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From information to data, and back again. Laine Ruus University of Toronto.Data Library Service 2009-03-13. Overview. The DIKW model and where libraries fit Where data have been (and are still) Data and the reference librarian. The DIKW model. Wisdom Knowledge Information Data.

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From information to data and back again

From information to data, and back again

LaineRuus

University of Toronto.Data Library Service

2009-03-13


Overview
Overview

The DIKW model and where libraries fit

Where data have been (and are still)

Data and the reference librarian


The dikw model
The DIKW model

Wisdom

Knowledge

Information

Data


Library systems are
Library systems are…

Set up, resourced and trained to locate information that exists

Not set up, resourced or trained to generate information that does not yet exist


Data have been around for a long time
Data have been around for a long time

Population census,since 1600-1700s

Public opinion polls, since early 1900s

Social surveys since early 1900s


And they have been hiding in
…and they have been hiding in…

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, one of first with Library involvement, like Columbia/EDS,

in USA, ca 42% of academic services in libraries


North american model
North American model

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 3rd generation interfaces to microdata: SDA, Nesstar, VDC


Requires a new set of skills
Requires a new set of skills

  • 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


Time to take up lucci et al challenge
Time to take up Lucci et al challenge

Only data management needs to be a discrete entity

Quantitative reference services should be part of every reference tool basket


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