsterdam • 1965 – Louis Harris Political Data Center, University of North Carolina, Chapel Hill • 1967(?)- Social and Economic Archive Committee, University of Essex

8. …and the first associations • 1962 - CSSDA (Council of Social Science Data Archives) • 1974 – IASSIST (International Association for Social Science Information Service and Technology) • 1976 – CESSDA (Council of European Social Science Data Archives) • 1977 – IFDO (International Federation of Data Organizations)

9. …and in Canada • 1957 - Lucci, Rokkan & Meyerhoff report for Columbia University. School of Library Science • 1964- NRC Directory of unpublished data • 1965 – York University. Institute for Behavioural Research. Data Archive • 1966 – Carleton University Data Centre (Department. of Sociology) • 1970 – University of British Columbia. Data Library (Library & Computing Centre) • 1971 – Inventory of social science quantitative data sources in Canada • 1971-1980 Data Clearinghouse for the Social Sciences in Canada • 1973-1983 – Public Archives of Canada. Machine-Readable Archives • 1974-1979 – Canadian Consortium for Social Research (CCSR)

10. …and still in Canada • 1981 – 1st SSHRCC policy on data deposit (11 data centres listed) • 1988 – CARL Consortium to purchase 1986 census data (25 academic institutions) • 1988-1995 – an additional 4 CARL Consortia • 1993-1994 - 2 ICPSR federated memberships, one in the east, one in the west • 1996 – Data Liberation Initiative (DLI) started, 43 university libraries • 2009 - DLI has 74 member universities and colleges ; ICPSR has about 30 member institutions in Canada; Roper has 4 member institutions in Canada

11. Two major models of organizing data services • Canada & US: local data services in academic institutions • Canada – all but 1 in university libraries • US – ca 42% in university libraries • US also has: 3 large ‘national’ archives + state data centers • Rest of the world: centralized national data archives, usually funded by a social science research council – none in libraries

12. Centralized data archives • Pros: • Better funding • More political clout • Synergies of large, specialized & stable staff in a central place • Cons: • Less flexibility • More accountability to funding bodies • More stable staff, less flexibility to hire for changing skills/needs • Distance from the users • Tend to focus more on preservation

13. Local data services • Pros: • Close to the users, more emphasis on user services • More flexible, sensitive to changing user/institutional needs • Cons: • Lack of resources • Lack of political clout • Staff training and continuing education need to be dealt with differently • Each instance duplicates activities of the others, wasting resources • Higher staff turnover – dead end/partial jobs • High & steep learning curve • Less emphasis on preservation

14. Collaborative initiatives are attempting to ameliorate some of the 'cons' of local data services

15. Lack of resources: data discovery & data access Growth of regional associations: the West, Quebec, the Atlantic provinces, and last Ontario Fee for service collaborations: CHASS, DLI, IDLS, DRI, SDA, CREPUQ, [<odesi>] and some 'free' eg LANDRU Also informal collaborations, eg Ryerson, University of Toronto, and York, And Carleton Univ and Univ of Ottawa Not a problem-free history: some have disappeared,eg CCSR, Data Clearinghouse for the Social Sciences

16. Lack of political clout Creation of CAPDU in 1988 Then we got Chuck and Wendy. Now we just send one of them DLI Section fights our battles in Statistics Canada Data management: duplication of effort Vince Gray at UWO does the QA DINO, Statistics Canada, and a project in CREPUQ do DDI

17. Staff training CAPDU, 'regionals', DLI Staff turnover More training Steep learning curve: almost all data services by librarians rather than researchers … and more training

18. And then there are the RDCs • A collaboration between the academic sector and Statistics Canada, with research (CFI) funding • 16 full and 8 'branch' RDCs in Canada, with 43 participating universities • Most are located in libraries, but are staffed by Statistics Canada analysts

19. What have we gained? • A strong support network, especially in the area of user services • A strong training network • Several complementary resource discovery tools • Three complementary data delivery tools • Much better relations with our national statistical agency

20. What Canada lacks... • Preservation infrastructure for federal, provincial and municipal institutions • An academic culture of data sharing • Any form of national data archive • The synergies that result from a number of people working together on related problems