Laine Ruus University of Toronto.Data Library Service

 The DIKW model and where libraries fit  Where data have been (and are still)  Data and the reference librarian

Wisdom Knowledge Information Data

 Set up, resourced and trained to locate information that exists  Not set up, resourced or trained to generate information that does not yet exist

 Population census  Public opinion polls, social surveys since early 1900s

 Early data archives (1940s and on) in local academically-based survey institutions and data archives  Later, national data archives, esp in Europe  1957 Lucci et al report, suggested libraries as appropriate organizations in which to situate data services  At UBC, Jean LaPonce, 1972, like Columbia/EDS, one of first with Library involvement  in USA, ca 42% of academic services in libraries

 Puts user services close to users, but not close enough; data services usually a discrete entity  Possibility of a quantitative answer should be part of every reference interview  Now possible – convergence of Inet, DDI metadata standard, and rise of 3 rd generation interfaces to microdata: SDA, Nesstar, VDC

 The hardware and software are now available to support generation of descriptive and inferential statistics from raw microdata at the reference desk  Requires: Numeracy: how to interpret statistics Knowledge how and by whom data are collected Knowledge how data become statistics

 Only data management needs to be a discrete entity  Quantitative reference services should be part of every reference tool basket