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ELUNA learns - Analytics. On Demand - Access for a year post event.

  • 25 Oct 2023
  • 1:00 PM (EDT)
  • 31 Dec 2024
  • 3:40 PM (EST)
  • Recorded October 25, 2023. On Demand - Access for a year post event.
  • 1847

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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. 

ELUNA is committed to keeping the cost of our educational events affordable, especially in these budget-challenged times.  In order to provide quality member-based programming, we ask that individuals register for each of the events that are relevant to their work. Sharing login information for group-viewing sessions undermines ELUNA's ability to provide this programming. If you value the program and community that provides this content, either via the face-to-face meeting or online, please register for each session you will attend with your email account.

ELUNA learns - Analytics

  • 1:00 pm - 1:05 pm. Introduction
  • 1:05 pm - 1:50 pm. Automate Your Team’s Professional Development Using Linked Data
  • 1:50 pm – 2:00 pm. 10 minute break
  • 2:00 pm – 2:45 pm. Under the hood of the FLVC statewide IPEDS/ACRL Analytics dashboard
  • 2:45 pm – 2:55 pm. 10 minute break
  • 2:55 pm – 3:40 pm. Creating an Alma Analytics dashboard for cataloging statistics data

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

Automate Your Team’s Professional Development Using Linked Data (Gold Sponsor). Jamie Lin, Head of Community and User Research, Skilltype, Inc.

In an evolving library landscape, seamless access to a centralized repository for training and in-house expertise is more crucial than ever. How can you effortlessly find what you need when you need it without the typical hassle? Led by Jamie Lin from Skilltype, this session focuses on the user's journey through their organization's training page. Here, you'll uncover how to seamlessly locate a diverse range of both internal and community-based training resources. The session will also showcase how to explore and create curated lists, find resources related to specific skills like AI, and identify Ex Libris users who are experts in areas that matter to you.

Under the hood of the FLVC statewide IPEDS/ACRL Analytics dashboard.  Jay Wiese, Alma Analytics Specialist, Florida Virtual Campus Library Services (FLVC); Hisham Makki, Assistant Director, Data Quality, Florida Virtual Campus Library Services (FLVC).

A look at 3 distinct challenges encountered when harvesting loan counts, physical items counts, and E-Inventory counts in Alma Analytics, from a catalog shared by 28 Florida colleges and 12 universities, for the 2023-2024 IPEDS and ACRL surveys:

1. How we used a saved column to consistently determine IPEDS/ACRL Material Type (books media, serials) for multiple reports in different Analytics subject areas

2. How we built report filters to give a ""snapshot"" of inventory as of a specific date for E-Inventory and Physical Item IPEDS/ACRL counts--including inventory that was discoverable as of a specific date, but which has subsequently been withdrawn

3. How we used a complex filter to exclude PDA discovery records from IPEDS/ACRL E-Inventory counts"         

Creating an Alma Analytics dashboard for cataloging statistics data. Minna Popkin, Metadata Management Librarian, Harvard Library.

In this presentation I will demonstrate how I created a dashboard in Alma Analytics for cataloging staff and managers to view their cataloging statistics data.  The data is based on a local holdings record field, which is mapped to a Holdings Local Param; I will explain how that field is configured and populated in Alma, and how the data is parsed in Alma Analytics.  I will outline the steps I took to create the dashboard and demonstrate how it is used.  I will also discuss the benefits and tradeoffs of this approach for keeping and exploring cataloging statistics data.