The dataset history helps you keep track of changes made to your dataset over time. Each time an entry is created, updated, or deleted, the system generates unique identifiers to help you trace the lifecycle of your data. The project owner and collaborators with access to all data will have access to this function.
1. Go to the Dataset window
2. Click on the three dots in the upper right corner
3. Select "History" from the dropdown menu
Understanding the Fields
Timestamp: The date and time of the action performed.
User: The name of the user who performed the action.
Action: A description of the action taken, such as creating a new entry or updating an existing one.
Original ID: The unique identifier of the initial dataset entry. This ID remains constant across all versions of the entry, allowing you to trace all revisions back to the original record.
Revision ID: The unique identifier for a specific version of the entry. Each time an entry is updated, a new Revision ID is generated to represent the latest version.
Example Scenario
1. Creating a New Entry
When a user creates a new entry, the system generates both an Original ID and a Revision ID. At this stage, both IDs are identical:
Original ID: 15542ce6d58426da4af33ad2d70bf7c
Revision ID: 15542ce6d58426da4af33ad2d70bf7c
Action: Created a new entry
2. Updating an Existing Entry
When the same entry is updated, the Original ID stays the same, but a new Revision ID is generated:
Original ID: 15542ce6d58426da4af33ad2d70bf7c
Revision ID: d92f0b15fd4640a86c5eec3a08892fd3
Action: Updated an existing entry
Filtering
You can filter through historical records using any of the available fields. For example, filtering by Action will show you all changes performed for one specific action, while filtering by User can help you audit changes made by individual users. By leveraging this feature, you gain visibility into data changes, enhancing both traceability and compliance with data governance standards.