Future of social science and libraries

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Future of social science and libraries

• A presentation to the IFLA Social Science
  Libraries Pre-Conference August 2008
• Laine G.M. Ruus
• University of Toronto. Data Library Service
• 2008-08-05

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Overview

• Why data services began in the social sciences
• History of data services from a Canadian
  perspective
• Models of organizing data services
• The future a highly personal perspective

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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

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Why data services began in the social sciences
(contd)

• 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

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Why data services began in the social sciences
(contd)

• 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 software is used, and research
  questions are different

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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

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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

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and the first associations

• 1962 - CSSDA (Council of Social Science Data
  Archives)
• 1974 IASSIST (International Association for
  Social Science Information Systems and
  Technology)
• 1976 CESSDA (Council of European Social Science
  Data Archives)
• 1977 IFDO (International Federation of Data
  Organizations)

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and in Canada

• 1957 - Lucci, Rokkan Meyerhoff report for
  Columbia University. School of Library Science
• 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)
• 1974-1979 Canadian Consortium for Social
  Research (CCSR)
• 1973-1983 Public Archives of Canada.
  Machine-Readable Archives

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and more in Canada

• 1981 1st SSHRCC policy on data deposit (11
  institutions listed)
• 1988 CARL consortium to purchase 1986 census
  data (25 academic institutions)
• 1996 Data Liberation Initiative (DLI)
• Today, DLI has 74 member institutions ICPSR has
  about 30 member institutions in Canada Roper has
  4 member institutions in Canada

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Two 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

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Centralized data archives

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

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Local data services

• Pros
• Close to the users
• 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 resources of the others
• Higher staff turnover dead end/partial jobs
• High steep learning curve

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The future a highly personal perspective

• New frontiers, not just new tools for old
  frontiers numeracy and GIS
• Multi/interdisciplinarity provides the ability to
  apply new independent variables to old dependent
  variables, and vice versa

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Satellite imagery
gynecology?
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MARC records in the economists toolbox.
Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
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Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
18
Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
19
Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
20
Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
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Source Alexopoulos, M. presentation to
University of Toronto conference. 2008
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A model of information

• Wisdom
• Knowledge
• Information
• Data

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One new frontier numeracy/statistical literacy

• John Allen Paulos/Innumeracy Darrell Huff/How
  to lie with statistics (1954) were not the first,
  but did much to popularize the problem of
  innumeracy
• Articulation has not made the problem go away
• Librarians involved in traditional and
  information literacy, also need to be
  numerically literate
• Simplest reading tables such as

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A question requiring a more sophisticated
response I need expenditures on eye care by age,
for as many years as possible.

• Requires
• Locating right data source
• Having managed the data set in a collection, and
  providing access to it
• Identifying the appropriate technique for
  generating the required descriptive statistics

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These are the published statistics. But the
student wants expenditure on eye-care by age of
household headsolution go to the original
source Survey of household spending
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to generate a table with the required
statistics from an original data source.
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Skills needed

• Understanding where statistics come from, ie how
  are they collected
• Interpreting statistics
• Critical understanding of statistics
• Understanding how statistics (especially
  descriptive statistics) are created
• And not being seduced by

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Another new frontier spatial literacy

• Monmonier, Mark H.J. de Blii/ How to lie with
  maps
• At its simplest, GIS software provides a way to
  display aggregate statistics, with a
  geographic/spatial attribute, in a graphic way
• Has made aggregate statistics much more
  comprehensible than published tables in books
  (whether print or pdf, or Excel)

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GIS (Geographic information systems) (contd)

• At a more sophisticated level, the ability to use
  spatial relationships as eg an independent
  variable in models
• a simple example, distance to an emergency ward
  as an explanatory variable for stroke survival
  rates

33
Supporting statistics/data and GIS resources as a
component of research

• Requires skilled staff
• All the usual resources
• All the usual library functions (acquisition,
  technical services, user services, planning),
  just done a bit differently.
• The skills are discipline-neutral descriptive
  statistics in environment are created just like
  those in sociology

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Is it worth it?

• Data GIS services reach a wide variety of
  users, from the Presidents office, to
  undergraduates in Religious Studies
• In 2006, 50 of the reference statistics in the
  combined Government Documents, Data and GIS
  department, came from Data and GIS.
• Government Documents reference statistics are
  declining those for Data GIS continue to rise

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In conclusion

• I reissue the challenge that Lucci, Rokkan and
  Meyerhoff issued in 1957
• Dare to deal not just with information, but a
  step earlier in the process, with data
• Incorporate data services as part of library
  services
• Preservation/archiving may benefit from other
  institutional models
• National data archives and local user services
  should be complementary take the folks from
  your national data archive to lunch!