Thorough assessments of subscriptions are unwieldy and time- consuming to perform every year. A metric has been developed for standardizing the process, with a Web-based platform being constructed to recommend renewals and
cancellations. This session will describe the metric and demonstrate the functionality of the platform, engaging the audience in making refinements. Coded in PHP and utilizing a MySQL database on the backend, the completed product will be an open-source Ongoing Automated Review System (OARS) for subscription reviews.
OARS will automate the selection of titles and data for review, which will result in a semi-automated process for annual renewal decisions. OARS will utilize multiple, customized variables, as well as an adjustable cumulative weighted scale of all variables which the system will use to recommend a renewal decision. OARS will use automated processes as much as possible, and will also provide data entry forms and uploads from files for easy input. There will also be an interface for stakeholders to view the data and respond to the review.
All of the variables and the draft requirements for OARS will be shared during the session. The variables include faculty ratings, cost, usage, Eigenfactor, and entanglements (such as consortial agreements, bundled packages, or cooperative collection development commitments).
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
OARS: Toward Automating the Ongoing Subscription Review
1. OARS: Toward Automating the
Ongoing Subs ription Re ieOngoing Subscription Review
Geoffrey P. Timms
Mercer University
i d
Jonathan H. Harwell
Georgia Southern University
jh ll i h d timms_gp@mercer.edujharwell@georgiasouthern.edu
2. Context
Georgia Southern University
Doctoral-Research University
FY09: $300 000 collection budget cutFY09: $300,000 collection budget cut
FY10: $470,000 collection budget cut, g
FY11: $1.2 million total budget
C&RS: 4 librarians & 12 ½ support staff
3. Needs
Balance the budgetg
Rapid assessment
Maximum input from stakeholders
St li th f l tStreamline the process for long-term use
Control the data vs. data controlling usControl the data vs. data controlling us
4.
5. What is OARS?
Ongoing Automated Review System
Automate the selection of titles and
data for review, which will result in a,
semi-automated process for annual
renewal decisionsrenewal decisions
Phase 1 & Phase 2
Two development guidelines:
SimpleSimple
Open source
6. Variables/Data Tracked
Title
U i C t l #
Entanglement
N tUnique Control #
ISSN
Notes
Current Cost
EISSN
LC Call Number
Usage Data
Faculty Rating
Year
Publisher
y g
Eigenfactor Percentile
u i e
7. “Discarded” Variables
lAlternative coverage
Frequency/cost per issue/useq y p
Faculty format preference
Peer reviewPeer review
Print cost
Online only cost
Print plus online costp
Usage Year 2
Librarian ratingLibrarian rating
ILL borrowing (will be in Phase 2)
8. Structure
L.A.M.P environment
MySQL database on backend
PEAR MDB2 Abstraction LayerPEAR MDB2 Abstraction Layer
Coded in PHP & JavaScriptJ p
Input via .csv upload or forms
Data export in .csv format
10. OARS Recommendation Metric
Relative Cost
Cost as % of average cost 80
100
120
ore
Calculation of Cost Score
Cost as % of average cost
(-0.5 x Cost as % of av. cost) + 100
Score range limited to 0-100 0
20
40
60
80
CostSc
g
Covers Cost at 0-200% of av.
cost
0
0 50 100 150 200 250 300
Cost as % of average cost
Relative Usage
U % f 80
100
120
ore
Calculation of Usage Score
Use as % of average use
(0.5 x Use as % of av. use)
Score range limited to 0-100 0
20
40
60
80
UsageScg
Covers 0-200% of av. use
0
0 50 100 150 200 250 300
Use as % of average use
11. OARS Recommendation Metric
Weighted Metric
% weighting applied to each of the four variables% weighting applied to each of the four variables
Total weighting = 100%
Even weighting
Cost Score Usage Score Rating Eigenfactor TOTAL
44 65 50 85.944 65 50 85.9
Weight 25% 25% 25% 25% 100%
Net Score 11 16.25 12.5 21.48 61.23
Usage/Eigenfactor preferred
Cost Score Usage Score Rating Eigenfactor TOTAL
44 65 50 85 944 65 50 85.9
Weight 10% 35% 10% 45% 100%
Net Score 4.4 22.75 5 38.66 70.81
23. Challenges
How to treat titles where usage data unavailable
Divide the usage data proportion of weighting
among the other data pointsamong the other data points
Library of Congress Call Numbersy g
MySQL Boolean Searching – spaces and periods
Sorting
Required normalizationRequired normalization
Complex Regex (Cheers to Bill Dueber)
Reverse normalization
MySQL Natural Language Search
Minimum is four-character stringMinimum is four character string
ACM, ACS, etc. cannot be searched
MySQL can be locally re-configured
25. h kTh k !Thank you!Thank you!yy
Jonathan H. Harwell
jharwell@georgiasouthern.edu
Geoffrey P. Timms
timms gp@mercer.edu
image from http://www.blogcdn.com/www.gadling.com/media/2008/05/oars.jpg
timms_gp@mercer.edu