Future of social science and libraries

Future ofsocialscience 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

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

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

Why data services began in the social sciences (cont’d) • 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

Why data services began in the social sciences (cont’d) • 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

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 Systems 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 • 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

…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

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

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

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

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

Satellite imagery… & gynecology?

MARC records in the economists toolbox…. Source: Alexopoulos, M. presentation to University of Toronto conference. 2008

Source: Alexopoulos, M. presentation to University of Toronto conference. 2008

A model of information Wisdom Knowledge Information Data

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 …

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

These are the ‘published’ statistics. But the student wants expenditure on eye-care by age of household head…solution go to the original source: Survey of household spending

…to generate a table with the required statistics from an original data source.

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 …

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)

GIS (Geographic information systems) (cont’d) • 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

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

Is it worth it? • Data & GIS services reach a wide variety of users, from the President’s 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

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!

Future of social science and libraries