Custom datasets are useful for storing data as you would on a person record, but for a different type of object or constituency not included among standard datasets in Slate.

True to their name, custom datasets are entirely custom: many of the features taken for granted on the person record must be built from scratch in a custom dataset to achieve baseline functionality. These include: Display name, matching criteria, Lookup, and the "Create a New Record" form.

Before jumping into a custom dataset, familiarize yourself with the general principles of datasets. Your goal might be achievable with a Slate feature that requires a smaller commitment. 

✨ Tip: Start a conversation in the Community Forums to find the right case for a custom dataset.

Five steps required to create a custom dataset

To make a custom dataset, you'll do the following: 

1. Create the custom dataset

2. Create fields (including optional matching criteria fields) & prompts

3. Refresh the field and prompt caches, Configurable Joins Library

4. Create the New Record form

5. Create the display name rule (if applicable)

Step 1: Creating the custom dataset

To create a custom dataset:

1. From the main navigation, select Database.

2. Under Records and Datasets, select Datasets.

3. Select Insert. A popup appears.

   

4. Configure the following settings:

   • Status: Set to Active. Set datasets to Inactive when no longer in use without fear of losing data.

   • Folder: Keep custom datasets organized by placing it in a folder. Select Other to create a new folder.

   • Name: Enter the name of the dataset.

   • Type: A dataset’s type connects it to other Slate functionality. Enter a list of one or more of the following types separated by a comma:

     • school: This dataset will autosuggest on the Schools table.

       • Only one dataset in your database should have this type.

       • In most databases, this type is already used for Organizations (Admission & Enrollment, Student Success) or Educational Institutions (Advancement).

     • location: This dataset will autosuggest on the event field Location Name.

     • job: This dataset will autosuggest on the Jobs table.

       • Only one dataset in your database should have this type.

       • In most Advancement databases, this type is already used for Companies and Foundations.

     • relation: This dataset will autosuggest on the Relationships table.

       • Adds a Relationships tab to the dataset.

     • gift_fund: Links the Funds dataset to the Fund field within the Giving table (Advancement).

     • giving: Adds the Giving tab within the dataset (Advancement).

   • Parent: If this custom dataset is a child dataset to a parent dataset, select the parent from the list.

   • Custom Icon (Optional): Choose a custom icon to represent the dataset records in Lookup.

5. Select Save.

With the custom dataset created, we'll move on to creating its fields and, optionally, prompts. 

Step 2: Creating custom dataset fields & prompts

Because of their similarity, an understanding of the creation of custom person-scoped fields and their associated prompts is recommended to create custom dataset fields. Read more about custom person-scoped fields and prompts before continuing with this section.

You can always start small and expand later. Create the fields you think you'll need now, then continue to the next steps. 

Basic custom dataset fields

To create custom dataset fields:

1. From the main navigation, select Database.

2. Under Records and Datasets, select Fields.

3. Click New Field. 

4. Configure the following settings:

   • Status: Active

   • Scope Category: Records

   • Scope: Select the name of your custom dataset from the list.

   • ID: Enter a unique, computer-friendly ID. Use an easily-identified prefix at the beginning of all custom dataset field IDs for easy recognition and sorting. For example, a Volunteer dataset might have field IDs such as volunteer_email, volunteer_firstname, and volunteer_lastname.

   • Name: Enter a unique, human-friendly name. This should indicate to the viewer at a glance what the field is for. Will not be displayed externally. If your dataset records do not require a first and last name, use the Slate provided “Record Name” field as it does not need to be created custom.

   • Folder: Keep custom dataset fields organized by selecting a folder, or select Other to create a new one. We recommend placing custom dataset fields in a single folder so they can be viewed simultaneously.

   • Category: Select or create a Category for your fields, if desired.

   • Field Type: Select the appropriate Field Type for your field.

5. Click Save. Repeat for each field required in your dataset.

   

🔔 Important! Do not recreate standard field IDs.

Refresh the field cache

Click the link at the top of the Fields tool page to view newly created fields in forms and queries. Click Refresh Configurable Joins Library in Database to use the fields in Configurable Joins queries.

⚠️ Caution!

Some standard prompt keys require special configuration, including: bit, country, language, sex, state, and user. When creating fields that use these prompt keys, select Custom Configuration (Advanced Use Only) from the Field Type select list. Under Prompt, choose the prompt key. Then, under Value, select Store Value. See Customizing Standard Fields and Prompts for more information.

Optional: Custom dataset matching criteria

Unlike person records, custom datasets do not come with matching criteria by default. Where person records match on first name, last name, birthdate and email, dataset matching criteria must be established using a dataset row key or a unique-for-merging field.

The key (or field) is used in Upload Dataset and upon form submission to match new records to existing records, allowing for the updating of existing records and preventing the creation of duplicate records.

📝 Without a dataset row key or a unique field, all entries will create new records.

📖 Further reading: Unique for Merging

Custom dataset row key

The dataset row key:

• is typically controlled by the institution (using something such as a unique ID number).

• is universal for every record.

• is unique for every record.

• facilitates matching during import by referencing the related dataset.

When creating a parent/child dataset (Organizations and Organization Contacts), a key is required to enable matching during import.

To create a dataset row key:

1. Select Database on the top navigation bar and select Fields.

2. Click New Field. A popup appears.

3. Configure the following settings:

   • Status: Active

   • Scope Category: Records

   • Scope: Select the name of your custom dataset from the list.

   • ID: Enter a computer-friendly ID for the dataset row key. Use an easily-identified prefix at the beginning of all custom dataset field IDs for easy recognition and sorting. For example, a Volunteer dataset might have a dataset row key ID of "volunteer_dataset_row_key".

   • Folder: Keep custom dataset fields organized by selecting a folder, or select Other to create a new one. We recommend placing custom dataset fields in a single folder so they can be viewed simultaneously.

   • Dataset: Configure the field similarly to a typical person-scoped field, but select the name of the dataset within the Dataset setting.

   • Category: Select or create a Category for your fields, if desired.

   • Field Type: Dataset Row Key

4. Click Save.

   

Custom dataset unique for merging field

A unique-for-merging field:

• is typically related to optional data points that are most likely controlled by a third party.

• facilitates matching during import.

If you created a dataset key field, note that field is also unique for merging.

To create a unique-for-merging field:

1. Select Database on the top navigation bar and select Fields.

2. Click New Field. A popup appears.

3. Configure the following settings:

   • Status: Active

   • Scope Category: Records

   • Scope: Select the name of your custom dataset from the list.

   • ID: Enter a computer-friendly ID for the dataset row key. Use an easily-identified prefix at the beginning of all custom dataset field IDs for easy recognition and sorting. For example, a Volunteer dataset might have a dataset row key ID of "volunteer_dataset_unique".

   • Folder: Keep custom dataset fields organized by selecting a folder, or select Other to create a new one. We recommend placing custom dataset fields in a single folder so they can be viewed simultaneously.

   • Category: Select or create a Category for your fields, if desired.

   • Field Type: Select the appropriate Field Type for your field.

   • Unique for Merging: Set to Value contains a unique ID which identifies a single record for merging.

4. Click Save.

   

Optional: Custom dataset prompts

Unlike fields, prompts don't require a scope. They can be used with any field, regardless of those fields' scope.

To create new prompts for use with your custom dataset fields, follow the directions provided in the Prompts article. If you have a lot of prompts to enter, consider uploading in batch.

Since prompts don't require a scope, you can safely use prompt keys already in your instance with your new custom dataset fields by selecting them from the prompt select list when creating your field. Reusing existing prompts keys like prefix (Mr., Ms., Mx., Dr.) rather than creating new ones keeps Slate lean and clean.

⭐ Best Practice

Use the same ID prefix in fields and in prompt keys. For example, if custom dataset field IDs begin with volunteer_, custom dataset prompt keys should also start with volunteer_.

Refresh the prompt cache

Click the link at the top of the Prompts tool page to view newly created prompts in forms and queries. Click Refresh Configurable Joins Library in Database to use the prompts in Configurable Joins queries.

Step 3: Refreshing Caches and Libraries

Refresh the three main caches and libraries: that is, the prompts cache, the fields cache, and the Configurable Joins library.

1. From the main navigation, select Database.

2. Under Queries, select Refresh Configurable Joins Library.

   

3. From the main navigation, select Database.

4. Under Records and Datasets, select Prompts.

5. Click the link to manually refresh the prompts cache.

   

6. On the right sidebar, select Fields.

7. Click the link to manually refresh the fields cache.

   

With the caches refreshed, we'll move on to the New Record form.

Step 4: Creating the new record form

Similar to the default form that creates a new person record in Slate, you can make a form that creates records for other datasets.

Create the Form

1. Select Forms on the top navigation bar

2. Select New Form. A popup appears (pictured).

3. Configure each setting as necessary.
   📖 Further reading: Form settings

4. Click Save

   

Adjust the Scope

1. Select Edit Form.

2. Select Edit Properties.

3. Change the Scope of the form to Dataset

4. Select the appropriate Dataset from the Dataset list.

5. Configure other settings as necessary.
   📖 Further reading: Form settings

6. Select Save.

   

Customize the Form

Default Fields

Prior to changing the scope, the scope of the form was person. Therefore the default person scoped form fields should be removed. Hover over each field and select the X that appears on the right side of the field. Confirm the deletion by clicking OK on the dialog box.

Dataset-Scoped Fields

Add in the desired form fields for the new dataset record, mapping to your new dataset-scoped custom fields.

Required Form Fields

Make sure to map a form field to the Name and Key or unique-for-merging field.

Record → Name

Mapping to Record → Name sets the display name for the dataset record. If the name is determined by a calculation of form fields (such as first name + last name), include a hidden form field using a calculation formula. The following example illustrates a typical dialog:

System Field to Record → Key

Mapping to the Key or unique-for-merging field ensures matching of form submissions based on the established matching criteria. To map to the Key, set the System Field to Record → Key. To map to the unique-for-merging field, set the System Field to Fields → the unique-for-merging field for the dataset.

📖 Further reading: Creating a new record form for a custom dataset

Integrating the New Record Form

Now that you have created a record creation form, you can use it to replace the default New Record button functionality.

📝 Note

If you don’t see the Customized Views icon on the Lookup page, your database does not have the Configurable Joins Lookup enabled. Ask your Slate administrator to activate the ‘Configurable Joins Lookup’ configuration key in Database → Configuration Keys → Early Access Features. If they are unable to do so, see Custom Dataset Legacy Compatibility for instructions on adding your new dataset to the legacy Lookup tool.

1. From the main navigation, select Records.

2. Click the Customized Views icon in the top-right corner. A pop-up appears.

   

3. Click the Edit Shared Views link.

4. On the Shared Views page, click the Edit Lookup button.

5. Select the new record form you created in the dropdown menu.

6. Click Save.
   

Step 5: Create a dataset display name rule

If your dataset represents people with a first and last name, or if you want to concatenate multiple field values to create the display name for a dataset record, you need to create a Dataset Display Name Rule. This ensures a display name is set for all records in your dataset, even if they are created in a manner that does not set a value for the Name (sys:name) field.

To create a custom dataset display name rule:

1. From the main navigation, select Database.

2. Under Automations, select Rules Editor.

3. Click New Rule. A popup appears.

4. Configure the following settings:

   • Name: Enter a descriptive, human-friendly name, such as Dataset Display Name Rule

   • Type: Configurable Joins

   • Category: Records

   • Base: Select your new custom dataset

   • Rule Type: Name

   • Folder (optional): Keep custom datasets organized by placing it in a folder, or create a new one by selecting Other.

   • Non-deterministic: Rule is deterministic and has an exclusive priority

   • Priority: 1

   • Status: Preview

5. Click Save. You are redirected to the rule configuration page.

   

6. In the Action section, set the Action to Replace Values from Formula.

7. Click the subquery icon to the right of ‘Export’. A pop-up window will open.

   

8. Within the Edit Part pop-up, configure the following settings:

   • Name: name

   • Type: Dependent subquery

   • Output: Concatenate

   • Row Separator: leave blank

   • Row Offset: 1

   • Row Limit: leave blank

   • Export Separator: insert a single space

9. Within the Edit Part pop-up, click ‘Export’. Select the exports you would like to use to generate the name for your dataset records (e.g., “First Name” and “Last Name”). Click Save.

   

10. Within the Formula box for your rule, type the @ symbol. The name of your subquery export (name) will automatically populate. Click the export name to auto-fill the name in the Formula box.

11. Click Save. You are redirected to the All Rules page.

12. Select the rule from the list.

13. Click Edit.

14. Set Status to Active.

    

✨ Tip: If your database has many rules, you can search for your rule using the text box in the top right of the page containing the text “Search Rules…”

Optional: Create index/header rules

To customize search criteria for your custom dataset, follow the the article Dataset Index/Header Rules - Customizing Dataset Record Search Criteria.

Optional/Not Recommended: Configure the Dataset for Legacy Features

If your institution is still using local/Slate Template Library-dependent features, additional steps are needed to make your custom dataset usable within those tools. We strongly advise against this, as local/Slate Template Library-based features are no longer in development and are not supported by Technolutions. If at all possible, make the switch to Configurable Joins.

For instructions on adding legacy feature compatibility, see Custom Dataset Legacy Compatibility.