The Metadata module is used to analyze the metadata associated with data that have been integrated into elluminate. This ensures that the data are in the expected structure for a single or for multiple studies. To manage these expectations, Metadata can be browsed, searched, compared, and exported.

The Metadata module is available for all elluminate users and takes into consideration which studies and global data stores the user is authorized to view.

Browse Metadata

1. From the Platform Menu, click Metadata under Metadata Management. By default, the Browse tab displays the latest version of the first listed study.
   
    

   
   Tip:
   By default, rows are filtered and highlighted to display new or changed metadata. The New and Changed buttons are highlighted to indicate active filters. Highlighted rows appear across all lists based on the selected filters, including Data Stores, Domains, and Variables.
    - Green rows indicate new additions.
    - Yellow rows indicate changes since the last import or metadata version.
    - Red rows indicate data deleted since the last import or metadata version.
   
   Selecting Differences filters the lists to display only items that have changed since the previous import or metadata version.

2. Click the Study drop-down and select the study or global data store (global data stores are listed at the bottom of the list, scroll to see all studies and global data stores). Data stores for the selected study display.

3. Select the study version from the drop-down list. By default, the latest version, sorted by Create Date, displays. Selecting a different version allows previous metadata versions to be viewed.

   Metadata versioning supports tracing changes to studies or global data stores at the study, domain, and variable levels over time. This includes identifying newly added datasets and changes made to variables across versions.
   
   
    

   Note: If only one entry exists, the metadata has not changed since the initial import.

4. The Data Stores list includes the data store name, number of domains, variables, and rows, creation date, selected tags, and last refresh date. To view corresponding domains, click a data store. The domains display in the lower section of the window.
   
5. The Domains list includes the domain name, domain label, number of variables and rows, the data source (for example, RAVE, FTP, Mapper), selected tags, last refresh date, and data date. To view corresponding variables, click a domain. The variables display in the right-side panel.
   
   For details on using the Data Lineage button, click View Data Lineage.
   For details on using the Domain History button, click View Domain Version History.
   For details on using the Delete button, click Delete a Domain.
   
   

6. The Variables list includes the variable name, variable label, data type, length, and critical flag.
   

   Note: Studies with the eIQ Data Classification algorithm enabled include three additional columns: Concept Name Tags with an algorithm-generated similarity score, Concept Label Tags, and Concept Domain Tags. The algorithm uses SDTM and CDASH standards to identify common field concepts and matches study source fields that share those concepts. A similarity score is assigned to each match to indicate the strength of the association.

7. To open the Variable Version History window, click a Variable Name.
   
8. To filter and view differences across all three lists, select the filter buttons to display only data that meets the criteria. By default, New and Changed are selected. Highlighted buttons with solid icons indicate active filters.
   1. Click the Differences button to view data stores, domains, and variables that have changed since the previous import / metadata version.
   2. Click the Deleted button to view data stores, domains, and variables that were deleted since the previous import / metadata version. Deleted items display highlighted in red.

Export Browse Results

Data from any data store can be exported and downloaded. Details for the selected data store are included in the output.

1. Click the row of the data store to export. The Export Excel and Export XML buttons in the upper-right corner become active.
2. Click Export Excel or Export XML. 

When exporting to Excel, the output includes a summary worksheet followed by one worksheet for each domain in the data store. Domain worksheets include Variable Name, Variable Label, Data Type, Length, and SAS Format.

The XML export downloads an XML file as pictured below. 

View Data Lineage

View data lineage for domains that undergo transformations in Mapper. The Sankey chart provides a visual that displays the selected domain and any upstream or downstream mapping dependencies.

1. Click a domain.
2. Click the Data Lineage button. The visual opens in a separate window.
3. Click the Close or the 'x' to close the window.

View Domain Version History

1. Click a domain.
2. Click the Domain History button.
   
3. Click the Close or the 'x' to close the window.

Delete a Domain

During the course of a study, a domain may be added accidentally due to an importDomain History elluminate. The ability to delete domains is restricted to users with the Metadata Editor privilege.

Important: This action cannot be undone. Be very careful with this feature.

1. Select the study data store that contains the domains to be deleted.
2. Select the checkbox for the domains to remove. The Last Refresh column can be used to identify the correct data to delete.
3. Click the Delete button. The Delete Summary window opens, displays any dependencies, and prompts for confirmation. 
   
4. Click Delete to remove the selected domains, or click Cancel to cancel the action.

Add Tags to Data Stores and Domains

Tags identify Data Stores and Domains with shared usage for exporting. A Data Store or Domain can have multiple tags. Tags applied at the Data Store (schema) level apply to all current and future domains. Only users with the Metadata Editor privilege can apply tags.

1. From the Data Stores or Domains list, click the drop-down arrow in the Tags column.
   1. Use the Search field to filter the tags list, if needed.
2. Select tags by checking the checkbox next to each tag.

Designate Variables as Critical

Users with the Metadata Editor privilege can designate variables (fields) as critical within a domain. Variables marked as critical are flagged in Data Central Issues and Queries listings.

1. Select the study or global data store, then select the data store and domain to display the list of variables.
2. Select the checkbox in the Critical column to mark variables as critical.
   
3. Click Save.