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ILL datasets: everything you need to know

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The ILL dataset allows you to record and analyze your aggregate interlibrary loan data. 

  • Analyze your total borrowing and lending volume by day, month, or year.
  • See the ratio of article requests vs. loan requests.
  • Break down your requests by status and reason for cancelation.
  • You can also track borrowing and lending with partner institutions, which is great if you need to compile stats for regional or consortial groups.

Example of an ILL dataset

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

Create an ILL dataset

ILL datasets are only available in LibInsight Full. 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.

  • What type of recording mode works best for your data? When you start recording data, the type of recording mode that you've selected will determine how records will be added to the dataset. You can choose from: manual entry or file upload. 
  • 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.
  • What ILL categories will you be tracking with your ILL data? By default, you can record data for articles and loans by their status: filled, in process, cancelled, and submitted. However, you can also create categories to track additional stats, including cancelled requests and partner institutions. Knowing exactly what categories you want to track is important/essential for when you start recording data.

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Record data to an ILL dataset

Records can be only be added to an ILL dataset by the recording mode that you selected for the dataset. When you've chosen manual entry, you can only record data to the dataset by entering it in each cell within the dataset. And when you've chosen file upload, data can only be added to the dataset by uploading it via a spreadsheet. As you're preparing your data for uploading, you will need to make sure that the columns in your file match exactly with the fields that you have set up in the dataset. Otherwise, your upload will fail.

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Analyze an ILL dataset

Once you have some data in your dataset, you can utilize LibInsight's powerful analysis tools to get a better understanding of what the data you've collected means. For ILL datasets you'll have access to:

  • Overview report: breaks down the number of borrowing and lending requests submitted by month and request type (article and loan).
  • ILL Details report: breaks down your data by status (cancelled, filled, and in process) and request type (articles and loans) for borrowing and lending.
  • Cancellations report: if you have created categories for Cancelled Requests, this report will break down the total number of requests cancelled by category for borrowing and lending..
  • Partner Institutions report: if you have created categories for Partner Institutions, this report will break down the total number of requests filled by category for borrowing and lending.

Additionally, you can compare your ILL data with other datasets using the Cross Dataset Analysis tool. For example, you may want to compare your ILL Lending totals with your circulation dataset to see how many books you are lending out via ILL versus local checkouts.

Lastly, you can pull your ILL data into a dashboard to provide an overview of how ILL impacts your library's metrics. Dashboards can be used internally, or you can create public-facing dashboards to give your community real-time insight into the usage of their library's services.

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