1. Shared Print Monographs: Emerging Trends
PAN Forum/ALA Annual
Chicago
June 28, 2013

2. The Problem Stacks are overcrowded
Use of print books is low and declining
Library space is wanted for other purposes
Print redundancy is significant
The cost of keeping books on shelves is high
Alternatives exist, but data is scattered
Traditional approaches to deselection are costly and time-consuming
Others:
If it is available for sale
Sustainablecollections.com

3. Monographs are different
Higher numbers
Low yield per decision
Stronger emotional attachment
Less acceptance of screen-based reading for long-form material
Physical delivery is a larger factor

4. Two strategies Independent action in a collective context
Formal shared print programs (collaborative activity and decisions)

5. Independent action in a collective context
Sustainablecollections.com

6. Shared Print Monographs Initiatives
Maine Shared Collection Strategy
Michigan Shared Print Initiative (MI-SPI)
California State University LOFT
Connect New York
Tri-University Group (Ontario)
Central Iowa Collaborative Collections Initiative
Washington Research Library Collection (WRLC)
FLARE (Florida), GWLA, SCELC
Northeast Regional Library Print Management Project
Sustainablecollections.com

7. Sharing Pros Buy-in; safety in numbers Amortization of retention costs
Reaffirms the library ethic
Makes sense, especially for low-demand material

8. Sharing Cons Adds complexity Divergent data and systems
Multiple, sometimes conflicting objectives
Shared decisions take more time

9. The Arc of a Project Origins: Vision, Mandate, Goals
Communication, Project Management & Decision-Making
Group Policies: the Memorandum of Understanding
Data Wrangling for Shared Print
Sharing the Benefits & Monitoring Progress
What does success in shared print look like?

10. Strong preferences: print, self-sufficiency
Hathi Trust or other digital surrogate
Print in Regional/National Collection
Print in state
Print within group
Sustainablecollections.com

11. Strategic issues
Slow build (small groups) vs. grand vision (mega-regions)
Data scales more easily than communication & decision-making
Discovery & delivery loom large, even if material is unlikely to be used
We are just beginning this work