Attribute Lists in Signals Notebook

In this document, you will learn how to utilize attribute lists in Signals Notebook, which are predefined value lists that ensure consistency and uniformity across experiments. The creation and management of these lists are exclusive to administrators with the appropriate user roles. Three types of attribute lists are available: inline lists, internal search lists, and number sequences.

Step 1

Welcome! This video will guide you through the process of using attribute lists in Signals Notebook by Revvity Signals Software. These are predefined value lists designed to promote consistency and uniformity across experiments.

Step 2

Attribute lists allow you to create globally accessible lists for various purposes, enabling centralized updates from a single location. Note that the creation and modification of these lists are limited to administrators with the user roles that authorize them to configure the system or manage attributes. There are three types of attribute lists: inline lists, internal search lists, and number sequences.

Step 3

Let's explore each type of attribute list. First, we have inline lists, which contain static values that you define manually or import via CSV.

Step 4

Observe as I create a new inline list for departments. Values can be added individually or imported.

Step 5

Take note of the parent-child relationship feature. I am creating a new attribute list named "Teams," which will be a child of the first attribute list. I will add the values using my CSV file.

Step 6

Now, let's look at the end-user interface. Selecting one value automatically filters options in related fields, establishing hierarchical dependencies. Next, let's explore internal search lists.

Step 7

Internal search lists dynamically display content from existing Signals Notebook data. Unlike static lists, these lists carry live data from your experiments, samples, or materials. Here, I'm creating a search list for active projects.

Step 8

I define the search criteria, set the refresh frequency, and the system automatically updates as new projects are added. No manual maintenance is required. As a result, if I create a new project on the end-user interface, update the lists, I can now see the newly created project in the list.

Step 9

Finally, let's discuss number sequences. These lists automatically generate sequential identifiers. I'm creating an experiment numbering scheme with the prefix "EXP," user initials, and a four-digit counter.

Step 10

This system generates unique IDs like "EXPMJ01" automatically when users create experiments, eliminating numbering errors and ensuring consistency. An attribute list can also be part of the name. If your experiment is associated with a project via an attribute list, it can be part of the experiment's name.

Step 11

These three types of attribute lists provide comprehensive data standardization: inline lists for controlled vocabularies, internal search lists for dynamic data references, and number sequences for automatic unique identification. Together, they ensure data consistency while reducing manual effort across your organization.