Calendaring datasets: everything you need to know

The Calendaring dataset allows you to keep track of the number of room reservations, librarian appointments, and events at your library. If you subscribe to LibCal, you can actually sync this data automatically from your Room Bookings module, Appointments, and Calendars. The data is synchronized daily, giving you an updated look at your activity over time.

Example analysis report for a Calendaring dataset

In this Springboard, you'll learn how to create and configure a calendaring dataset, as well as record and analyze data.


Create a calendaring dataset

Calendaring datasets are only available to LibInsight Full customers. If you're interested in upgrading to the Full version of LibInsight, contact our Springy Sales team.

To create a new dataset, Admin users can go to Admin > Manage Datasets and click the Add New Dataset button.

Navigating the the Manage Datasets page and adding a new dataset

Before you begin, you may find it helpful to do a little planning. This can make the process of creating your dataset go more quickly.

  • How are you going to enter your calendaring data? A Calendaring dataset can be configured to:
    • Manually enter or upload aggregate totals. Use this option if you only want to gather the aggregate numbers of room bookings, appointments, and events, but do not have a LibAnswers system.
    • Automatically import aggregate activity data from your LibCal system. This will run a daily import of the total number of room bookings, appointments, and events from your LibCal system. This does not import individual booking, appointment, or event details -- just aggregate totals.
  • Who should have access to the dataset? By default, only Admin users are allowed to manage, record data to, and analyze a dataset. However, you can choose to extend each of those permissions to selected Regular users, or all Regular users in your system. For example, if you only want a few people to add data to your dataset, you would extend them Record permissions. But, if you want everyone in your library to be able to view and analyze (but not edit) the data, you could choose to give Analyze permissions to all Regular users.

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Record data or import from LibCal

There are two primary ways you can add data to your calendaring dataset:

  • Manually enter or upload aggregate data: if you configured your dataset to record aggregate data, you can manually enter daily or monthly totals, or upload the data from a CSV or Excel file.
    • LibInsight will provide you with the list of field names your file must include in order to be uploaded successfully.
  • Import from LibCal: if you connected your dataset to your LibCal system, LibInsight will automatically import the previous day's data each night.
    • For example, the data from January 1 will be available on January 2, and so on.
    • This follows the initial import of your data when setting up your dataset.

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Analyze a calendaring dataset

When analyzing a calendaring dataset, you'll find the following reports:

  • Overview: breaks down your room bookings, librarian appointments, or events by day, month, or year. This will allow you to analyze your totals over time.
  • Distribution: gives you a breakdown of your room bookings, librarian appointments, or events by day of the week, weekday/weekend, and month. This can help you identify your busiest days of the week, busiest months of the year, and how busy you are during week vs. the weekends.
  • Trends: allows you to compare the current date range against the same period of time in previous years. This can help you see the overall trend, as well as year-over-year differences.

In addition, LibInsight includes other tools to help you analyze, visualize, and share your data with others:

  • Dashboards: dashboards allow you to display charts of key data points from one or more datasets on a single page. Dashboard pages can be public or private, allowing you to create and share dashboards internally with staff or with the public.
  • Cross-Dataset Analysis: allows you to compare up to 4 variables from across one or more datasets. This can provide some really useful insight for seeing trends across services or resources over time.

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