Organization Strategies
By Project
Organize datasets by the project they belong to:- Group related data together
- Use clear naming conventions
- Add descriptions for context
By Data Type
Separate datasets by data format:- Images for 2D annotation
- Point clouds for 3D annotation
- Video sequences
By Stage
Track data through your pipeline:- Raw uploads
- In progress (being annotated)
- Completed (ready for training)
Tagging Best Practices
Use consistent tags for easy filtering:| Category | Example Tags |
|---|---|
| Status | raw, annotated, reviewed |
| Source | batch-1, batch-2, external |
| Domain | indoor, outdoor, aerial |
| Quality | production, test, validation |
Search and Filter
Mission Control supports powerful search:Search Operators
field:value- Exact match"quoted phrase"- Phrase match-term- Exclude term
Filterable Fields
| Field | Description |
|---|---|
visibility | public or private |
data_type | image, video, lidar |
owner | Dataset owner username |
Bulk Operations
Select multiple datasets for bulk actions:- Bulk tag: Add or remove tags from many datasets
- Bulk delete: Remove datasets (with confirmation)
- Bulk visibility: Change visibility settings
Dataset Settings
Visibility
- Private: Only you and collaborators can access
- Public: Listed in public marketplace
Collaborators
Add team members who can view or edit your dataset:- Go to dataset Settings
- Click Add Collaborator
- Search for user by username or email
- Set permission level
Best Practices
- Use descriptive names: “Training Images Batch 3” is better than “data”
- Add descriptions: Help teammates understand the dataset contents
- Tag consistently: Establish tagging conventions for your team
- Archive completed work: Move finished datasets to archive to keep workspace clean