Semi-permeable boundaries
among institutions: the
Canadian scene(s)
A presentation to IASSIST 2009
Laine G.M. Ruus
University of Toronto. Data Library Service
<laine.ruus@utoronto.ca>
Overview
• History of data services development from a
Canadian perspective
• Models of organizing data services
• Models of collaboration in Canada
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
•
•
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
• 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
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
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
…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)
…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)
…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
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
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
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
Collaborative initiatives are
attempting to ameliorate some of
the 'cons' of local data services
Lack of resources: Growth of regional associations: the
data discovery & West, Quebec, the Atlantic provinces,
data access
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 Clearingh
for the Social Sciences
Lack of political clout
Creation of CAPDU in 1988
Then we got Chuck and Wendy
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
Staff training
Staff turnover
Steep learning
curve: almost all
data services by
librarians rather
than researchers
CAPDU, 'regionals', DLI
More training
… and more training
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
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
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