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ELUNA learns - Metadata Management and Government Documents. On Demand - Access for a year post event.

  • 1 Nov 2023
  • 1:00 PM (EDT)
  • 31 Dec 2024
  • 3:40 PM (EST)
  • Recorded November 1, 2023. On Demand - Access for a year post event.
  • 1927



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Each session contains multiple presentations and will cost $25 per attendee. The $25 registration fee is per session/per attendee for the live session and/or on demand recording. The registration and on-demand link are registered to each individual participant's email address. 

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ELUNA learns - Metadata Management and Government Documents.

  • 1:00 pm - 1:05 pm. Introduction 

  • 1:05 pm - 1:50 pm.MARCIVE Data Enrichment at Penn 

  • 1:50 pm – 2:00 pm. 10 minute break 

  • 2:00 pm – 2:45 pm. Resolving Mixed-Format Government Document Records in an Alma Network Zone 

  • 2:45 pm – 2:55 pm. 10 minute break 

  • 2:55 pm – 3:40 pm. Rectify eResource Records Using Normalization & Indication Rules, OpenRefine, and Python Script 

Note, schedule times are approximate. Schedule may shift slightly during the event. 

MARCIVE Data Enrichment at Penn (Gold Sponsor). Beth Picknally Camden, Retired Former Goldstein Director of Information Processing, University of Pennsylvania Libraries; Rachelle R. Nelson-Resource Management Coordinator, University of Pennsylvania Libraries; Katherine M. Schultz, Applications Developer, University of Pennsylvania Libraries; Ligia Gomez, Director of Marketing and Sales, MARCIVE, Inc.  

In the spring of 2022, the University of Pennsylvania Libraries partnered with MARCIVE on a project to upgrade and enrich our entire Alma database. The MARCIVE services included name and subject authority control, RDA upgrades, table of contents notes, and summary notes.  The process included data extraction from Alma, MARCIVE processing and data reload to Alma. This presentation will include Penn’s reasons for pursuing this project, the steps involved, problems along the way, outcomes, and plans for using the enriched data.   

Resolving Mixed-Format Government Document Records in an Alma Network Zone. Alex Chisum, Head Discovery Data Services, Florida State University; Natasha Owens,  Data Management Consultant; Florida Virtual Campus Library Services (FLVC) . 

The consortium of Florida college and university libraries is currently in the process of remediating multiformat government document records shared between institutions in the Alma Network Zone. This project presents a number of challenges. Some records contain institution-specific metadata that needs to be preserved, while, in other instances, different institutions have holdings for different formats on one record. So how do we untie this knot? The ability to unlink from and relink to the NZ has proved to be a useful tool in cleaning shared but messy metadata. In this presentation, we will discuss the methods used to identify problematic records in the NZ, compare institutional holding formats to the formats of corresponding records in OCLC, and use the unlinking/relinking process to preserve local metadata while improving the shared catalog wherever possible. We will also discuss plans to remediate Institution Zone records when relinking isn’t practical. 

Rectify eResource Records Using Normalization & Indication Rules, OpenRefine, and Python Script.  Elaine Dong, Database and Metadata Management Librarian, Florida International University. 

In this study, the author presents an enhanced methodology for rectifying fields containing subfield $5s across more than 680,000 eResource bibliographic records within the Florida International University (FIU) Libraries. The rectification process is achieved through the strategic utilization of diverse tools, including Alma Normalization and indication rules, OpenRefine, Excel, and Python Scripting. The application of these automated tools proved to be exceedingly efficient, saving a substantial amount of time in comparision to manual record correction.  

The key phases of the remediation process encompass the following steps: search and create set in Alma, filter set with indication rules, run job to export bibliographic records, run Python Script to extract pertinent fields and subfields, review and analyze data using OpenRefine and Excel, and create and apply norm rules to correct or remove identified fields and subfields.

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