Data Management

Overview

The gobrave data model is project-centric and designed for reproducible workflow input assembly.

Core Data Objects

  • Dataset: logical collection of biological assets
  • Sample: biological sample metadata record
  • File: physical or logical file metadata
  • Relations:
  • project <-> dataset
  • dataset <-> sample
  • dataset <-> file
  • sample <-> file

API Surface

Data endpoints are exposed under /api/v1 and grouped as:

  • /data/dataset/*
  • /data/project-dataset/*
  • /data/file/*
  • /data/sample/*
  • /data/dataset-file/*
  • /data/sample-file/*
  • /data/dataset-sample/*

Typical Data Workflow

  1. Create project.
  2. Create datasets.
  3. Register samples and files.
  4. Bind sample/file roles (for example FASTQ_R1, FASTQ_R2).
  5. Link assets into dataset relationships.
  6. Build workflow input selectors from these relations.

Workflow Integration

The workflow engine can resolve structured inputs from managed data:

  • sample collections
  • role-grouped files per sample
  • standalone reference files

This allows workflows to be template-driven while preserving project-level traceability.

Data Integrity Notes

  • IDs are generated by the backend and used for relationship joins.
  • Delete/update operations are validated through repository and service layers.
  • Use paginated list APIs for large projects.

Best Practices

  1. Define a consistent file role taxonomy before bulk import.
  2. Keep dataset boundaries meaningful to one analysis objective.
  3. Use project-scoped list APIs to avoid cross-project leakage.
  4. Keep workflow input contracts aligned with data role conventions.