Registration Instructions
Please:
- Pay with credit card (visa or mastercard), or choose offline for check/wire transfer payments.
- Do not log in if prompted.
- Check your contact information before submitting the registration. If you have a typo in your email address, you will not receive receipts or links to the event.
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.
Currently, there are no volume or bulk discounts for ELUNA Learns. ELUNA is committed to keeping the cost for 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 the ELUNA's ability to provide this programming. If you value this program and the 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
Analytics
October 13, 2021, 1:00 – 4:00 pm EST
1:00 pm - 1:05 pm. Introduction
1:05 pm - 1:45 pm. Figuring It Out with Alma Analytics.
1:45 pm – 1:50 pm. 5-minute break
1:50 pm - 2:30 pm. Refining Your Results in Alma Analytics.
2:30 pm - 2:35 pm. 5-minute break
2:35 pm - 3:15 pm. Finding Gold in Your Library's Data.
3:15 pm – 3:20 pm. 5-minute break
3:20 pm - 4:00 pm. Creating Replicable Metadata Analyses with Python, Pandas, and Jupyter.
Note, schedule times are approximate. Schedule may shift slightly during the event.
Figuring It Out with Alma Analytics. Tim J Siegel, Virginia Commonwealth University.
Have you ever found yourself staring at a screen of results in Alma Analytics and wondering what all of it means? Have you attempted to filter results, but what you see doesn't match your expectations? Have you ever found yourself looking for a certain bit of information you know should be there, but your searches keep coming up blank? If you answered “Yes” to any and/or all of these questions, then this session is for you. This session, a continuation of last year's entry, Fall in Love with Alma Analytics, covers a new range of analytics processes from simple to complex, and provides more tips and tricks for how to optimize your analyses. Come with your questions, as that's what analytics aims to accomplish: provide the right answers.
Refining Your Results in Alma Analytics. Mary Ellen Willemsen, WIN Library Network; Julene Laurel Jones, University of Kentucky.
Analyzing your data is important for all aspects of efficient library operations. We will be sharing some ideas to help you effectively gather and analyze your data using Alma Analytics. After our presentation, you will know how and why to create bins, how to save a filter for reuse in another analysis, and how to create and use a sub-query. We will also be sharing a couple of complicated formulas that are not included in Alma documentation.
Finding Gold in Your Library's Data. Mike Rossetti, myLIBRO; Greg Davis, Iowa State University.
Your library collects a tremendous amount of data - from your ILS, program and service fulfillment, door counts, financial systems and more. Hidden within is a treasure trove of valuable information some libraries are using to drive everyday decisions, while many more should be. The issue? Difficulty in obtaining the data in an understandable way. Learn how Iowa State University has been an instrumental partner in natural language processing in libraries over the past three years and how the partnership with myLIBRO continues to grow. Then explore how myLIBRO Insights is helping libraries increase access to data through patented natural language technology - think of it as Google or Siri for all your library data in one platform - without the need for data analysts or superusers. Spend less time preparing for meetings and financial presentations and more time taking positive action from all the data collected daily.
Creating Replicable Metadata Analyses with Python, Pandas, and Jupyter. Stacie Traill, University of Minnesota; Martin Patrick, University of Minnesota.
Many Alma users know that saved reports in Alma Analytics can be rerun on demand to replicate the same conditions and transformations. But what if you need to create a report that combines multiple data sources, relies on data not available in Analytics, or requires spot-checking the results of multiple complex data transformations? In this presentation, we will introduce the benefits and potential of building replicable analyses using the free, cross-platform, and well-documented combination of Python, Pandas, and Jupyter Notebooks. We will describe and demonstrate several replicable analyses we’ve created for various ongoing workstreams, including processing HathiTrust holdings, determining post-cancellation access for serials, identifying existing holdings for publisher ebook offers, and others. The examples we will share rely on data from Alma, but the techniques and tools are relevant for processing data from any source.