Data Management

The Data Lifecycle

1. 1

   Ingest

   Upload files or connect Google Drive.

2. 2

   Inspect

   Review type, dimensions, schema, and warnings.

3. 3

   Organize

   Use folders and descriptive names.

4. 4

   Analyze

   Select exact data for Chat or a workspace tool.

5. 5

   Trace

   Keep versions, provenance, code, and generated files.

Upload Files

1. Open Database and select Upload, drag files into the upload area, or press Ctrl/Cmd+U.
2. Wait for storage and metadata processing to complete before starting an analysis.
3. Open each dataset and confirm the detected format, schema, preview, and any parsing warning.
4. Rename generic files and add project folders where that makes the study structure clearer.

Supported Format Families

Format recognition and interactive preview support are separate. A file may be available to analysis tools even when the browser shows metadata rather than a full visual preview.

File and Archive Limits

Archives receive additional safety checks: a compressed archive is limited to 500 MB and extracted content to 2 GB. Unsafe paths, excessive expansion, unsupported encryption, corrupt members, or too many entries can cause extraction to fail even when the original file is within your plan limit.

Folders and Naming

01_raw/
02_dictionary/
03_processed/
04_analysis/
05_figures/
06_manuscript/

Google Drive

Connect Google Drive from the Database integration controls, authorize the requested account, and select the folder associated with the project. CATO stores a project-to-folder relationship so supported files can be linked or synchronized without mixing unrelated Drive content into the project.

• Confirm that you authorized the intended institutional or personal Google account.
• Do not rename, move, or revoke access to the linked folder during an active sync.
• If a file changes externally, verify the current project copy before analysis.
• Disconnect the integration when the project no longer needs access.

Generated Files and Cross-Service Handoffs

Data created by a sandbox run—cleaned tables, model outputs, notebooks, figures, and reports—can be persisted as project artifacts. CATO records their project and conversation context so the frontend, backend, and analysis worker resolve the same authorized object.

• Open a generated artifact from the response card or Workspace.
• Use the selected version, not merely the latest filename, when reproducing a result.
• Review provenance, logs, and environment information before accepting the result.
• Promote a generated dataset to the next analysis only after validating its row count, columns, and transformations.

Artifact Versioning

Supported files, figures, notebooks, annotations, and manuscripts retain version history. A selected version can be represented in the workspace URL so collaborators can return to the intended state. Unsaved-edit guards prevent accidental version changes or tab closure while local edits are pending.

Preview and Quality Checks

Delete and Recovery

Deletion is an intentional project action and may affect conversations, workspace references, generated descendants, or reproducibility. Before deleting a source or project, export required materials and verify that downstream manuscripts and analyses no longer depend on it. Confirmation dialogs reduce accidents but are not a backup system.

Security and Privacy

• Upload only data your account and institution are authorized to process.
• Minimize direct identifiers and secrets before upload.
• Analysis runs execute in isolated sandboxes with project and object authorization boundaries.
• Data is transported over TLS and protected at rest by the production storage services.
• Consult the current Privacy Policy and institutional agreement for processing and retention terms.