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From PDA/DDA to EBA: Following the Data

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Presentation on theme: "From PDA/DDA to EBA: Following the Data"— Presentation transcript:

1 From PDA/DDA to EBA: Following the Data
Monica Metz-Wiseman, Interim Director, Academic Resources Laura Pascual, Electronic Resources Librarian University of South Florida ALA Midwinter, 2016

2 All those acronyms DDA - Demand-Driven Acquisitions
PDA - Patron-Driven Acquisitions STL - Short-Term Loan EBA - Evidence-Based Acquisitions

3 Overview Why PDA? Why EBA? Impact of using data

4 Who we are Comprehensive, multi-campus research university, FTE: 32,400 – 48,300 headcount University of South Florida is one of the twelve public universities in the Florida system

5 Our materials budget Approval plan and $1.5 million -print monographic spend (2010) Shift from 24% of budget for print monographs (2010) to .5% (2015) Overall materials budget from Protect the journal

6 PDA/DDA program EBL/ProQuest
# of visible ebooks varies 250,000 – 400,000 Average spend per year: $350,000 Owned titles 9600+ 3 STL’s prior to purchase Dynamic profile Student funded

7 PDA/DDA data – what do we get?
Content Data for all titles on the platform almost 780,000 titles Cost Expenditure data by title/transaction about 77,000 transactions Usage Counter Book Reports 1,3 & 6 PDA Usage by title/patron/use type 72,634 titles used. 41,489 unique users.

8 PDA/DDA data – what can we do with it?
Program Management Catalog Maintenance Collection Assessment Drive Purchasing

9 PDA/DDA data – how do we manipulate it?
Tools Platform Admin (LibCentral) ILS (Aleph) Excel, Access MarcEdit, Notepad++ Skills Data Transfer Matching Grouping Queries Processes Standard Weekly, Ad Hoc Reports

10 PDA/DDA data – what we learned
Analysis of Content, Cost, Usage by Publisher Discipline/Subject Purchase Type Periodic/Over Time Timing/Course related Rising STL Costs, Embargoes and Financial Burden Increased Profile Management Indications for EBA

11 Evidenced-Based data Less data due to one publisher
Cross-platform duplication with aggregators Less data analysis required

12 Evidence-Based programs
Wiley (3 years) - funded Project Muse (2 years) – funded Oxford University Press (pending) Taylor & Francis (pending) Elsevier - funded

13 Impact of using the data
Compelling argument Domino effect Funding during a weak economic period

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