Reorienting Collection Analysis: Cost-Effective Item-Level Analysis and Machine Learning in Public Libraries.
In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed...
| Published in: | Information Technology & Libraries Vol. 42; no. 4; pp. 1 - 7 |
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| Format: | tables/charts Journal Article |
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American Library Association
Dec2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174633061&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174633061 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07309295 ITL jtl: Information Technology & Libraries issn: 07309295 maglogo: N pubinfo: dt: Dec2023 vid: 42 iid: 4 pid: 55 pub: American Library Association place: Chicago, Illinois artinfo: ui: 174633061 174633061 174633061 10.5860/ital.v42i4.16987 174633061 ppf: 1 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Reorienting Collection Analysis: Cost-Effective Item-Level Analysis and Machine Learning in Public Libraries. aug: au: Hanney, Ross affil: Staff Development Coordinator, St. Joe County (Indiana) Public Library sug: subj: Libraries, Public Economics Machine Learning Algorithms Rural Areas Information Technology Cost Effectiveness Analysis Item Analysis Indiana Data Analytics Integrated Library Systems Cost Savings Library Circulation Economics Artificial Intelligence Software Design Data Analysis Neural Networks (Computer) ab: In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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